From 7ee84bedf99e9e96a2192574defb3962945e42a2 Mon Sep 17 00:00:00 2001 From: Dreycey Albin Date: Sun, 4 Aug 2024 15:06:16 -0700 Subject: [PATCH 01/17] fixing multi line docker examples. --- README.md | 18 +++--------------- 1 file changed, 3 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 714d497..559cbe3 100644 --- a/README.md +++ b/README.md @@ -70,10 +70,7 @@ There are three fundamental pipelines in the PhageScanner tool. Each of these pi ``` - Example using Docker (multiclass pvps) ``` - docker run --rm \ - -v "$(pwd)/configs:/app/configs" \ - -v "$(pwd)/multiclass_database:/app/multiclass_database" \ - dreyceyalbin/phagescanner database -c /app/configs/multiclass_config.yaml -o /app/multiclass_database/ -v info + docker run --rm -v "$(pwd)/configs:/app/configs" -v "$(pwd)/multiclass_database:/app/multiclass_database" dreyceyalbin/phagescanner database -c /app/configs/multiclass_config.yaml -o /app/multiclass_database/ -v info ``` 2. Training and Test ML models - Basic usage @@ -86,11 +83,7 @@ There are three fundamental pipelines in the PhageScanner tool. Each of these pi ``` - Example using Docker (multiclass pvps) ``` - docker run --rm \ - -v "$(pwd)/configs:/app/configs" \ - -v "$(pwd)/multiclass_database:/app/multiclass_database" \ - -v "$(pwd)/training_output:/app/training_output" \ - dreyceyalbin/phagescanner train -c /app/configs/multiclass_config.yaml -o /app/training_output --database_csv_path /app/multiclass_database/ -v debug + docker run --rm -v "$(pwd)/configs:/app/configs" -v "$(pwd)/multiclass_database:/app/multiclass_database" -v "$(pwd)/training_output:/app/training_output" dreyceyalbin/phagescanner train -c /app/configs/multiclass_config.yaml -o /app/training_output --database_csv_path /app/multiclass_database/ -v debug ``` 3. Run on metagenomic data, genomes or proteins - Basic usage @@ -105,12 +98,7 @@ There are three fundamental pipelines in the PhageScanner tool. Each of these pi ``` - Example using Docker (genomes) ``` - docker run --rm \ - -v "$(pwd)/configs:/app/configs" \ - -v "$(pwd)/examples:/app/examples" \ - -v "$(pwd)/prediction_output:/app/prediction_output" \ - -v "$(pwd)/training_output:/app/training_output" \ - dreyceyalbin/phagescanner predict -t "genome" -c /app/configs/multiclass_config.yaml -o /app/prediction_output -n "OUTPREFIX" -tdir .\training_output\ -i /app/examples/GCF_000912975.1_ViralProj227117_genomic.fna -v debug + docker run --rm -v "$(pwd)/configs:/app/configs" -v "$(pwd)/examples:/app/examples" -v "$(pwd)/prediction_output:/app/prediction_output" -v "$(pwd)/training_output:/app/training_output" dreyceyalbin/phagescanner predict -t "genome" -c /app/configs/multiclass_config.yaml -o /app/prediction_output -n "OUTPREFIX" -tdir .\training_output\ -i /app/examples/GCF_000912975.1_ViralProj227117_genomic.fna -v debug ``` # PhageScanner GUI From 9aa7046456d58f738e0b6eadc3457d40e6c404a2 Mon Sep 17 00:00:00 2001 From: Dreycey Albin Date: Sun, 4 Aug 2024 15:20:09 -0700 Subject: [PATCH 02/17] minor README updates --- README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 559cbe3..a660870 100644 --- a/README.md +++ b/README.md @@ -89,8 +89,10 @@ There are three fundamental pipelines in the PhageScanner tool. Each of these pi - Basic usage ``` python phagescanner.py predict [-h] -i INPUT -t TYPE ("reads", "genome", or "protein") -c CONFIG -o training_output -n NAME -tdir TRAINING_OUTPUT - [--megahit_path MEGAHIT_PATH (Default: 'megahit')] [--phanotate_path PHANOTATE_PATH (Default: 'phanotate.py')] - [--probability_threshold PROBABILITY_THRESHOLD] [-v VERBOSITY] + [--megahit_path MEGAHIT_PATH (Default: 'megahit')] + [--phanotate_path PHANOTATE_PATH (Default: 'phanotate.py')] + [--probability_threshold PROBABILITY_THRESHOLD] + [-v VERBOSITY] ``` - Example (genomes; though sequencing reads and proteins can be used as input) ``` From 7f3125e892f5abb23bb6fabca1d4fd14ca0c5faf Mon Sep 17 00:00:00 2001 From: Dreycey Albin Date: Sun, 4 Aug 2024 16:09:51 -0700 Subject: [PATCH 03/17] update config yaml files --- configs/binary_pvps_config.yaml | 88 ++++++++++++++--------------- configs/lysin_detection_config.yaml | 14 ++--- configs/multiclass_config.yaml | 46 +++++++-------- configs/phagetoxins_config.yaml | 8 +-- 4 files changed, 78 insertions(+), 78 deletions(-) diff --git a/configs/binary_pvps_config.yaml b/configs/binary_pvps_config.yaml index 191a9b4..4a259f8 100644 --- a/configs/binary_pvps_config.yaml +++ b/configs/binary_pvps_config.yaml @@ -10,29 +10,29 @@ classes: uniprot: "capsid NOT cc_subcellular_location: virion AND reviewed: true" entrez: "bacteriophage[Organism] NOT (bacteriophage[Organism] AND ((shaft[Title] OR sheath[Title]) AND tail[Title]) OR head-tail[Title] OR tail fiber[Title] OR portal[Title] OR minor tail[Title] OR major tail[Title] OR baseplate[Title] OR minor capsid[Title] OR major capsid[Title]))" models: - # - name: "BLAST" - # model_info: - # model_name: "BLAST" - # sequential: false - # features: - # - name: "PROTEINSEQ" # only option for blast - # - name: "iVIREONS (FFNN)" - # model_info: - # model_name: "FFNN" - # sequential: false - # features: - # - name: "AAC" - # - name: "ISO" - # - name: "Feng et al. (Naive Bayes)" - # model_info: - # model_name: "MULTINAIVEBAYES" - # sequential: false - # features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - # - name: "AAC" - # - name: "DPC" - # parameters: - # gap_size: 0 - - name: "PVP-SVM (SVM)" + - name: "PhageScanner (BLAST) (binary-PVP)" + model_info: + model_name: "BLAST" + sequential: false + features: + - name: "PROTEINSEQ" # only option for blast + - name: "iVIREONS (FFNN) (binary-PVP)" + model_info: + model_name: "FFNN" + sequential: false + features: + - name: "AAC" + - name: "ISO" + - name: "Feng_NaiveBayes (binary-PVP)" + model_info: + model_name: "MULTINAIVEBAYES" + sequential: false + features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" + - name: "AAC" + - name: "DPC" + parameters: + gap_size: 0 + - name: "PVP-SVM (SVM) (binary-PVP)" model_info: model_name: "SVM" sequential: false @@ -44,26 +44,26 @@ models: - name: "ATC" - name: "CTD" - name: "PCP" - # - name: "SCORPION-partial (RF)" - # model_info: - # model_name: "RANDOMFOREST" - # sequential: false - # features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - # - name: "AAC" - # - name: "CTD" - # - name: "DPC" - # parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC - # gap_size: 0 - # - name: "Baseline Model - Logistic Regression" - # model_info: - # model_name: "LOGREG" - # sequential: false - # features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - # - name: "AAC" - # - name: "ATC" - # - name: "CTD" - # - name: "PCP" - - name: "PhageScanner (RNN)" + - name: "SCORPION-partial (RF) (binary-PVP)" + model_info: + model_name: "RANDOMFOREST" + sequential: false + features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" + - name: "AAC" + - name: "CTD" + - name: "DPC" + parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC + gap_size: 0 + - name: "PhageScanner (Logistic Regression) (binary-PVP)" + model_info: + model_name: "LOGREG" + sequential: false + features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" + - name: "AAC" + - name: "ATC" + - name: "CTD" + - name: "PCP" + - name: "PhageScanner (RNN) (binary-PVP)" model_info: model_name: "RNN" sequential: 3 @@ -71,7 +71,7 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 0 - - name: "DeePVP (CNN)" + - name: "DeePVP (CNN) (binary-PVP)" model_info: model_name: "CNN" sequential: false diff --git a/configs/lysin_detection_config.yaml b/configs/lysin_detection_config.yaml index 6484f62..ada6995 100644 --- a/configs/lysin_detection_config.yaml +++ b/configs/lysin_detection_config.yaml @@ -10,12 +10,12 @@ classes: uniprot: "cc_subcellular_location: virion NOT endolysin" entrez: "bacteriophage[Organism]" models: - # - name: "BLAST" - # model_info: - # model_name: "BLAST" - # sequential: false - # features: - # - name: "PROTEINSEQ" # only option for blast + - name: "PhageScanner (BLAST) (binary-Lysins)" + model_info: + model_name: "BLAST" + sequential: false + features: + - name: "PROTEINSEQ" # only option for blast - name: "Baseline Model - Logistic Regression" model_info: model_name: "LOGREG" @@ -25,7 +25,7 @@ models: - name: "ATC" - name: "CTD" - name: "PCP" - - name: "PhageScanner (RNN)" + - name: "PhageScanner (RNN) (binary-Lysins)" model_info: model_name: "RNN" sequential: 3 diff --git a/configs/multiclass_config.yaml b/configs/multiclass_config.yaml index 1c67a9e..a6e37bb 100644 --- a/configs/multiclass_config.yaml +++ b/configs/multiclass_config.yaml @@ -5,46 +5,46 @@ clustering: classes: - name: MajorCapsid uniprot: "major capsid AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "major capsid[Title]" + # entrez: "bacteriophage[Organism] AND major capsid[Title]" - name: MinorCapsid uniprot: "minor capsid AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "minor capsid[Title]" + # entrez: "bacteriophage[Organism] AND minor capsid[Title]" - name: Baseplate uniprot: "baseplate AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "baseplate[Title]" + # entrez: "bacteriophage[Organism] AND baseplate[Title]" - name: MajorTail uniprot: "major tail AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "major tail[Title]" + # entrez: "bacteriophage[Organism] AND major tail[Title]" - name: MinorTail uniprot: "minor tail AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "minor tail[Title]" + # entrez: "bacteriophage[Organism] AND minor tail[Title]" - name: Portal uniprot: "portal AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "portal[Title]" + # entrez: "bacteriophage[Organism] AND portal[Title]" - name: TailFiber uniprot: "tail fiber AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "tail fiber[Title]" + # entrez: "bacteriophage[Organism] AND tail fiber[Title]" - name: Collar uniprot: "collar AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "collar[Title]" - - name: shaft + # entrez: "bacteriophage[Organism] AND collar[Title]" + - name: Shaft uniprot: "(shaft OR sheath) AND tail AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "(shaft[Title] OR sheath[Title]) AND tail[Title]" + # entrez: "bacteriophage[Organism] AND (shaft[Title] OR sheath[Title]) AND tail[Title]" - name: HTJ uniprot: "head-tail AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "head-tail[Title]" + # entrez: "bacteriophage[Organism] AND head-tail[Title]" - name: non-PVP uniprot: "capsid NOT cc_subcellular_location: virion AND reviewed: true" # entrez: "bacteriophage[Organism] NOT ((shaft[Title] OR sheath[Title]) AND tail[Title]) OR head-tail[Title] OR tail fiber[Title] OR portal[Title] OR minor tail[Title] OR major tail[Title] OR baseplate[Title] OR minor capsid[Title] OR major capsid[Title])" models: - # - name: "BLAST" - # model_info: - # model_name: "BLAST" - # sequential: false - # feature_selection: false # Options: - # features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - # - name: "PROTEINSEQ" - - name: "PhANNs (FFNN)" + - name: "PhageScanner_BLAST (multiclass-PVP)" + model_info: + model_name: "BLAST" + sequential: false + feature_selection: false # Options: + features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" + - name: "PROTEINSEQ" + - name: "PhANNs_FFNN (multiclass-PVP)" model_info: model_name: "FFNN" sequential: false @@ -55,13 +55,13 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 2 - - name: "DeePVP (CNN)" + - name: "DeePVP_CNN (multiclass-PVP)" model_info: model_name: "CNN" sequential: false features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - name: "SEQUENTIALONEHOT" - - name: "Baseline Model - Logistic Regression" + - name: "PhageScanner_LogisticRegression (multiclass-PVP)" model_info: model_name: "LOGREG" sequential: false @@ -69,7 +69,7 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 0 - - name: "PhageScanner (HashSeq-FFNN)" + - name: "PhageScanner_HashSeq-FFNN (multiclass-PVP)" model_info: model_name: "FFNN" sequential: false @@ -78,7 +78,7 @@ models: parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC vec_size: 1000 kmer_size: 10 - - name: "PhageScanner (RNN)" + - name: "PhageScanner_RNN (multiclass-PVP)" model_info: model_name: "RNN" sequential: 2 diff --git a/configs/phagetoxins_config.yaml b/configs/phagetoxins_config.yaml index d2eeaa9..6bd9605 100644 --- a/configs/phagetoxins_config.yaml +++ b/configs/phagetoxins_config.yaml @@ -8,7 +8,7 @@ classes: - name: Non-Toxin uniprot: 'bacteriophage NOT (go:0090729) AND reviewed: true' models: - - name: "BLAST" + - name: "PhageScanner (BLAST) (binary-Toxins)" model_info: model_name: "BLAST" sequential: false @@ -26,7 +26,7 @@ models: - name: "ATC" - name: "CTD" - name: "PCP" - - name: "ToxinFinder (FFNN)" + - name: "PhageScanner (FFNN) (binary-Toxins)" model_info: model_name: "FFNN" sequential: false @@ -37,7 +37,7 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 2 - - name: "ToxinFinder (RNN)" + - name: "PhageScanner (RNN) (binary-Toxins)" model_info: model_name: "RNN" sequential: 3 @@ -45,7 +45,7 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 0 - - name: "Baseline Model - Logistic Regression" + - name: "PhageScanner (Logistic Regression) (binary-Toxins)" model_info: model_name: "LOGREG" sequential: false From e0f08bf884243235d8f871de3e41de7141d1cd5e Mon Sep 17 00:00:00 2001 From: dreycey albin Date: Mon, 5 Aug 2024 04:10:45 +0000 Subject: [PATCH 04/17] updating requirements file to include xml lib --- configs/multiclass_config.yaml | 22 +++++++++++----------- requirements.txt | 1 + 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/configs/multiclass_config.yaml b/configs/multiclass_config.yaml index a6e37bb..a204b63 100644 --- a/configs/multiclass_config.yaml +++ b/configs/multiclass_config.yaml @@ -5,37 +5,37 @@ clustering: classes: - name: MajorCapsid uniprot: "major capsid AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] AND major capsid[Title]" + entrez: "major capsid[Title]" - name: MinorCapsid uniprot: "minor capsid AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] AND minor capsid[Title]" + entrez: "minor capsid[Title]" - name: Baseplate uniprot: "baseplate AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] AND baseplate[Title]" + entrez: "baseplate[Title]" - name: MajorTail uniprot: "major tail AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] AND major tail[Title]" + entrez: "major tail[Title]" - name: MinorTail uniprot: "minor tail AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] AND minor tail[Title]" + entrez: "minor tail[Title]" - name: Portal uniprot: "portal AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] AND portal[Title]" + entrez: "portal[Title]" - name: TailFiber uniprot: "tail fiber AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] AND tail fiber[Title]" + entrez: "tail fiber[Title]" - name: Collar uniprot: "collar AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] AND collar[Title]" + entrez: "collar[Title]" - name: Shaft uniprot: "(shaft OR sheath) AND tail AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] AND (shaft[Title] OR sheath[Title]) AND tail[Title]" + entrez: "(shaft[Title] OR sheath[Title]) AND tail[Title]" - name: HTJ uniprot: "head-tail AND cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] AND head-tail[Title]" + entrez: "head-tail[Title]" - name: non-PVP uniprot: "capsid NOT cc_subcellular_location: virion AND reviewed: true" - # entrez: "bacteriophage[Organism] NOT ((shaft[Title] OR sheath[Title]) AND tail[Title]) OR head-tail[Title] OR tail fiber[Title] OR portal[Title] OR minor tail[Title] OR major tail[Title] OR baseplate[Title] OR minor capsid[Title] OR major capsid[Title])" + entrez: "bacteriophage[Organism] NOT ((shaft[Title] OR sheath[Title]) AND tail[Title]) OR head-tail[Title] OR tail fiber[Title] OR portal[Title] OR minor tail[Title] OR major tail[Title] OR baseplate[Title] OR minor capsid[Title] OR major capsid[Title])" models: - name: "PhageScanner_BLAST (multiclass-PVP)" model_info: diff --git a/requirements.txt b/requirements.txt index 9aeaa5c..4292ec2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,6 +6,7 @@ numpy==1.26.4 pandas==2.2.2 pyyaml==6.0.1 requests==2.32.3 +lxml==5.2.2 # used for testing black==23.1.0 From b073e097c3798809af8d9299532ad8674d953918 Mon Sep 17 00:00:00 2001 From: dreycey albin Date: Tue, 6 Aug 2024 05:38:57 +0000 Subject: [PATCH 05/17] updating config files for consistency --- configs/binary_pvps_config.yaml | 16 ++++++++-------- configs/feature_testing_config.yaml | 2 +- configs/lysin_detection_config.yaml | 2 +- configs/multiclass_config.yaml | 14 +++++++------- configs/phagetoxins_config.yaml | 12 ++++++------ 5 files changed, 23 insertions(+), 23 deletions(-) diff --git a/configs/binary_pvps_config.yaml b/configs/binary_pvps_config.yaml index 4a259f8..61d03c2 100644 --- a/configs/binary_pvps_config.yaml +++ b/configs/binary_pvps_config.yaml @@ -10,20 +10,20 @@ classes: uniprot: "capsid NOT cc_subcellular_location: virion AND reviewed: true" entrez: "bacteriophage[Organism] NOT (bacteriophage[Organism] AND ((shaft[Title] OR sheath[Title]) AND tail[Title]) OR head-tail[Title] OR tail fiber[Title] OR portal[Title] OR minor tail[Title] OR major tail[Title] OR baseplate[Title] OR minor capsid[Title] OR major capsid[Title]))" models: - - name: "PhageScanner (BLAST) (binary-PVP)" + - name: "PhageScanner_BLAST_binary_PVP" model_info: model_name: "BLAST" sequential: false features: - name: "PROTEINSEQ" # only option for blast - - name: "iVIREONS (FFNN) (binary-PVP)" + - name: "iVIREONS_FFNN_binary_PVP" model_info: model_name: "FFNN" sequential: false features: - name: "AAC" - name: "ISO" - - name: "Feng_NaiveBayes (binary-PVP)" + - name: "Feng_NaiveBayes_binary_PVP" model_info: model_name: "MULTINAIVEBAYES" sequential: false @@ -32,7 +32,7 @@ models: - name: "DPC" parameters: gap_size: 0 - - name: "PVP-SVM (SVM) (binary-PVP)" + - name: "PVP-SVM_SVM_binary_PVP" model_info: model_name: "SVM" sequential: false @@ -44,7 +44,7 @@ models: - name: "ATC" - name: "CTD" - name: "PCP" - - name: "SCORPION-partial (RF) (binary-PVP)" + - name: "SCORPION-partial_RF_binary_PVP" model_info: model_name: "RANDOMFOREST" sequential: false @@ -54,7 +54,7 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 0 - - name: "PhageScanner (Logistic Regression) (binary-PVP)" + - name: "PhageScanner_LogisticRegression_binary_PVP" model_info: model_name: "LOGREG" sequential: false @@ -63,7 +63,7 @@ models: - name: "ATC" - name: "CTD" - name: "PCP" - - name: "PhageScanner (RNN) (binary-PVP)" + - name: "PhageScanner_RNN_binary_PVP" model_info: model_name: "RNN" sequential: 3 @@ -71,7 +71,7 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 0 - - name: "DeePVP (CNN) (binary-PVP)" + - name: "DeePVP_CNN_binary_PVP" model_info: model_name: "CNN" sequential: false diff --git a/configs/feature_testing_config.yaml b/configs/feature_testing_config.yaml index 79c0d16..486040c 100644 --- a/configs/feature_testing_config.yaml +++ b/configs/feature_testing_config.yaml @@ -1,7 +1,7 @@ clustering: + deduplication-threshold: 100 clustering-percentage: 90 k_partitions: 5 # number of partitions in k-fold cross validation - deduplication-threshold: 100 classes: - name: MajorCapsid uniprot: "major capsid AND cc_subcellular_location: virion AND reviewed: true" diff --git a/configs/lysin_detection_config.yaml b/configs/lysin_detection_config.yaml index ada6995..ade1211 100644 --- a/configs/lysin_detection_config.yaml +++ b/configs/lysin_detection_config.yaml @@ -1,5 +1,5 @@ clustering: - deduplication-threshold: 99 + deduplication-threshold: 100 clustering-percentage: 90 k_partitions: 5 # number of partitions in k-fold cross validation classes: diff --git a/configs/multiclass_config.yaml b/configs/multiclass_config.yaml index a204b63..4613fa5 100644 --- a/configs/multiclass_config.yaml +++ b/configs/multiclass_config.yaml @@ -1,7 +1,7 @@ clustering: + deduplication-threshold: 100 clustering-percentage: 90 k_partitions: 5 # number of partitions in k-fold cross validation - deduplication-threshold: 100 classes: - name: MajorCapsid uniprot: "major capsid AND cc_subcellular_location: virion AND reviewed: true" @@ -37,14 +37,14 @@ classes: uniprot: "capsid NOT cc_subcellular_location: virion AND reviewed: true" entrez: "bacteriophage[Organism] NOT ((shaft[Title] OR sheath[Title]) AND tail[Title]) OR head-tail[Title] OR tail fiber[Title] OR portal[Title] OR minor tail[Title] OR major tail[Title] OR baseplate[Title] OR minor capsid[Title] OR major capsid[Title])" models: - - name: "PhageScanner_BLAST (multiclass-PVP)" + - name: "PhageScanner_BLAST_multiclass_PVP" model_info: model_name: "BLAST" sequential: false feature_selection: false # Options: features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - name: "PROTEINSEQ" - - name: "PhANNs_FFNN (multiclass-PVP)" + - name: "PhANNs_FFNN_multiclass_PVP" model_info: model_name: "FFNN" sequential: false @@ -55,13 +55,13 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 2 - - name: "DeePVP_CNN (multiclass-PVP)" + - name: "DeePVP_CNN_multiclass_PVP" model_info: model_name: "CNN" sequential: false features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - name: "SEQUENTIALONEHOT" - - name: "PhageScanner_LogisticRegression (multiclass-PVP)" + - name: "PhageScanner_LogisticRegression_multiclass_PVP" model_info: model_name: "LOGREG" sequential: false @@ -69,7 +69,7 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 0 - - name: "PhageScanner_HashSeq-FFNN (multiclass-PVP)" + - name: "PhageScanner_FFNN_Hashseq_multiclass_PVP" model_info: model_name: "FFNN" sequential: false @@ -78,7 +78,7 @@ models: parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC vec_size: 1000 kmer_size: 10 - - name: "PhageScanner_RNN (multiclass-PVP)" + - name: "PhageScanner_RNN_multiclass_PVP" model_info: model_name: "RNN" sequential: 2 diff --git a/configs/phagetoxins_config.yaml b/configs/phagetoxins_config.yaml index 6bd9605..1fe6dbe 100644 --- a/configs/phagetoxins_config.yaml +++ b/configs/phagetoxins_config.yaml @@ -1,6 +1,6 @@ clustering: deduplication-threshold: 100 - clustering-percentage: 95 + clustering-percentage: 90 k_partitions: 5 # number of partitions in k-fold cross validation classes: - name: KnownToxin @@ -8,13 +8,13 @@ classes: - name: Non-Toxin uniprot: 'bacteriophage NOT (go:0090729) AND reviewed: true' models: - - name: "PhageScanner (BLAST) (binary-Toxins)" + - name: "PhageScanner_BLAST_binary_toxins" model_info: model_name: "BLAST" sequential: false features: - name: "PROTEINSEQ" # only option for blast - - name: "PVP-SVM (SVM)" + - name: "PVP-SVM_SVM_binary_toxins" model_info: model_name: "SVM" sequential: false @@ -26,7 +26,7 @@ models: - name: "ATC" - name: "CTD" - name: "PCP" - - name: "PhageScanner (FFNN) (binary-Toxins)" + - name: "PhageScanner_FFNN_binary_toxins" model_info: model_name: "FFNN" sequential: false @@ -37,7 +37,7 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 2 - - name: "PhageScanner (RNN) (binary-Toxins)" + - name: "PhageScanner_RNN_binary_toxins" model_info: model_name: "RNN" sequential: 3 @@ -45,7 +45,7 @@ models: - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 0 - - name: "PhageScanner (Logistic Regression) (binary-Toxins)" + - name: "PhageScanner_LogisticRegression_binary_toxins" model_info: model_name: "LOGREG" sequential: false From 6bdb9198ac2418c9c9f5a7ef221982f1abeebbc5 Mon Sep 17 00:00:00 2001 From: dreycey albin Date: Tue, 6 Aug 2024 05:39:38 +0000 Subject: [PATCH 06/17] remove unused config --- .../combining_different_models_config.yaml | 57 ------------------- 1 file changed, 57 deletions(-) delete mode 100644 configs/combining_different_models_config.yaml diff --git a/configs/combining_different_models_config.yaml b/configs/combining_different_models_config.yaml deleted file mode 100644 index db61796..0000000 --- a/configs/combining_different_models_config.yaml +++ /dev/null @@ -1,57 +0,0 @@ -models: - # - name: "ToxinFinder-BLAST" - # model_path: "pretrained_models/ToxinFinder-BLAST" # local path. - # index2class_file: "pretrained_models/toxin_index2class_name.csv" # local path. - # model_info: - # model_name: "BLAST" - # sequential: false - # feature_selection: false # Options: - # features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - # - name: "PROTEINSEQ" - # - name: "ToxinFinder (FFNN)" - # model_path: "pretrained_models/ToxinFinder (FFNN)" # local path. - # index2class_file: "pretrained_models/toxin_index2class_name.csv" # local path. - # model_info: - # model_name: "FFNN" - # sequential: false - # features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - # - name: "DPC" - # parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC - # gap_size: 0 - # - name: "DPC" - # parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC - # gap_size: 2 - - name: "PVP-SVM (SVM)" - model_path: "pretrained_models/PVP-SVM (SVM)" # local path. - index2class_file: "pretrained_models/binary_index2class_name.csv" # local path. - model_info: - model_name: "SVM" - sequential: false - features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - - name: "DPC" - parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC - gap_size: 0 - - name: "AAC" - - name: "ATC" - - name: "CTD" - - name: "PCP" - # - name: "BLAST" - # model_path: "pretrained_models/BLAST" # local path. - # index2class_file: "pretrained_models/multiclass_index2class_name.csv" # local path. - # model_info: - # model_name: "BLAST" - # sequential: false - # feature_selection: false # Options: - # features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - # - name: "PROTEINSEQ" - - name: "PhageScanner (RNN)" - model_path: "pretrained_models/PhageScanner (RNN)" # local path. - index2class_file: "pretrained_models/multiclass_index2class_name.csv" # local path. - model_info: - model_name: "RNN" - sequential: 2 - features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - - name: "DPC" - parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC - gap_size: 0 - - name: "TPC" \ No newline at end of file From 9f5e6b38552e9245b9652e66cdebd2944a157fd5 Mon Sep 17 00:00:00 2001 From: dreycey albin Date: Tue, 6 Aug 2024 05:40:14 +0000 Subject: [PATCH 07/17] minor changes/updates to the README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a660870..a13cf77 100644 --- a/README.md +++ b/README.md @@ -96,7 +96,7 @@ There are three fundamental pipelines in the PhageScanner tool. Each of these pi ``` - Example (genomes; though sequencing reads and proteins can be used as input) ``` - python phagescanner.py predict -t "genomes" -c configs/multiclass_config.yaml -n "OUTPREFIX" -tdir .\training_output\ -o prediction_output -i examples/GCF_000912975.1_ViralProj227117_genomic.fna -v debug + python phagescanner.py predict -t "genome" -c configs/multiclass_config.yaml -n "OUTPREFIX" -tdir .\training_output\ -o prediction_output -i examples/GCF_000912975.1_ViralProj227117_genomic.fna -v debug ``` - Example using Docker (genomes) ``` From b3b580d00001de252ebbd58164293353cc55748c Mon Sep 17 00:00:00 2001 From: dreycey albin Date: Tue, 6 Aug 2024 05:43:58 +0000 Subject: [PATCH 08/17] updates to the prediction pipeline --- PhageScanner/main/models.py | 49 ++++++++++++++----- .../main/pipelines/prediction_pipeline.py | 2 +- .../main/tool_wrappers/blast_wrapper.py | 2 +- 3 files changed, 38 insertions(+), 15 deletions(-) diff --git a/PhageScanner/main/models.py b/PhageScanner/main/models.py index a6e7505..a1b629f 100644 --- a/PhageScanner/main/models.py +++ b/PhageScanner/main/models.py @@ -309,8 +309,9 @@ def build_model(self, feature_vector_length, number_of_classes): model.add(Dropout(0.2)) model.add(Dense(200, activation="relu")) model.add(Dropout(0.2)) - model.add(Dense(200, activation="relu")) + model.add(Dense(2000, activation="relu")) model.add(Dropout(0.2)) + model.add(Dense(200, activation="relu")) # Add an output layer model.add(Dense(number_of_classes, activation="softmax")) @@ -331,11 +332,19 @@ def train(self, train_x, train_y): number_of_classes=max(train_y) + 1, ) - # set up early stopping criterion. If training doesn't - # improve after 2 batches, finish. + # set up early stopping criterion. early_stopping = EarlyStopping( - monitor="loss", mode="min", min_delta=0.01, patience=10 + monitor="loss", + min_delta=0.01, + patience=2, + verbose=1, + mode="auto", + baseline=None, + restore_best_weights=True, + start_from_epoch=0, ) + + # train the model self.model.fit( train_x, train_y, @@ -393,11 +402,18 @@ def train(self, train_x, train_y): number_of_classes=max(train_y) + 1, ) - # set up early stopping criterion. If training doesn't - # improve after 2 batches, finish. + # set up early stopping criterion. early_stopping = EarlyStopping( - monitor="loss", mode="min", min_delta=0.01, patience=2 + monitor="loss", + min_delta=0.01, + patience=2, + verbose=1, + mode="auto", + baseline=None, + restore_best_weights=True, + start_from_epoch=0, ) + self.model.fit( train_x, train_y, @@ -461,11 +477,18 @@ def train(self, train_x, train_y): number_of_classes=max(train_y) + 1, ) - # set up early stopping criterion. If training - # doesn't improve after 2 batches, finish. + # set up early stopping criterion. early_stopping = EarlyStopping( - monitor="loss", mode="min", min_delta=0.01, patience=2 + monitor="loss", + min_delta=0.01, + patience=2, + verbose=1, + mode="auto", + baseline=None, + restore_best_weights=True, + start_from_epoch=0, ) + self.model.fit( train_x, train_y, @@ -479,10 +502,10 @@ def train(self, train_x, train_y): class BlastClassifier(BLASTWrapper, Model): """Creates a classifier around BLAST.""" - def __init__(self, database_path=None): + def __init__(self, database_path=None, makeblastdb_exe_path="makeblastdb", blastp_exe_path="blastp"): """Construct the BLAST classifier.""" - self.makedbcmd = "makeblastdb" - self.querycmd = "blastp" + self.makeblastdb_exe_path = makeblastdb_exe_path + self.blastp_exe_path = blastp_exe_path self.dbpath = database_path self.temp_directory = None diff --git a/PhageScanner/main/pipelines/prediction_pipeline.py b/PhageScanner/main/pipelines/prediction_pipeline.py index 1d3ec3f..d9505cb 100644 --- a/PhageScanner/main/pipelines/prediction_pipeline.py +++ b/PhageScanner/main/pipelines/prediction_pipeline.py @@ -148,7 +148,7 @@ def get_proteins(self): self.orffinder.get_info_from_name(orf_name) ) - if stop_pos is None: + if stop_pos == -1: stop_pos = start_pos + len(orf_sequence) # save to output file. diff --git a/PhageScanner/main/tool_wrappers/blast_wrapper.py b/PhageScanner/main/tool_wrappers/blast_wrapper.py index b0b8ccf..0b085fa 100644 --- a/PhageScanner/main/tool_wrappers/blast_wrapper.py +++ b/PhageScanner/main/tool_wrappers/blast_wrapper.py @@ -67,7 +67,7 @@ def query(self, fasta_file: Path, outputfile: Path, threads: int = 1): command += f"-db {self.dbpath} " command += f"-out {outputfile} " command += f"-num_threads {threads} " - command += "-max_target_seqs 1 " + command += "-max_target_seqs 5 " command += '-outfmt "6 qseqid sseqid score"' # run the command. From 36353e353e8bc1fcec1b453c983e80e3865ae3ba Mon Sep 17 00:00:00 2001 From: Dreycey Date: Thu, 8 Aug 2024 20:18:48 -0700 Subject: [PATCH 09/17] adding binary confusion matrix results --- results/PhageScanner (RNN)_confusion_matrix_binary.csv | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 results/PhageScanner (RNN)_confusion_matrix_binary.csv diff --git a/results/PhageScanner (RNN)_confusion_matrix_binary.csv b/results/PhageScanner (RNN)_confusion_matrix_binary.csv new file mode 100644 index 0000000..f5a3bb0 --- /dev/null +++ b/results/PhageScanner (RNN)_confusion_matrix_binary.csv @@ -0,0 +1,2 @@ +691.000,209.000 +36.000,518.000 From 9a1e10c8eb6dda3e754f40058962c5a45d5c0cd4 Mon Sep 17 00:00:00 2001 From: Dreycey Date: Thu, 15 Aug 2024 08:56:58 -0700 Subject: [PATCH 10/17] updating different aspect of the pipelines --- PhageScanner/main/database_adapters.py | 4 +- PhageScanner/main/feature_extractors.py | 29 ++- PhageScanner/main/models.py | 202 ++++++++++++++++-- .../main/pipelines/database_pipeline.py | 106 ++++++--- .../main/pipelines/pipeline_interface.py | 3 + .../main/pipelines/prediction_pipeline.py | 2 +- .../main/pipelines/training_pipeline.py | 24 ++- requirements.txt | 1 + 8 files changed, 309 insertions(+), 62 deletions(-) diff --git a/PhageScanner/main/database_adapters.py b/PhageScanner/main/database_adapters.py index d8437ea..4cf5316 100644 --- a/PhageScanner/main/database_adapters.py +++ b/PhageScanner/main/database_adapters.py @@ -13,7 +13,7 @@ import re from abc import ABC, abstractmethod from enum import Enum -from typing import List +from typing import Generator, Any import requests from bs4 import BeautifulSoup @@ -223,7 +223,7 @@ def get_phanns_query(query, extra=""): modified_query += extra return modified_query - def esearch(self, query, batch_size=10000) -> List[str]: + def esearch(self, query, batch_size=10000) -> Generator[Any, Any, Any]: """Return a list of URIs. Description: diff --git a/PhageScanner/main/feature_extractors.py b/PhageScanner/main/feature_extractors.py index e9b5255..22e814a 100644 --- a/PhageScanner/main/feature_extractors.py +++ b/PhageScanner/main/feature_extractors.py @@ -19,6 +19,7 @@ import numpy as np from Bio.SeqUtils.ProtParam import ProteinAnalysis +from keras.preprocessing.sequence import pad_sequences from PhageScanner.main.exceptions import ( IncorrectValueError, @@ -51,6 +52,7 @@ class FeatureExtractorNames(Enum): onehot = "SEQUENTIALONEHOT" pcp = "PCP" chemfeatures = "CHEMFEATURES" + integerencoding = "INTEGERENCODING" @classmethod def get_extractor(cls, name, parameters: Optional[Dict]): @@ -68,6 +70,7 @@ def get_extractor(cls, name, parameters: Optional[Dict]): cls.onehot.value: SequentialOneHot, cls.pcp.value: PCPExtractor, cls.chemfeatures.value: ChemFeatureExtractor, + cls.integerencoding.value: IntegerEncoding } # instantiate the class @@ -495,7 +498,7 @@ class SequentialOneHot(ProteinFeatureExtraction): def __init__(self, parameters: Optional[Dict] = None): """Instantiate tokenization extract method.""" self.aa2index = {aa: ind for ind, aa in enumerate(self.canonical_amino_acids)} - self.matrix_length = 1000 + self.matrix_length = 2000 def extract_features(self, protein: str): """Obtain an tokenization of the protein sequence.""" @@ -563,6 +566,30 @@ def extract_features(self, protein: str): return hash_vec +class IntegerEncoding(ProteinFeatureExtraction): + """Extraction method for obtaining an integer encoding for a protein.""" + + def __init__(self, parameters: Optional[Dict] = None): + """Instantiate CTD extract method.""" + codes = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', + 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y'] + + aa_dict = {} + for index, val in enumerate(codes): + aa_dict[val] = index+1 + + self.aa_dict = aa_dict + + def extract_features(self, protein: str, max_length=1000): + """ Integer encoding for a protein. """ + integer_encoding = [] + for amino_acid in protein: + integer_encoding.append(self.aa_dict.get(amino_acid, 0)) + + return pad_sequences([integer_encoding], + maxlen=max_length, + padding='post', + truncating='post')[0] class CTDExtractor(ProteinFeatureExtraction): """Extraction method for obtaining Composition-transition-distribution (CTD)""" diff --git a/PhageScanner/main/models.py b/PhageScanner/main/models.py index a1b629f..7d79967 100644 --- a/PhageScanner/main/models.py +++ b/PhageScanner/main/models.py @@ -30,10 +30,14 @@ Dropout, Flatten, MaxPooling1D, + Embedding, + Bidirectional, + Input, + GlobalMaxPooling1D ) from keras.models import Sequential, load_model from keras.optimizers import Adam -from keras.regularizers import l1 +from keras.regularizers import l1, l2 from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier from sklearn.linear_model import LogisticRegression from sklearn.metrics import ( @@ -67,6 +71,7 @@ class ModelNames(Enum): svm = "SVM" ffnn = "FFNN" + phagescanner_ffn = "PhagescannerFFNN" multinaivebayes = "MULTINAIVEBAYES" gradboost = "GRADBOOST" randomforest = "RANDOMFOREST" @@ -74,6 +79,7 @@ class ModelNames(Enum): logreg = "LOGREG" cnn = "CNN" rnn = "RNN" + rnn2 = "RNN2" @classmethod def get_model(cls, name): @@ -81,6 +87,7 @@ def get_model(cls, name): name2adapter = { cls.svm.value: SVCMultiClassModel(), cls.ffnn.value: FFNNMultiClassModel(), + cls.phagescanner_ffn.value: PhageScannerFFNNMultiClassModel(), cls.multinaivebayes.value: MultiNaiveBayesClassModel(), cls.gradboost.value: GradientBoostingClassModel(), cls.randomforest.value: RandomForestClassModel(), @@ -88,6 +95,7 @@ def get_model(cls, name): cls.logreg.value: LogRegClassModel(), cls.cnn.value: CNNMultiClassifier(), cls.rnn.value: RNNMultiClassifier(), + cls.rnn2.value: RNNMultiClassifier2(), } adapter = name2adapter.get(name) @@ -109,12 +117,12 @@ class Model(ABC): """ @abstractmethod - def train(self, x, y): + def train(self, train_x, train_y, test_x, test_y): """Train a binary classification model.""" pass @abstractmethod - def predict(self, x, y): + def predict(self, test_x, test_y): """Use the model to predict a binary class.""" pass @@ -192,9 +200,11 @@ def load(cls, file_path: Path): model_obj.model = joblib.load(str(file_path) + cls.file_extension) return model_obj - def train(self, train_x, train_y): + def train(self, train_x, train_y, test_x, test_y): """Train the model.""" self.model.fit(train_x, train_y) + avg_score = np.average(self.model.score(test_x, test_y)) + print(f"model validation accuracy: {avg_score}") def predict(self, test_x): """Predict a classes for an array of input proteins.""" @@ -268,7 +278,11 @@ class GradientBoostingClassModel(ScikitModel): def __init__(self): """Instantiate a new GradientBoostingClassModel.""" self.model = GradientBoostingClassifier( - n_estimators=10, learning_rate=1.0, max_depth=1, random_state=0 + n_estimators=10, + learning_rate=1.0, + max_depth=1, + random_state=0, + verbose=True ) @@ -283,6 +297,85 @@ def __init__(self): class FFNNMultiClassModel(KerasModel): """Class defining the PhANNs FFNN.""" + def __init__(self): + """Instantiate the FFNN model.""" + self.model = None + + def build_model(self, feature_vector_length, number_of_classes): + """Build the model from the feature vector length.""" + logging.info( + f"Building FFNN model. Feature vector length: {feature_vector_length}" + ) + logging.info(f"Building FFNN model. Number of classes: {number_of_classes}") + + # initialize the constructor + model = Sequential() + model.add( + Dense( + feature_vector_length, + activation="relu", + input_dim=feature_vector_length, + kernel_initializer="random_uniform", + ) + ) + + # hidden layers + model.add(Dropout(0.2)) + model.add(Dense(200, activation="relu")) + model.add(Dropout(0.2)) + model.add(Dense(200, activation="relu")) + model.add(Dropout(0.2)) + + # Add an output layer + model.add(Dense(number_of_classes, activation="softmax")) + + # compile the model + opt=Adam(learning_rate=0.001, + beta_1=0.9, + beta_2=0.999, + weight_decay=0.0, + amsgrad=False + ) + model.compile( + loss="sparse_categorical_crossentropy", + optimizer=opt, + metrics=["accuracy"] + ) + + return model + + def train(self, train_x, train_y, test_x, test_y): + """Train an FFNN on multiclass data""" + if self.model is None: + self.model = self.build_model( + feature_vector_length=len(train_x[0]), + number_of_classes=max(train_y) + 1, + ) + + # set up early stopping criterion. + early_stopping = EarlyStopping( + monitor="loss", + mode='min', + verbose=2, + patience=5, + min_delta=0.02 + ) + + # train the model + self.model.fit( + train_x, + train_y, + validation_data=(test_x, test_y), + epochs=200, + callbacks=[early_stopping], + batch_size=5000, + verbose=2, + ) + + +class PhageScannerFFNNMultiClassModel(KerasModel): + """Class defining the optimized FFNN.""" + def __init__(self): """Instantiate the FFNN model.""" self.model = None @@ -311,6 +404,8 @@ def build_model(self, feature_vector_length, number_of_classes): model.add(Dropout(0.2)) model.add(Dense(2000, activation="relu")) model.add(Dropout(0.2)) + model.add(Dense(2000, activation="relu")) + model.add(Dropout(0.2)) model.add(Dense(200, activation="relu")) # Add an output layer @@ -324,7 +419,7 @@ def build_model(self, feature_vector_length, number_of_classes): return model - def train(self, train_x, train_y): + def train(self, train_x, train_y, test_x, test_y): """Train an FFNN on multiclass data""" if self.model is None: self.model = self.build_model( @@ -334,7 +429,7 @@ def train(self, train_x, train_y): # set up early stopping criterion. early_stopping = EarlyStopping( - monitor="loss", + monitor="val_loss", min_delta=0.01, patience=2, verbose=1, @@ -349,11 +444,77 @@ def train(self, train_x, train_y): train_x, train_y, epochs=300, + validation_data=(test_x, test_y), callbacks=[early_stopping], batch_size=32, verbose=1, ) +class RNNMultiClassifier2(KerasModel): + """RNN MultiClassifier built""" + + def __init__(self): + """Initialize the RNN MultiClassifier.""" + self.model = None + + def build_model(self, row_length, number_of_classes, max_length=1000): + """Build the RNN Model. + + Description: + Sets the layers and parameters for the RNN. The last functionality + of this method is comiling the model. + """ + # model + model = Sequential() + model.add(Input(shape=(max_length,))) + model.add(Embedding(21, 128, input_length=max_length)) + model.add( + Bidirectional(LSTM(64, + kernel_regularizer=l2(0.01), + recurrent_regularizer=l2(0.01), + bias_regularizer=l2(0.01)) + )) + model.add(Dropout(0.3)) + model.add(Dense(number_of_classes, activation="softmax")) + + # Compile the model + model.compile( + loss="sparse_categorical_crossentropy", + optimizer="adam", + metrics=["accuracy"], + ) + + return model + + def train(self, train_x, train_y, test_x, test_y): + """Train an RNN on multiclass data""" + if self.model is None: + self.model = self.build_model( + row_length=len(train_x[0]), + number_of_classes=max(train_y) + 1, + ) + + # set up early stopping criterion. + early_stopping = EarlyStopping( + monitor="val_loss", + min_delta=0.01, + patience=2, + verbose=1, + mode="auto", + baseline=None, + restore_best_weights=True, + start_from_epoch=0, + ) + + self.model.fit( + train_x, + train_y, + validation_data=(test_x, test_y), + epochs=300, + callbacks=[early_stopping], + batch_size=32, + verbose=1, + ) class RNNMultiClassifier(KerasModel): """RNN MultiClassifier built""" @@ -393,7 +554,7 @@ def build_model(self, row_length, column_length, number_of_classes): return model - def train(self, train_x, train_y): + def train(self, train_x, train_y, test_x, test_y): """Train an RNN on multiclass data""" if self.model is None: self.model = self.build_model( @@ -417,6 +578,7 @@ def train(self, train_x, train_y): self.model.fit( train_x, train_y, + validation_data=(test_x, test_y), epochs=300, callbacks=[early_stopping], batch_size=32, @@ -443,23 +605,26 @@ def build_model(self, row_length, column_length, number_of_classes): # Convolutional layer model.add( Conv1D( - filters=32, - kernel_size=3, + filters=700, + kernel_size=9, activation="relu", input_shape=(row_length, column_length), ) ) # Max pooling layer - model.add(MaxPooling1D(pool_size=4)) + model.add(GlobalMaxPooling1D()) # Batch normalization layer model.add(BatchNormalization()) + model.add(Dropout(0.55)) # Flatten the output from the previous layer model.add(Flatten()) # Fully connected layer - model.add(Dense(64, activation="relu", kernel_regularizer=l1(0.01))) - # Compile the model - # if number_of_classes > 2: + model.add(Dense(600, activation="relu", kernel_regularizer=l1(0.01))) + model.add(Dropout(0.55)) + # Final output layer model.add(Dense(number_of_classes, activation="softmax")) + + # Compile the model model.compile( loss="sparse_categorical_crossentropy", optimizer="adam", @@ -468,7 +633,7 @@ def build_model(self, row_length, column_length, number_of_classes): return model - def train(self, train_x, train_y): + def train(self, train_x, train_y, test_x, test_y): """Train an CNN on multiclass data""" if self.model is None: self.model = self.build_model( @@ -492,9 +657,10 @@ def train(self, train_x, train_y): self.model.fit( train_x, train_y, - epochs=300, + validation_data=(test_x, test_y), + epochs=80, callbacks=[early_stopping], - batch_size=32, + batch_size=256, verbose=1, ) @@ -550,7 +716,7 @@ def load(cls, file_path: Path): model_obj = cls(database_path=Path(file_path) / "BLAST_DB") return model_obj - def train(self, train_x: List[str], train_y: List[int]): + def train(self, train_x: List[str], train_y: List[int], test_x, test_y): """Create a blast database using model template. Description: diff --git a/PhageScanner/main/pipelines/database_pipeline.py b/PhageScanner/main/pipelines/database_pipeline.py index 35cdc93..f886673 100644 --- a/PhageScanner/main/pipelines/database_pipeline.py +++ b/PhageScanner/main/pipelines/database_pipeline.py @@ -49,14 +49,11 @@ def get_fasta_path(self, class_name, identity=None): have been clustered at that particular identity threshold. """ - identity_str = "" - if identity: - identity = int(100 * identity) - identity_str = f"_{identity}" + identity_str = f"_{int(100 * identity)}" if identity else "" path = self.directory / (class_name + f"{identity_str}" + ".fasta") return path - def get_proteins_from_db_adapters(self): + def get_proteins_from_db_adapters(self, db_count_filename="db_count.csv"): """Get proteins from each database adapter and saves to local file. Description: @@ -119,7 +116,7 @@ def get_proteins_from_db_adapters(self): ) # save count to csv. - db_count_csv = self.directory / "db_count.csv" + db_count_csv = self.directory / db_count_filename temp_db_count = { "datetime": self.pipeline_start_time, "database": database_name, @@ -133,7 +130,7 @@ def get_proteins_from_db_adapters(self): ) def cluster_proteins( - self, clustering_identity_threshold, input_identity_threshold=None + self, clustering_identity_threshold, input_identity_threshold=None, cluster_count_filename="result_cluster_ouput.csv" ): """Cluster each class of proteins. @@ -158,6 +155,12 @@ def cluster_proteins( class_name, identity=clustering_identity_threshold ) + if os.path.isfile(output_file_path): + logging.warning( + f"(Skip) Clustered class already obtained: {class_name} | {output_file_path}" + ) + continue + # cluster proteins. self.clustering_tool_adapter.cluster( fasta_file=input_file_path, @@ -166,7 +169,7 @@ def cluster_proteins( ) # save count to csv. - db_count_csv = self.directory / "result_cluster_ouput.csv" + cluster_count_csv = self.directory / cluster_count_filename temp_db_count = { "datetime": self.pipeline_start_time, "class_name": class_name, @@ -178,11 +181,62 @@ def cluster_proteins( CSVUtils.appendcsv( data_dict=[temp_db_count], # input must be an array. fieldnames=temp_db_count.keys(), - file_path=db_count_csv, + file_path=cluster_count_csv, ) + def get_min_partition_size(self, clustering_identity_threshold): + """ Get the minimum class size per partition. """ + min_cluster_size_to_use = float("inf") + for class_info in self.config_object.get_classes(): + class_name = class_info.get("name") # TODO: move to config_object + + # get path to proteins after clustering. + fasta_clustered = self.get_fasta_path( + class_name, identity=clustering_identity_threshold + ) + + # get clusters as Dict + cluster_graph = self.clustering_tool_adapter.get_clusters(fasta_clustered) + + # get cluster sizes + cluster_sizes = [] + for cluster, cluster_proteins in cluster_graph.items(): + cluster_size = len(cluster_proteins) + cluster_sizes.append(cluster_size) + max_cluster_size = max(cluster_sizes) + cluster_size_90P = np.percentile(cluster_sizes, 90) + min_cluster_size = min(cluster_sizes) + cluster_count = len(cluster_sizes) + + # save memory + del cluster_sizes + del cluster_graph + + # get minimal cluster sizes + if cluster_size_90P < min_cluster_size_to_use: + min_cluster_size_to_use = int(cluster_size_90P) + + # save information about the clusters + cluster_count_csv = self.directory / "cluster_sizes.csv" + temp_cluster_count = { + "datetime": self.pipeline_start_time, + "class_name": class_name, + "cluster_count": cluster_count, + "cluster_sizes_min": min_cluster_size, + "cluster_sizes_90P": cluster_size_90P, + "cluster_sizes_max": max_cluster_size + } + CSVUtils.appendcsv( + data_dict=[temp_cluster_count], + fieldnames=temp_cluster_count.keys(), + file_path=cluster_count_csv, + ) + + return min_cluster_size_to_use + + def partition_proteins( - self, clustering_identity_threshold, k_partitions=5, get_cluster_sizes=False + self, clustering_identity_threshold, max_cluster_size, k_partitions=5 ): """Partion the proteins. @@ -203,7 +257,7 @@ def partition_proteins( 4. sequence """ for class_info in self.config_object.get_classes(): - class_name = class_info.get("name") + class_name = class_info.get("name") # TODO: move to config_object logging.info(f"\t Partitioning class {k_partitions}-fold: {class_name}") # get path to proteins before and after clustering. @@ -215,36 +269,19 @@ def partition_proteins( ) # get clusters as Dict - # TODO: should done without storing all clusters into memory. + # TODO: should be done without storing all clusters into memory. cluster_graph = self.clustering_tool_adapter.get_clusters(fasta_clustered) # randomize the clusters randomized_clusters = list(cluster_graph.keys()) np.random.shuffle(randomized_clusters) - # save cluster sizes to csv - if get_cluster_sizes: - cluster_count_csv = self.directory / "cluster_sizes.csv" - temp_cluster_count = { - "datetime": self.pipeline_start_time, - "class_name": class_name, - "cluster_count": len(cluster_graph.keys()), - "cluster_sizes": "\t".join( - [str(len(cluster)) for cluster in cluster_graph.values()] - ), - } - CSVUtils.appendcsv( - data_dict=[temp_cluster_count], - fieldnames=temp_cluster_count.keys(), - file_path=cluster_count_csv, - ) - # obtain a dictionary of protein -> partition protein2partition = {} for i, cluster_id in enumerate(randomized_clusters): cluster_partition = (i % k_partitions) + 1 # assign clusters to the same partition - for protein_accesion in cluster_graph[cluster_id]: + for protein_accesion in cluster_graph[cluster_id][:max_cluster_size]: protein2partition[protein_accesion] = cluster_partition # delete graph to save some space @@ -265,10 +302,6 @@ def partition_proteins( output_csv.write( f"{partition},{accession},{protein},{len(protein)}\n" ) - else: - logging.warning( - f"protein {accession} was not found in clusters" - ) def run(self): """Run the pipeline. @@ -301,8 +334,13 @@ def run(self): # Step 4: create k-fold partitioned clusters. logging.info("Step 4 - Create k-fold partitions...") + min_cluster_size = self.get_min_partition_size( + clustering_identity_threshold=self.config_object.get_clustering_threshold() + ) + min_cluster_size = 100 self.partition_proteins( clustering_identity_threshold=self.config_object.get_clustering_threshold(), + max_cluster_size=min_cluster_size, k_partitions=self.config_object.get_k_partition_count(), ) logging.info("Step 4 (Finished) - Create k-fold partitions...") diff --git a/PhageScanner/main/pipelines/pipeline_interface.py b/PhageScanner/main/pipelines/pipeline_interface.py index 3b77132..4f367c8 100644 --- a/PhageScanner/main/pipelines/pipeline_interface.py +++ b/PhageScanner/main/pipelines/pipeline_interface.py @@ -4,6 +4,8 @@ import time from abc import ABC, abstractmethod +import swifter + from PhageScanner.main.feature_extractors import ( FeatureExtractorNames, ProteinFeatureAggregator, @@ -54,4 +56,5 @@ def extract_feature_vector(self, model_name): aggregator.extract_features ) + logging.info(f"done extracting features for model: '{model_name}'") diff --git a/PhageScanner/main/pipelines/prediction_pipeline.py b/PhageScanner/main/pipelines/prediction_pipeline.py index d9505cb..12c4a9b 100644 --- a/PhageScanner/main/pipelines/prediction_pipeline.py +++ b/PhageScanner/main/pipelines/prediction_pipeline.py @@ -39,7 +39,7 @@ def get_type(name: str): if name == pipeline_type.value: logging.info(f"prediction type: {pipeline_type}") return pipeline_type - raise ValueError("Unknown prediction pipeline type") + raise ValueError(f"Unknown prediction pipeline type: {name}. Must be: {",".join([predtype.value for predtype in PredictionPipelineType])}") class PredictionPipeline(Pipeline): diff --git a/PhageScanner/main/pipelines/training_pipeline.py b/PhageScanner/main/pipelines/training_pipeline.py index 8d7a960..85b278e 100644 --- a/PhageScanner/main/pipelines/training_pipeline.py +++ b/PhageScanner/main/pipelines/training_pipeline.py @@ -4,9 +4,11 @@ import os from pathlib import Path from typing import Dict, Union +import gc import numpy as np import pandas as pd +import swifter from PhageScanner.main import utils from PhageScanner.main.exceptions import IncorrectValueError @@ -64,6 +66,7 @@ def combine_partitions(self): for class_name in model_classes: # Read the csv file into a pandas DataFrame class_name = class_name.get("name") + logging.debug(f"Combining class {class_name} into the dataframe.") class_path = self.config_object.get_csv_path_from_name( class_name, self.db_directory ) @@ -117,7 +120,7 @@ def balance_partitions(self): # create a balanced partition. balanced_partition_df_list = [] for class_index in partition_df["class"].unique(): - # get proteins corresponding to the class index. + # get proteins corresponding to the class index. class_df: pd.DataFrame = partition_df[ partition_df["class"] == class_index ] @@ -287,11 +290,10 @@ def run(self): self.extract_feature_vector(model_name) logging.info(f"Step 4.{iteration} (Finished) - Feature Extraction...") - # TODO: feature selection - # train model logging.info(f"Step 4.{iteration} - k-fold testing model: {model_name}...") kfold_iteration = 0 + bestmodel_f1score = 0 for x_train, y_train, x_test, y_test in self.get_kfold_training(): # obtain model. model_predictor_name = self.config_object.get_predictor_model_name( @@ -301,7 +303,7 @@ def run(self): # train model. logging.info(f"Training Model: {model_name}...") - model_object.train(x_train, y_train) + model_object.train(x_train, y_train, x_test, y_test) logging.info(f"(Finished) Training Model: {model_name}...") # test trained model. @@ -317,8 +319,11 @@ def run(self): ) # save the model. - path2savemodel = self.directory / f"{model_name}" - model_object.save(path2savemodel) + model_f1score = np.average([float(i) for i in model_results["f1score"].split("\t")]) + if model_f1score > bestmodel_f1score: + bestmodel_f1score = model_f1score + path2savemodel = self.directory / f"{model_name}" + model_object.save(path2savemodel) # make sure saved model works. new_model = model_object.load(path2savemodel) @@ -326,6 +331,13 @@ def run(self): # increment the kfold iteration kfold_iteration += 1 + + # free up memory + del x_train, y_train, x_test, y_test + gc.collect() + + if kfold_iteration >= 3: + break logging.info( f"Step 4.{iteration} (Finished) - k-fold testing model: {model_name}..." diff --git a/requirements.txt b/requirements.txt index 4292ec2..c0d3352 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,6 +7,7 @@ pandas==2.2.2 pyyaml==6.0.1 requests==2.32.3 lxml==5.2.2 +swifter==1.4.0 # used for testing black==23.1.0 From 0fca7a742d2aed8d37b291b62e0789fe3827593b Mon Sep 17 00:00:00 2001 From: Dreycey Date: Thu, 15 Aug 2024 09:03:33 -0700 Subject: [PATCH 11/17] moving source code directory from to --- phagescanner.py | 4 ++-- phagescanner_gui.py | 4 ++-- {PhageScanner => src}/__init__.py | 0 {PhageScanner => src}/gui/feature_plotter.py | 0 {PhageScanner => src}/gui/gui_frames.py | 0 {PhageScanner => src}/main/DNA.py | 0 {PhageScanner => src}/main/__init__.py | 0 {PhageScanner => src}/main/database_adapters.py | 0 {PhageScanner => src}/main/exceptions.py | 0 {PhageScanner => src}/main/feature_extractors.py | 0 {PhageScanner => src}/main/models.py | 0 {PhageScanner => src}/main/pipelines/__init__.py | 0 {PhageScanner => src}/main/pipelines/database_pipeline.py | 0 {PhageScanner => src}/main/pipelines/pipeline_interface.py | 0 {PhageScanner => src}/main/pipelines/prediction_pipeline.py | 0 {PhageScanner => src}/main/pipelines/training_pipeline.py | 0 .../main/tool_wrappers/assembler_wrappers.py | 0 {PhageScanner => src}/main/tool_wrappers/blast_wrapper.py | 0 .../main/tool_wrappers/clustering_wrappers.py | 0 .../main/tool_wrappers/orffinder_wrappers.py | 0 {PhageScanner => src}/main/utils.py | 0 {PhageScanner => src}/tests/__init__.py | 0 {PhageScanner => src}/tests/test_DNA.py | 0 {PhageScanner => src}/tests/test_clustering_wrappers.py | 0 {PhageScanner => src}/tests/test_database_adapters.py | 0 {PhageScanner => src}/tests/test_feature_extractors.py | 0 {PhageScanner => src}/tests/test_utils.py | 0 {PhageScanner => src}/third_party/CTD.py | 0 {PhageScanner => src}/third_party/PseudoAAC.py | 0 {PhageScanner => src}/third_party/__init__.py | 0 30 files changed, 4 insertions(+), 4 deletions(-) rename {PhageScanner => src}/__init__.py (100%) rename {PhageScanner => src}/gui/feature_plotter.py (100%) rename {PhageScanner => src}/gui/gui_frames.py (100%) rename {PhageScanner => src}/main/DNA.py (100%) rename {PhageScanner => src}/main/__init__.py (100%) rename {PhageScanner => src}/main/database_adapters.py (100%) rename {PhageScanner => src}/main/exceptions.py (100%) rename {PhageScanner => src}/main/feature_extractors.py (100%) rename {PhageScanner => src}/main/models.py (100%) rename {PhageScanner => src}/main/pipelines/__init__.py (100%) rename {PhageScanner => src}/main/pipelines/database_pipeline.py (100%) rename {PhageScanner => src}/main/pipelines/pipeline_interface.py (100%) rename {PhageScanner => src}/main/pipelines/prediction_pipeline.py (100%) rename {PhageScanner => src}/main/pipelines/training_pipeline.py (100%) rename {PhageScanner => src}/main/tool_wrappers/assembler_wrappers.py (100%) rename {PhageScanner => src}/main/tool_wrappers/blast_wrapper.py (100%) rename {PhageScanner => src}/main/tool_wrappers/clustering_wrappers.py (100%) rename {PhageScanner => src}/main/tool_wrappers/orffinder_wrappers.py (100%) rename {PhageScanner => src}/main/utils.py (100%) rename {PhageScanner => src}/tests/__init__.py (100%) rename {PhageScanner => src}/tests/test_DNA.py (100%) rename {PhageScanner => src}/tests/test_clustering_wrappers.py (100%) rename {PhageScanner => src}/tests/test_database_adapters.py (100%) rename {PhageScanner => src}/tests/test_feature_extractors.py (100%) rename {PhageScanner => src}/tests/test_utils.py (100%) rename {PhageScanner => src}/third_party/CTD.py (100%) rename {PhageScanner => src}/third_party/PseudoAAC.py (100%) rename {PhageScanner => src}/third_party/__init__.py (100%) diff --git a/phagescanner.py b/phagescanner.py index ec0a16c..5e909ed 100644 --- a/phagescanner.py +++ b/phagescanner.py @@ -13,8 +13,8 @@ from pathlib import Path import logging -import PhageScanner.main.utils as utils -from PhageScanner.main.pipelines import ( +import src.main.utils as utils +from src.main.pipelines import ( database_pipeline, prediction_pipeline, training_pipeline, diff --git a/phagescanner_gui.py b/phagescanner_gui.py index 5ad8aea..8885847 100644 --- a/phagescanner_gui.py +++ b/phagescanner_gui.py @@ -10,9 +10,9 @@ from enum import Enum from pathlib import Path import sys -from PhageScanner.gui import feature_plotter +from src.gui import feature_plotter import tkinter as tk -from PhageScanner.gui.gui_frames import Application +from src.gui.gui_frames import Application diff --git a/PhageScanner/__init__.py b/src/__init__.py similarity index 100% rename from PhageScanner/__init__.py rename to src/__init__.py diff --git a/PhageScanner/gui/feature_plotter.py b/src/gui/feature_plotter.py similarity index 100% rename from PhageScanner/gui/feature_plotter.py rename to src/gui/feature_plotter.py diff --git a/PhageScanner/gui/gui_frames.py b/src/gui/gui_frames.py similarity index 100% rename from PhageScanner/gui/gui_frames.py rename to src/gui/gui_frames.py diff --git a/PhageScanner/main/DNA.py b/src/main/DNA.py similarity index 100% rename from PhageScanner/main/DNA.py rename to src/main/DNA.py diff --git a/PhageScanner/main/__init__.py b/src/main/__init__.py similarity index 100% rename from PhageScanner/main/__init__.py rename to src/main/__init__.py diff --git a/PhageScanner/main/database_adapters.py b/src/main/database_adapters.py similarity index 100% rename from PhageScanner/main/database_adapters.py rename to src/main/database_adapters.py diff --git a/PhageScanner/main/exceptions.py b/src/main/exceptions.py similarity index 100% rename from PhageScanner/main/exceptions.py rename to src/main/exceptions.py diff --git a/PhageScanner/main/feature_extractors.py b/src/main/feature_extractors.py similarity index 100% rename from PhageScanner/main/feature_extractors.py rename to src/main/feature_extractors.py diff --git a/PhageScanner/main/models.py b/src/main/models.py similarity index 100% rename from PhageScanner/main/models.py rename to src/main/models.py diff --git a/PhageScanner/main/pipelines/__init__.py b/src/main/pipelines/__init__.py similarity index 100% rename from PhageScanner/main/pipelines/__init__.py rename to src/main/pipelines/__init__.py diff --git a/PhageScanner/main/pipelines/database_pipeline.py b/src/main/pipelines/database_pipeline.py similarity index 100% rename from PhageScanner/main/pipelines/database_pipeline.py rename to src/main/pipelines/database_pipeline.py diff --git a/PhageScanner/main/pipelines/pipeline_interface.py b/src/main/pipelines/pipeline_interface.py similarity index 100% rename from PhageScanner/main/pipelines/pipeline_interface.py rename to src/main/pipelines/pipeline_interface.py diff --git a/PhageScanner/main/pipelines/prediction_pipeline.py b/src/main/pipelines/prediction_pipeline.py similarity index 100% rename from PhageScanner/main/pipelines/prediction_pipeline.py rename to src/main/pipelines/prediction_pipeline.py diff --git a/PhageScanner/main/pipelines/training_pipeline.py b/src/main/pipelines/training_pipeline.py similarity index 100% rename from PhageScanner/main/pipelines/training_pipeline.py rename to src/main/pipelines/training_pipeline.py diff --git a/PhageScanner/main/tool_wrappers/assembler_wrappers.py b/src/main/tool_wrappers/assembler_wrappers.py similarity index 100% rename from PhageScanner/main/tool_wrappers/assembler_wrappers.py rename to src/main/tool_wrappers/assembler_wrappers.py diff --git a/PhageScanner/main/tool_wrappers/blast_wrapper.py b/src/main/tool_wrappers/blast_wrapper.py similarity index 100% rename from PhageScanner/main/tool_wrappers/blast_wrapper.py rename to src/main/tool_wrappers/blast_wrapper.py diff --git a/PhageScanner/main/tool_wrappers/clustering_wrappers.py b/src/main/tool_wrappers/clustering_wrappers.py similarity index 100% rename from PhageScanner/main/tool_wrappers/clustering_wrappers.py rename to src/main/tool_wrappers/clustering_wrappers.py diff --git a/PhageScanner/main/tool_wrappers/orffinder_wrappers.py b/src/main/tool_wrappers/orffinder_wrappers.py similarity index 100% rename from PhageScanner/main/tool_wrappers/orffinder_wrappers.py rename to src/main/tool_wrappers/orffinder_wrappers.py diff --git a/PhageScanner/main/utils.py b/src/main/utils.py similarity index 100% rename from PhageScanner/main/utils.py rename to src/main/utils.py diff --git a/PhageScanner/tests/__init__.py b/src/tests/__init__.py similarity index 100% rename from PhageScanner/tests/__init__.py rename to src/tests/__init__.py diff --git a/PhageScanner/tests/test_DNA.py b/src/tests/test_DNA.py similarity index 100% rename from PhageScanner/tests/test_DNA.py rename to src/tests/test_DNA.py diff --git a/PhageScanner/tests/test_clustering_wrappers.py b/src/tests/test_clustering_wrappers.py similarity index 100% rename from PhageScanner/tests/test_clustering_wrappers.py rename to src/tests/test_clustering_wrappers.py diff --git a/PhageScanner/tests/test_database_adapters.py b/src/tests/test_database_adapters.py similarity index 100% rename from PhageScanner/tests/test_database_adapters.py rename to src/tests/test_database_adapters.py diff --git a/PhageScanner/tests/test_feature_extractors.py b/src/tests/test_feature_extractors.py similarity index 100% rename from PhageScanner/tests/test_feature_extractors.py rename to src/tests/test_feature_extractors.py diff --git a/PhageScanner/tests/test_utils.py b/src/tests/test_utils.py similarity index 100% rename from PhageScanner/tests/test_utils.py rename to src/tests/test_utils.py diff --git a/PhageScanner/third_party/CTD.py b/src/third_party/CTD.py similarity index 100% rename from PhageScanner/third_party/CTD.py rename to src/third_party/CTD.py diff --git a/PhageScanner/third_party/PseudoAAC.py b/src/third_party/PseudoAAC.py similarity index 100% rename from PhageScanner/third_party/PseudoAAC.py rename to src/third_party/PseudoAAC.py diff --git a/PhageScanner/third_party/__init__.py b/src/third_party/__init__.py similarity index 100% rename from PhageScanner/third_party/__init__.py rename to src/third_party/__init__.py From 940733048b4ddbc505b48ee22a0835c5b5e87f95 Mon Sep 17 00:00:00 2001 From: Dreycey Date: Thu, 15 Aug 2024 20:35:55 -0700 Subject: [PATCH 12/17] Revert "moving source code directory from to" This reverts commit 0fca7a742d2aed8d37b291b62e0789fe3827593b. --- {src => PhageScanner}/__init__.py | 0 {src => PhageScanner}/gui/feature_plotter.py | 0 {src => PhageScanner}/gui/gui_frames.py | 0 {src => PhageScanner}/main/DNA.py | 0 {src => PhageScanner}/main/__init__.py | 0 {src => PhageScanner}/main/database_adapters.py | 0 {src => PhageScanner}/main/exceptions.py | 0 {src => PhageScanner}/main/feature_extractors.py | 0 {src => PhageScanner}/main/models.py | 0 {src => PhageScanner}/main/pipelines/__init__.py | 0 {src => PhageScanner}/main/pipelines/database_pipeline.py | 0 {src => PhageScanner}/main/pipelines/pipeline_interface.py | 0 {src => PhageScanner}/main/pipelines/prediction_pipeline.py | 0 {src => PhageScanner}/main/pipelines/training_pipeline.py | 0 .../main/tool_wrappers/assembler_wrappers.py | 0 {src => PhageScanner}/main/tool_wrappers/blast_wrapper.py | 0 .../main/tool_wrappers/clustering_wrappers.py | 0 .../main/tool_wrappers/orffinder_wrappers.py | 0 {src => PhageScanner}/main/utils.py | 0 {src => PhageScanner}/tests/__init__.py | 0 {src => PhageScanner}/tests/test_DNA.py | 0 {src => PhageScanner}/tests/test_clustering_wrappers.py | 0 {src => PhageScanner}/tests/test_database_adapters.py | 0 {src => PhageScanner}/tests/test_feature_extractors.py | 0 {src => PhageScanner}/tests/test_utils.py | 0 {src => PhageScanner}/third_party/CTD.py | 0 {src => PhageScanner}/third_party/PseudoAAC.py | 0 {src => PhageScanner}/third_party/__init__.py | 0 phagescanner.py | 4 ++-- phagescanner_gui.py | 4 ++-- 30 files changed, 4 insertions(+), 4 deletions(-) rename {src => PhageScanner}/__init__.py (100%) rename {src => PhageScanner}/gui/feature_plotter.py (100%) rename {src => PhageScanner}/gui/gui_frames.py (100%) rename {src => PhageScanner}/main/DNA.py (100%) rename {src => PhageScanner}/main/__init__.py (100%) rename {src => PhageScanner}/main/database_adapters.py (100%) rename {src => PhageScanner}/main/exceptions.py (100%) rename {src => PhageScanner}/main/feature_extractors.py (100%) rename {src => PhageScanner}/main/models.py (100%) rename {src => PhageScanner}/main/pipelines/__init__.py (100%) rename {src => PhageScanner}/main/pipelines/database_pipeline.py (100%) rename {src => PhageScanner}/main/pipelines/pipeline_interface.py (100%) rename {src => PhageScanner}/main/pipelines/prediction_pipeline.py (100%) rename {src => PhageScanner}/main/pipelines/training_pipeline.py (100%) rename {src => PhageScanner}/main/tool_wrappers/assembler_wrappers.py (100%) rename {src => PhageScanner}/main/tool_wrappers/blast_wrapper.py (100%) rename {src => PhageScanner}/main/tool_wrappers/clustering_wrappers.py (100%) rename {src => PhageScanner}/main/tool_wrappers/orffinder_wrappers.py (100%) rename {src => PhageScanner}/main/utils.py (100%) rename {src => PhageScanner}/tests/__init__.py (100%) rename {src => PhageScanner}/tests/test_DNA.py (100%) rename {src => PhageScanner}/tests/test_clustering_wrappers.py (100%) rename {src => PhageScanner}/tests/test_database_adapters.py (100%) rename {src => PhageScanner}/tests/test_feature_extractors.py (100%) rename {src => PhageScanner}/tests/test_utils.py (100%) rename {src => PhageScanner}/third_party/CTD.py (100%) rename {src => PhageScanner}/third_party/PseudoAAC.py (100%) rename {src => PhageScanner}/third_party/__init__.py (100%) diff --git a/src/__init__.py b/PhageScanner/__init__.py similarity index 100% rename from src/__init__.py rename to PhageScanner/__init__.py diff --git a/src/gui/feature_plotter.py b/PhageScanner/gui/feature_plotter.py similarity index 100% rename from src/gui/feature_plotter.py rename to PhageScanner/gui/feature_plotter.py diff --git a/src/gui/gui_frames.py b/PhageScanner/gui/gui_frames.py similarity index 100% rename from src/gui/gui_frames.py rename to PhageScanner/gui/gui_frames.py diff --git a/src/main/DNA.py b/PhageScanner/main/DNA.py similarity index 100% rename from src/main/DNA.py rename to PhageScanner/main/DNA.py diff --git a/src/main/__init__.py b/PhageScanner/main/__init__.py similarity index 100% rename from src/main/__init__.py rename to PhageScanner/main/__init__.py diff --git a/src/main/database_adapters.py b/PhageScanner/main/database_adapters.py similarity index 100% rename from src/main/database_adapters.py rename to PhageScanner/main/database_adapters.py diff --git a/src/main/exceptions.py b/PhageScanner/main/exceptions.py similarity index 100% rename from src/main/exceptions.py rename to PhageScanner/main/exceptions.py diff --git a/src/main/feature_extractors.py b/PhageScanner/main/feature_extractors.py similarity index 100% rename from src/main/feature_extractors.py rename to PhageScanner/main/feature_extractors.py diff --git a/src/main/models.py b/PhageScanner/main/models.py similarity index 100% rename from src/main/models.py rename to PhageScanner/main/models.py diff --git a/src/main/pipelines/__init__.py b/PhageScanner/main/pipelines/__init__.py similarity index 100% rename from src/main/pipelines/__init__.py rename to PhageScanner/main/pipelines/__init__.py diff --git a/src/main/pipelines/database_pipeline.py b/PhageScanner/main/pipelines/database_pipeline.py similarity index 100% rename from src/main/pipelines/database_pipeline.py rename to PhageScanner/main/pipelines/database_pipeline.py diff --git a/src/main/pipelines/pipeline_interface.py b/PhageScanner/main/pipelines/pipeline_interface.py similarity index 100% rename from src/main/pipelines/pipeline_interface.py rename to PhageScanner/main/pipelines/pipeline_interface.py diff --git a/src/main/pipelines/prediction_pipeline.py b/PhageScanner/main/pipelines/prediction_pipeline.py similarity index 100% rename from src/main/pipelines/prediction_pipeline.py rename to PhageScanner/main/pipelines/prediction_pipeline.py diff --git a/src/main/pipelines/training_pipeline.py b/PhageScanner/main/pipelines/training_pipeline.py similarity index 100% rename from src/main/pipelines/training_pipeline.py rename to PhageScanner/main/pipelines/training_pipeline.py diff --git a/src/main/tool_wrappers/assembler_wrappers.py b/PhageScanner/main/tool_wrappers/assembler_wrappers.py similarity index 100% rename from src/main/tool_wrappers/assembler_wrappers.py rename to PhageScanner/main/tool_wrappers/assembler_wrappers.py diff --git a/src/main/tool_wrappers/blast_wrapper.py b/PhageScanner/main/tool_wrappers/blast_wrapper.py similarity index 100% rename from src/main/tool_wrappers/blast_wrapper.py rename to PhageScanner/main/tool_wrappers/blast_wrapper.py diff --git a/src/main/tool_wrappers/clustering_wrappers.py b/PhageScanner/main/tool_wrappers/clustering_wrappers.py similarity index 100% rename from src/main/tool_wrappers/clustering_wrappers.py rename to PhageScanner/main/tool_wrappers/clustering_wrappers.py diff --git a/src/main/tool_wrappers/orffinder_wrappers.py b/PhageScanner/main/tool_wrappers/orffinder_wrappers.py similarity index 100% rename from src/main/tool_wrappers/orffinder_wrappers.py rename to PhageScanner/main/tool_wrappers/orffinder_wrappers.py diff --git a/src/main/utils.py b/PhageScanner/main/utils.py similarity index 100% rename from src/main/utils.py rename to PhageScanner/main/utils.py diff --git a/src/tests/__init__.py b/PhageScanner/tests/__init__.py similarity index 100% rename from src/tests/__init__.py rename to PhageScanner/tests/__init__.py diff --git a/src/tests/test_DNA.py b/PhageScanner/tests/test_DNA.py similarity index 100% rename from src/tests/test_DNA.py rename to PhageScanner/tests/test_DNA.py diff --git a/src/tests/test_clustering_wrappers.py b/PhageScanner/tests/test_clustering_wrappers.py similarity index 100% rename from src/tests/test_clustering_wrappers.py rename to PhageScanner/tests/test_clustering_wrappers.py diff --git a/src/tests/test_database_adapters.py b/PhageScanner/tests/test_database_adapters.py similarity index 100% rename from src/tests/test_database_adapters.py rename to PhageScanner/tests/test_database_adapters.py diff --git a/src/tests/test_feature_extractors.py b/PhageScanner/tests/test_feature_extractors.py similarity index 100% rename from src/tests/test_feature_extractors.py rename to PhageScanner/tests/test_feature_extractors.py diff --git a/src/tests/test_utils.py b/PhageScanner/tests/test_utils.py similarity index 100% rename from src/tests/test_utils.py rename to PhageScanner/tests/test_utils.py diff --git a/src/third_party/CTD.py b/PhageScanner/third_party/CTD.py similarity index 100% rename from src/third_party/CTD.py rename to PhageScanner/third_party/CTD.py diff --git a/src/third_party/PseudoAAC.py b/PhageScanner/third_party/PseudoAAC.py similarity index 100% rename from src/third_party/PseudoAAC.py rename to PhageScanner/third_party/PseudoAAC.py diff --git a/src/third_party/__init__.py b/PhageScanner/third_party/__init__.py similarity index 100% rename from src/third_party/__init__.py rename to PhageScanner/third_party/__init__.py diff --git a/phagescanner.py b/phagescanner.py index 5e909ed..ec0a16c 100644 --- a/phagescanner.py +++ b/phagescanner.py @@ -13,8 +13,8 @@ from pathlib import Path import logging -import src.main.utils as utils -from src.main.pipelines import ( +import PhageScanner.main.utils as utils +from PhageScanner.main.pipelines import ( database_pipeline, prediction_pipeline, training_pipeline, diff --git a/phagescanner_gui.py b/phagescanner_gui.py index 8885847..5ad8aea 100644 --- a/phagescanner_gui.py +++ b/phagescanner_gui.py @@ -10,9 +10,9 @@ from enum import Enum from pathlib import Path import sys -from src.gui import feature_plotter +from PhageScanner.gui import feature_plotter import tkinter as tk -from src.gui.gui_frames import Application +from PhageScanner.gui.gui_frames import Application From b2dfa2aed348614be19839f2df446e448c1dc978 Mon Sep 17 00:00:00 2001 From: Dreycey Date: Sat, 17 Aug 2024 10:50:42 -0700 Subject: [PATCH 13/17] adding toxin GO term analysis --- misc/plot_results.ipynb | 300 ++++++------ results/uniprot_toxin_info.tsv | 837 +++++++++++++++++++++++++++++++++ 2 files changed, 1003 insertions(+), 134 deletions(-) create mode 100644 results/uniprot_toxin_info.tsv diff --git a/misc/plot_results.ipynb b/misc/plot_results.ipynb index 985e32d..c368973 100644 --- a/misc/plot_results.ipynb +++ b/misc/plot_results.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 43, "id": "1911b805", "metadata": {}, "outputs": [], @@ -23,7 +23,8 @@ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", - "import numpy as np" + "import numpy as np\n", + "from collections import Counter" ] }, { @@ -48,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 73, "id": "5c18d62b", "metadata": {}, "outputs": [], @@ -71,7 +72,10 @@ " clustering_result_path = Path(\"../results/result_cluster_ouput.csv\")\n", " \n", " # genomes analysis\n", - " genome_analysis_path = Path(\"../results/genomes_predictions.csv\")" + " genome_analysis_path = Path(\"../results/genomes_predictions.csv\")\n", + " \n", + " # uniprot toxin information\n", + " toxin_uniprot_info = Path(\"../results/uniprot_toxin_info.tsv\")" ] }, { @@ -103,7 +107,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "def expand_colors(colors, num_intermediate):\n", " def hex_to_rgb(color):\n", " return tuple(int(color[i:i+2], 16) for i in (1, 3, 5))\n", @@ -389,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "id": "975b511f", "metadata": {}, "outputs": [], @@ -400,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 9, "id": "b716a8e9", "metadata": {}, "outputs": [], @@ -418,20 +421,18 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 10, "id": "ef913563", "metadata": {}, "outputs": [ { "data": { - "image/png": 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V/2twb4OpXZigzXf3kt9jPeSyJ3rlynXL2dMj2axn5iveSdfWR63//4WrdmGCyv6T3qF17gqIFSNihViJFMRmaEpf6tFhdQHhivS4xa7h8j2v1B3732P5WWNTo9ZVrlVZQ5nl5wf1OVjvHvOhYhzBJSyEI8YRo3eP+VD79N7P8vOKxgqtrVijhibzapMOu0P3HviQLhl9edDnmznlCZ094jzLz0rrS7WqfKU2VK1XfVO95T6H9j9cbxz9rmId7X9YKMmVpMtCTFRH5Ll8ryt1x5RWYqx8rcrqW4ix/gfr3VM6N8ZOHXmGZhz5uGKd5j5b467RuvK1WlO2WpUNlZbHD84YovdO/UT90voHdb6Zf3hCZ49pIcbqSrWqdKU2VKxXvaeFGBt0uN44qWNiDLDC9RdCFel9hnkw5sHQtSL9bwQAAN2V9dUqEKH8fr9uuOEG/fe//zV9NnXqVJ111lkaN26cHA7ziiE+n09ff/21nnjiCS1atKi5vK6uTn/+8581a9YsDR8+vFPbD8Ba/S9xKn7SPGBMGF+rHn8qU/zIetn+N+fhrbKr8sNUlf0nXd6K7V9jDb/Gq/iJTGVfvnVnNRvY6YgVhCIa+kvxE5lq+M04YW+L9Sn92AqlHV0hV9bvr730+6XGtTEqfyNDVZ8aVwbb+nim4oY1tPlKTr9XKvx7jnzVxutEZ6ZHGX8qU+rUKtkT/M371i5KUNnLPVT/8/b2+d12Ff5fb/V/er0ciS2/itpbY9fm+3tJPuNkZky/RmWcUK6kfWvkSNl+vLfKruqvklX6SoaatroMx1S8na7kA2uUMNL6hnEomkod2nxvr+BWpelGiBUjYoVYiRTEZmjKZqep5ltWikTXioa4RfTbq9dE3bnfvabyj9d/qPt+uEvfFnwtv36/VkmPTdfZI87TleOuVVZCVvO+E3P20T0HPKCrvgg+mToU9xzwgCb0mmgoq/PU6ZEf/6V/L3tU+TX5zeUje47SFWOv1mm7nWms48AH9UPR9/p+y4JWz3XabmeakrxrPbW6/4e79fKvL2hj9cbmcofNoXHZ43XpmCt00rBTDMcc1Odg3TjxFt363U0h/aw7ctldenzq0+qT3DfsOtD19sqdqDunWMTYmg9137d36duNO8RYXLrOHnOertznWmUl7hBjefvonqkP6KqPOj7GBqUP1qN/eNJU/uLS5/TI9//SsqKlze2Tfk/qPmP02bp8r6sU79p+3dY7JVfP/PFFTXn+gFbPd9qoM01J3rXuWt3/3d16efkL2lgZEGO9x+vSva7QSSMCYqz/wbrxwFt06+fhxxhghesvhCoa+gzzYP/bn3kwdIFo+BsBAEB3xYreiCpPPfWUKck7PT1dTz31lB555BHttddelknekmS323XQQQfppZde0hVXXGH4rLq6WldccYUaGxs7re0ArPl9UtHMLMlrnEDIOLlMfe4qUMLo7QNGSXKk+JRxUrn6PrTp9yfad1Dx31Q1rGBlFOyaiBWEIhr6S8PqWFX8N9VQZk/2qs89+co8r6R5wl6SbDYpbpBbOddvUdblRcbVTHw2Ff0rq83XVVa8k6bGtcaVVWIHNqjfjI1Kn1bZPGEvSTaHlLRXnfo8sEnJB1cZjmkqdarkudZXTC2fnW5aASblsEr1e3SjUg+vMkzYS7///tOOqtSAp9Yrca8aU32lL7R/hVa/T9p8d45hwhXEihVihViJBMRmcHxum2oXJij/5t4qfjyr7QOAThQNcYvoZ5NND06aKZfDmOhy/w9364/vHKFvCr4yJHiWN5brocX3a/J/9lNBdb7hmAtG/lljs8Z1eBtHZ47RBSP/bCgrayjTH96aqlu+u9GQ5C1Jy0uW6fyPz9IVn18in3/7tY/T7tTDkx9v9Vyxjljdtu8/DGV1njodNnuS7v7+DkOStyR5/V59v2WBzvrwVF362YWm+q4ce41yEnOC+jl3lBabptN2O1PfnvKDjh96UsjHI3LYZNODh1nE2Ld364+vHKFvNgbEWEO5Hpp/vyY/t58KqgJibNyfNTan42Pszin3mFbyvvKDS3XhnHO0tOhHQ/skaXXZKt36+U2a+sIBphW+9+2zv47b7YQWzxXriNVtkyxi7MVJuvubOwxJ3tL/Yqxggc5661Rd+p5FjO19jXKSQo8xoCVcfyFU0dBnmAfbjnkw7GzR8DcCAIDujERvRI1Vq1bp4YcfNpRlZWXppZde0gEHtL7qwo5sNpsuueQSXXrppYby9evX69FHH+2QtgIIXs23SWpcZZxESdy7Rpnnl7R6XEyeRzk3bDEW+mwqez2jo5sIRARiBaGIhv5S8nwP0+olva4qUvyI1ldZSZ9WqZRDjBPpjWvjVPt9YovH+Nw2lb5s/BlscT7l3lYoZw/rVwxKv0/e51y/Ra5ct6G88oNUeaush1J+r1Qxx3gzIm73evW6qkj2mNbvLNjj/ep962a5ehvPV7c8Xt6a9g3dSl/KUN2PCc3bttiWV5fpTogVI2KFWIkUxKa1mgWJKpudpqIZWdp4ZR+tPn6Q8m/MU+18VvJG14uGuEX0++PgYzUue7yh7L21c3Tzt9NbPW5NxWqd/dFphjKH3aG/jruuw9t48963yWE3LkRy6WcXaP7mea0e9+9lj2nWr88bykZnjtFh/Y9o8ZipfQ9V76RcQ9kd82/VoqIf2mznMz89qSeXGxPJYxwxOn5I64nacY44nTzsFF0z/gb9+5BnNf/UJdp0YbGeOvR5jew5qs3zIrL9cfixGtc7IMZWztHNn7cRY+WrdfbbFjG2T8fGWHpcuo4ccrSh7INV7+mJRW3f11m8eZEuf/8iU/mf9jjNYu/fTR14qHqnBMTYl7dq0eYgYmzJk3pykUWM7c7DEOg4XH8hVNHQZ5gHM2MeDDtLNPyNAACgOyPRG1Fj+vTpcru3D2CcTqcee+wxDRo0KKz6LrvsMo0ZM8ZQ9tJLL6m+vv2vOAIQvPJ30owFTn/Qr3JKGF2vhLG1hrLqb5PkKebJc+x6iBWEItL7i3uz0zTJnjC+VskHmFcmsdLzzFLJaZwAL387rcX9q79IlrfS2P4ep5XKld3UwhHb2Rz/O98O/I12Vbyfarl//fJ407kyzyuRzfqlMyb2GL/Sj60wFnptalgZZ7l/MOqWxqt01vYVXly93Uo7qqLlA7oRYsWIWCFWIgWxaa1oRpaKH89Sxbtpqv85Xv4GpvUQOSI9brFruHjUZYZtt9etKz+/JKhjvyn4Sp9u+NhQNm3QscoNSJRuj34p/XV4/yMNZR+v/1Bvr34zqOPvnP93ub3GBJ5LRv+lxf0n951q2PZ4PXr6p38H2Vrp/oV3mcr2bGOV86yEbD13+Mu6fb+7dMbuZ2t05hg57cTqruLi8RYx9mGQMbbxK326NiDGhh2r3OSOi7ED+00yrTb+7yCSvLf5zy+vaV35WkPZnq2sOj55gEWMLQkhxr6ziLFOWOUc3RfXXwhVpPcZ5sFaxjwYdoZI/xsBAEB3xx0hRIX58+dr+fLlhrLzzz9fe+yxR9h12u12XXHFFYay6upqffTRR2HXCSA0nq1O1S9NMJQl7V1jePVaWwKf0JfXpuqvWNEOuxZiBaGIhv5SPTfFtDJL+rSKoI93ZTcpYWSdoaxucUKLK6ZUfZps2La5fEo7stJyXytJ+9fIFmdczaT68xTLfet/NU6uO1K8ih8Z2oOE8bub9/eWBznrH6Cp0q7Nd/fa/vt2+pVz42bDa0e7K2LFjFghViIBsQlEn2iIW0S/vKQ8HdTnYEPZ++vmKL8mP+g6Xvr1BcO2y+HScUNO7JD2SdKfhp1mWs37iWWPBH38xuqN+qbgK0PZlL6HKCPOeiW8vKQ+hu11VWtV5a6y3Lel822tMyZuZCZkBX08di15KXk6qH9AjK2co/yqEGJsmUWM7dZxMZaX0sdU9uOWxSHVsThgNe7W+nzg+dZVrFVVYwgxVrlRW2uJMXQOrr8QqmjoM8yDtY55MHSmaPgbAQBAd8cdJESFWbNmGbYTExN10UXm1+yFat9991WfPsbJusWLQ5sYrKys1Jdffqn//Oc/euKJJzRr1ix98MEHKigoaHf7OovP59OqVav07rvv6tlnn9Wjjz6q5557Tu+8845++uknw8rpHaG+vl6ff/65nn76aT366KPatGlTh9aP6FUz3/zKtNTAQWAbkibWSnbjxEPd4oQW9gaiE7GCUERDf6mZZ5zcc6Q1KXGv2hb2tpa0b8D+Ppvqlpjb6K21q265sTxxr1o5UoJ/DaU9xq/E8cbzNa6LUZPFRLp7U4xhO6Z/o2w2026tny/e3DZfGKu2+v3Slnt7qalk+4pnPc8uUfywxpDr2hURK0bECrESKYhNIPpEQ9wi+h058GhT2axfng+pjg/Xv6cmnzFR4eA+U1vYO3RHDZxm2N5at1Ufrf8gpDr+u/Ydw7bD7tCkvMmW+8a7jDES+LMFI8ZhvCar84T2fYZdx5FDLGJsWYgxttoixgZ0XIwF9nkp9H4f2OdrW+nzxBgiGddfCFU09Bnmwdo4H/Ng6ETR8DcCAIDujvdkIOKVl5dr7ty5hrIjjjhCCQkdc1F42WWXGZK7BwwYENRx33zzjZ566iktXLhQTU3WE3xDhgzR6aefrhNOOEFOZ9eHW1lZmZ599lm98847KioqanG/hIQETZo0Seecc45GjRrVZr1vvvmmpk+f3rx988036/TTT5ckvfbaa3rooYdUXl7e/PnQoUNNCfbonuoWBwwabX7FjwrtCXZHsk8x/dxyr4vdXu/yBPm9CvqVZ0CkI1YQikjvL94auxpWxhrK4kfUh1xv/Ajzz1T7Y4KSDzK+yrNuabzUZJw1Txgd2u9j2/lqvtlhlRe/TXVL45UyyXg+b5XxB3Gme0M+V1OZ+brRkRJ6PeVvpKv2++03SBLG1irjpPJWjuheiBViZRtiJbIQmy2/krrfzI1SK/ectz6apeovk1veAegkkR632DVM6XOIYdvn95lWv25LRWOFfi37RSN7bp/v3D/3QDlsDnn9oV9D7Cg1JlVjs8cbyuYVfhNyvfMKvzWVHdRnst5c/YapvKh2i2G7f8oAJTgTVNdUZ9rXyrD04UqLTTOUra5Y1eox+TWb1P/JXq3u882fFiovmbnfaDNlgEWMbQwxxhoq9GvxLxqZvUOM9e2YGJPMfV6Sds/cQ19t+CKo422yaXzviYayNWUt93lTjKUNUIIrQXWeIGOsx3ClxaUZyla3cj4gFFx/IVSR3meYB2sb82DoTJH+NwIAAJDojSiwePFieb3GQcpRRx3VYfUfc8wxOuaYY4Lev6qqSjfddJM+/vjjNvddtWqVbr31Vj377LP617/+peHDh7ejpe3z3nvv6ZZbblFNTcs3jbepq6vT+++/r/fff18nnHCCbr31VsXExLR53I58Pp9uvfVWvf766+E2Gd1A4KRNTF+3HEnBPy2/TdzgBsOg0V9vl2eLSzG5nna3EYgExApCEen9pWFVrOQ3TqLH794Qcj2x/Rtlc/nk92xftWTH9m7TuDLOVBZn8ZrLtsQNMa9o0rguVgqYtE87qvL3lSv+J6ZP6G9KqbVY5SJ2UGgrqtT/FqfiZ3o2bztSm5Rz3ZaQV4rZlRErRsTK74iVrkdstsyZ1voNXFtM6L8noCNEetxi17Bn9jjD9m9lv6rSXRlyPT9uXWxI9E6OSVa/lP5aW7mmXe0bkzVWdptxRcUFm+eFXM/PpT+psalRsc7tsbBHz5GW+35b+LVO3/2s5u0EV4LO3eMCzfzxX0Gd67oJN5rKPt/0WavH+Pw+FdW1vICIpA5J6MXOt2dOQIyV/KrKxjBibMtiQ6J3cmyy+qX119ry9sWYJH278WtT2WV7XRF0oveZY85RTnKOoezz9S33+W83fq3TRwXE2J4XaOb3QcbY/hYx1sr5gFBw/YVQRXqfYR6sbcyDoTNF+t8IAAAghf4uF2An23G1bUmy2WwaOdJ6cruzlZeX66yzzgoqyXtH69ev1ymnnKIvvviicxrWhjlz5ujqq68OKsk70BtvvKHrrrsu5ONuu+02krzRKm+VXU3FLkNZ7IDwXgsW2988IeIpdFnsCUQfYgWhiIb+0rjGPLEeThttLsnV2zg56N5sbl9D4PlsfsUOCH0iPaZfcL+PpL1rlXZ0ZfO/hDGh3SBoKneo8oNU47n7Nyqmd/ATod5auzbf2cuwKk2va4rk7EHCxTbECrEiESuRiNjkugzRJxriFtEvPTZdfZL7Gsp+KlkWVl2/lv5sKhuYOiisunY0OnOMqWx5GG30+DxaU7naUDaghfa9sfI1ldSXGMpu2+8uHTVwWpvnuWLPv+rU3c4wlP1S+rM+2/hJiC3GriA9Ll19UgNibGuYMVZsEWPp7Y8xSVpbvkafrjXemzl62DH6+6Q72jx2v74H6IFDHzaUNTQ16OnFT7R4zBu/vKaSuoAYO/guHTU0iBib+FedOjIgxop/1mdriTG0H9dfCFU09BnmwVrHPBg6UzT8jQAAACR6IwosX77csN2vXz8lJSW1sHfn8Xq9+vOf/6xffvnFUD5hwgTdc889eu+99zR//nx98MEHevzxx3XkkUfK5dp+0VpXV6errrpKK1as2Kntrqqq0p133im/399cFhcXp3POOUezZs3SV199pYULF+qLL77Q888/rwsuuECpqcaB4gcffKBvvvkm6HN+9NFHeuWVV5q309PTdeSRR+q8887TxRdfrLy8vPb/YIh6HouJFVdWU1h1OXuajyNJArsKYgWhiIb+4tlsfkuIM9w29jAe5y1zyldvXH7Es8XYZkeqV/YYv0LlSPXK5jKuYGH1s7SHZ6tTBbf0lrfc+OKlnqeXhlTPlgey5dmyvW1px5Qrae/aVo7ofogVYkUiViIRsWmOTSDSRUPcIvoNSB1oKttUvTGsugprC0xlA9Pan4TaoW2sMbYxJzFHCU7zCo41nhpd+fklhrJ4Z7z+c/Q7eu7wlzSx196GVcYdNof2ydlXrx/1lu4+8AHDcQ1NDbr4k3PDai+i34B0i/5bGWb/rbaIsQ5K9JakKz+4VOX15Yay6/e/SV+c/Z2OHvpHxTvjDZ8NyRiqu6ferw9O+0yJMYmGz/722fXa2MrPWeOu0ZUfBMSYK17/OekdPXfMS5qYaxFjefvq9RPf0t2HWMTYHGIMHYPrL4QqGvoM82AtYx4MnS0a/kYAAADJ2fYuQNcqLTUOUgYPHtwl7Xj66ae1ZMkSQ9n06dN19tlnG8rS09M1cOBAHXzwwVq0aJH+8pe/qKTk91Uf6urq9Je//EX//e9/DUngnemTTz5Refn2ic+0tDQ9//zzGj58uGG/lJQU5eTkaO+999aZZ56pM844Q+vXr2/+fM6cOdp///2DOuf3338vSXK5XLrmmmt02mmn7bSfF9HDU2L+CnJmdsykjSR5Kx1h1QVEGmIFoYiG/tJk0UZXVniv7bNsY5VD9vjt5U3FxvOF+/uw2SRHhldNRdtv4obz+/D7JL/bJnucX74Gm7wVDrkLYlTzbZKqPkuWr85YZ9q0CiUfFPxbWcrnpKrm6+Tm7diBjcq8oKSVI7onYoVYIVYiE7Fpjk0g0kVD3CL65SaZF43Ir94UVl2FNYWmsp7xmWHVtSPLNtaE18bNtVZt7KmNFonjs1f9RzEfnaGZk59Qgmt7MvjJw07VycNOVY27RlvqNssuu7ITeynRlWiqo6yhTGd+8CctLPo+rPYi+uUmW/Tfqg6MsYT2x9g2a8pX68iXpujVE95Uv7T+zeUT8/bR6ye9LY/Xo4LqfLm9bmUmZCk9Pt1UR5OvSX///CY9svBh02eBZv/6H8W8c4ZmHhkQY3ucqpP3+F+M1WyW3fa/GIuxiLH6Mp351p+0sJAYQ8fg+guhioY+wzwY82DoOtHwNwIAAJDojShQWVlp2E5JSdnpbSgrK9PMmTMNZeedd54pyTvQuHHjNHPmTJ166qny+X5/mnf9+vV67733dMwxx3RSa42+++47w/all15qSvIOlJWVpWuvvVaXXnppc9maNWtCOq/NZtPDDz+syZMnh3Qcug9vmfkryJEa3uvB7Ak+U5mvgZdWYNdArCAU0dBfmsqMk3o2l0/2+NBXS5Eke2LrbfS5bfLVGs8X7u9DMv9OfA2hr7raVOrU2lPNq6UFssX61OO0UvU4pbzNfbdpWBOj4se330C3xfmUc+PmsFaj2dURK8QKsRKZiE2uzRB9oiFuEf2yE3uZykobwktMqXZXmcoSnebEzFBlJxjb2NDUoFpPeCskWrUxwSJBe5tXfpuleYXf6tZ9btefhp9m+CwpJkmDY4a0eOwnGz7SxZ+cq0KL5HJ0H9lJFjFWF2aMNVrEWCv9Nxw/blmiCf8epcsnXqXr9rtRsc7Y5s9cDpf6pw1o8di15Wt09lunhpR0/cryWZq36VvdOul2/WkPixjLaCXG1nyki/97rgqriTF0HK6/EKpo6DPMgzEPhq4TDX8jAACAxDcqIl5gondiYsdOCgbjjTfeUGNjY/N2VlaWrrzyyqCO3XPPPXXKKacYyp599tmObF6rCguNE4jTpk0L6rjx48cbtndcFTwYp556KkneaJWv0TzRYYs1D/6CYbOYjPDX8xWHXQOxglBEQ3/xNRrrsMWGP6Fs1cYdJ9L9FpPq9nacL3DyuzMnKDNOKFfatMq2d9zWlnqbNt/ZW3739jZlXVys2H7uzmhe1CNWiBViJTIRm+HdEAa6UjTELaJfgjPBVFbfVB9WXQ3eBnP9HZCEGu8ytjHc9km/J4kHaitR9sC8Sdov94CQzzWx1z46dbczFeOICflY7DoSXB0YYxb9N8Filev2GpU9WocPPtKQ5B2Mfqn9de7YC5WdmB3ScQf2m6T9+oQRY3n76NSRxBg6FtdfCFU09BnmwYLDPBg6QzT8jQAAACR6IwpsWwl7m65I9J49e7Zh+4wzzlBMTPATc6edZlzl4bffflNpaWmHtK0tY8aM0ZFHHqkjjzxSp5xyitLS0oI6zuVyGbb9/uAHuHa7XRdddFEozUQ3tOOkwjZ2V3gTKZaDRg8JEtg1ECsIRTT0F7/bWIfVeYJl9bPt+DsIPJf0+2ow4bLFGI+1qr+jlL7UQ2vPGKCK/6YGtX/RjCy5N22/Pk06oFppfwh+0r+7IVaIlW2IlchCbFr/DoBIFg1xi+gX64wzlVklkwbD6rg4i/pDFecw1mGVUB4sq2MD698mwZmg2dPm6IlDnlGf5L4hnyslNkW373eXPjn+S2UlZIV8PHYN0RBjO/rbgX/XR2d8oQm5E0M+1mF36Owx52n+BT9qQu+92tw/wZWg2SfP0RNHP6M+qWHG2OS79MkZXyorkRhDx+D6C6GKhj7DPFhwmAdDZ4iGvxEAAEAyv4MDiDAxMTFqampq3na7d+4TpsXFxVq/fr2h7PDDDw+pjkGDBik3N1cFBQXNZT/88IMOO+ywjmhiq66//vqwjluyZEnY59xtt92UnR3aihjohizGh7YwB42ymH8Juy4g0hArCEU09JeAKmzO8Ou0eg5tx4l1v99q0r4dP4Ov/TccbE6/Yvr//qYYf4NN3lqHfNUOy3191Q4V/Stb7k0xyvpzcYt1Vn6coqpPtk/uOzM96nVVUcht61aIFWJFxEpEIjZNN4iBiBcNcYuoZ7dYr8btC2+O2G4z1xVuQmtr9Xq84c9hW7bRIvnbaXfqnWM+0P65B5o++2TDR3rxl2e1YPM8FdUVyWl3qndirvbN3V/njDhfE3P2Mey/V87e+vSEr7Xfq+NV7a4Ou+2ITlZ9zh1mH+6sGNvm9oPv0jX73WAq/7X4Fz3xwyP6csPnyq/aJI/Xo8zETI3LmaATdj9Zx+x2vJz27bdEeyX10kdnfKHJz++nH7dY3wtx2p1655QPtH9fixhb85FeXPqsFhTMU1HN/2IsOVf79t1f54w5XxPzAmIsb299eubX2u9pYgwdgOsvhCoa+gzzYMyDoetEw98IAABAojciX3Jysurq6pq3a2pqdur5f/zxR8N279691bdv6Cs3DBs2zJDovWbNmvY2rcP5/X4VFRVp/vz5uv/++8Oup0+fPh3YKuyqrF755G8K74lev9ei/jgSJLBrIFYQimjoL4Gv3Qy3fZIkb+uv2bR34O9DMv9OrOpvizPdqwFPbjCUeavsalgRp+qvklX1WbL8HuON8fI30xU7oFGph1eZ6nNvcqloxg6rgtn9ypm+RY5kYrs1xAqxQqxEJmKzfa+LBrpCNMQtol99U52pzGV3WezZth0TPVurP1R1AXWE2z7Juo11HnMbb93ndlOSd0Vjhc7/6Ey9t26OobzR26hVFSu1qmKlnv/5GZ087BTNmPyEkmOSm/cZkj5U/5z0iM77+Myw247oVG/Rv1yODowxi/rDceigw01J3j6/T3d89Xfd/fUd8gdkKeVX5Su/Kl/vrHhLe84bqxeOe1WDM4Y0fx7vites417X2CdGWCa233rQ7aYk74qGCp3/zpl6b5VFjJWt1KqylXr+x2d08ohTNOPIJ5Qcu0OM9Riqfx7xiM57hxhD+3D9hVBFQ59hHox5MHSdaPgbAQAASPRGFMjOzlZR0fanSysqKnbq+QNX8x46dGhY9eTm5hq2Kyu75rVIPp9P69ev14oVK7Rp0yYVFBQoPz9f+fn5Kiws7JAV0+Pj4zugpdjV2eM67tVN/nqLV0qRIIFdBLGCUERDf7EHTOq157V9Pos27jgpaevA34ck+RqM5wu8AREuR4pPiRPqlDihTj1OL1XB//VW4yrjq62Ln8xU8kHVssdvP6fPbVPhHTny79CuHqeWKWFkfYe0a1dGrBArxEpkIjatb64BkSwa4hbRr9ZTayqLdcSGVVeSK8lUFpikHY66gDbGhNk+Kbg29k7srcvGXGkoc3vdOuHdafq28Os2z/HaildUXF+st6e9b0joPXnYqbpn4Z1aWb4ivMYjKnVojMVY9N8OSvS+c8q9prJbP79R9393T5vHLtmyWIe/eLC+PGe+clPymssHZQzWGaPP1tOL/23Yv3dyb1028UpDmdvr1gmvT9O3G4OIsZ9fUXFdsd7+U0CMjThV93xzp1aWEmMIH9dfCFU09BnmwcyYB8POEg1/IwAAgCzeeQhEmLy8PMP2ypUrO7T+mpoa/fDDD4Z/OyY7V1UZn4LNzs4O6zwJCQmG7Z2d6L1582bdcccdOuCAA3TEEUfoyiuv1AMPPKBXX31V33zzjdavX98hSd5AsOzx5gQGb014X0veOvNxjoymsOoCIg2xglBEQ38JbKOvzm75Os1g+Cza6EzfvmSEPcYvOYyV+2qsX3kZ1PlqjedzdkL8uLKb1Pf+TXJmegzl3iqHqr9KNpSVPN1TjWu3T+7Hj6hXj9NLO7xNuyJihVghViITsWmMTSAaREPcIvrVesxveEyNTQurruSYFFNZUe2WsOraUU1AG1MszhMsqzZurSsybJ+62xmKcxoTfZ5c9lhQSd7bzN34qZ7/5RlDmcPu0Om7nRVCa7ErqHVbxFhcWlh1JcdaxFhN+2Nsr9yJ2iNrpKHsxy1Lgkry3qagukA3fnatqfyMUWebyk4daRFjix4LKsl7m7nrPtXzSy1ibBQxhvbh+guhioY+wzxY65gHQ2eKhr8RAACAFb0RBXbbbTe9//77zdsbNmxQTU2NkpLMK0OE47333tMtt9zSvB0bG6vFixc3bwcmZCcmJnbIeW22drxyKkQvvvii7r333qATuYcOHarDDjtMM2bM6OSWoTtz9jAP6rzl4X0tWR3nyvZY7AlEH2IFoYiG/mJqo9cmb6VDzrTQE9uayo0T8I60JtPqE84eTWra6mrxmFB4K4zHurI7Z4LSnuBXj9PLVPSQ8QHDuiUJSj1s+0OIDauNK6x5tjq1/uJ+QZ0j8GeRpHUXmI/tc1e+nD13vaRDYoVYIVYiE7Fpjk0g0kVD3CL6FdYWmsp6JfQKq67sBPMiHhuq1odV14421xjb6HK41DO+p0rqS0KuKyugjUV1RapvMq7AOLnvIabjHl/2SMjnemzpDJ0/8iJD2YF5k0KuB9GtsNoixhLDjLFEixirXB9WXTuaPMDc559c9FjI9bz+86u679B/KSsxq7lsbM54JbgSDCuPW53v8R/CiLGFM3T+2IAY6zcp5HqAHXH9hVBFQ59hHqxtzIOhs0TD3wgAAECiN6LA6NGjDdt+v1/fffedDj300A6pf/78+YbtwYMHy+ncHhqBCdlNTeENzmprja8/TEkJf1WXUMyYMUMzZ860/CwzM1MDBgxQXl6e+vTpo+HDh2vkyJHKzMxUfn4+id7oVK4886DOUxze11JTicWgsReDRuwaiBWEIhr6S0yuuY6mEmd4k/YBbbRqX0ye2zhpb/FzBXWuCof8HuNqFJ0ZP0l716hIxkl796aYVo9pKnZJxeGf073e/Gpuf9POezhxZyJWiBViJTIRm1yXIfpEQ9wi+q0uN7/hMTcpz2LPtlkdt75qXVh17WhVhXUbw0n0Dmzjhkpz+4Zn7G7Y3lxTqNUVq0I+1y+lP6u4rliZCZnNZf1SBoRcD6Lb6jKL/psSZowlW8RYRftjbLfM3U1lX234Iqy6vt34lY7d7YTmbZfDpbyUPlpZuqK5bHjPgBirLtTqsjBirPhnFdcWKzNxhxhLI8bQPlx/IVTR0GeYBwsO82DoDNHwNwIAAJDojSgwduxYpaSkqKpq+5Oob731Vockejc0NOiLL74wlI0fP96wnZqaatiuqTG/xjAYBQUFhu2dkei9bNkyU5J3enq6LrzwQh1++OHq3bt3p7cBaImrZ5NscT75G7ZPgLg3tD4Z0RJ3vvE4W5zPclIIiEbECkIRDf0lJs/8hpHG9TGKG9wYUj1+n+QpdBnKYgeZ64jJ9ahu+8ta5Kt1yFPslCsztIf33PkuU1nsQOP5Kt5PVd2S+O0Fdqn39PBeUe3M8Jr+X3prw3tdIsyIFWIFkYnYDK0OIBJEQ9wi+hXWFqrGXaOkmO1veNytx4iw6hqcPtSwXeOu0ZqK1e1qn2SdjL5bxggtLf4xpHpssmlg6iBD2bKSpab9MmIzDNuFtQWmfYJVWFtgSPTOiMtoZW/sigqrLWIsM8wY62ERY2Xtj7F0i35ZWB1evy+oyjeVZcRntLod7rm2Hbtjondg3UCouP5CqKKhzzAPFhzmwdAZouFvBAAAkLjqQ8RzuVyaOnWqoeyrr77Spk2b2l33u+++q7q6OkPZUUcdZdhOS0szbK9bF97qE2vXrjVsDx48OKx6QvHUU08ZtnNycvTOO+/o3HPPbTPJ2+fzdWbTAElS3OAGw3bjmjj5w3hTeeBryOKHN8gW/lvWgIhDrCAUkd5fYoeYJ9YbV8eFXI97Q4xptZT4EfWm/WKHNJjKGleZVyFpi1Ub4/cwns9d4FL1Fynb/81NUZPF6y6DZU/wtbqN9iFWAs9HrCAyEJtA9In0uMWuYWnxEsP2qMwxYdUzJnOsYXth0QJ5/e1/7fyS4sWmsj2zxlrs2brde4xQnNP4vfJd4Tem/dw+YzKU1xf+z1DvMc6P13jCW+gE0W3ploAYyx4TVj1jegXEWEHHxJjba04ADLff1zXVmcpq3MZ+H3i+Do0xNzGG9uP6C6GK9D7DPFjwmAdDZ4j0vxEAAIAVvRElTj/9dL355pvN201NTbrzzjv1+OOPh11nY2OjnnjiCUPZ4MGDNWrUKEPZ8OHDDdsrVqyQ2+1WTEzwTzFu2bJFGzZsMJSNHDkyxBaHbt68eYbtq6++WtnZ2S3sbVRUVNQZTQIMEsbVqf6nhOZtb5VDjStjFTcs+Cf0vdV2Na4JGDSOMk/WA9GMWEEoIr2/xPT2yJXjlmfz9mup2kUJrRxhrW6p+ZiEkeZJ+8Rx5nbXLkpU0r61IZ4v3rDt6uWRK8u4wosz1Xzj170pRs600BP3/H7JV228KRH4qtK+D5hXIQtW5Ucp2nJ/L0PZsE/MqxDuyogVI2LFGrGy8xGbQPSJ9LjFruHTjR9rv9wDmrd7xvfU2KxxWrx1UdB1pMWmaVTmaEPZNwVfdUj71lWu1dqKNRqYtn017il9Q38j5YF5k0xl3xZ8bSorrtuq5Jjk5u2cpPDf3NgjvqepbnQ/n679WPv13SHGEnpqbM44Ld4cQozFpWlUdkCMbeyYGLPqlznJvbW+IvSFeQL7vCQV1xrrL67bquTYHWIsuR0xlhAQY7XEGNqP6y+EKtL7DPNgwWEeDJ0l0v9GAAAAVvRGlBgxYoQmT55sKPv888/19ttvh13n3Xffrfx840Dnr3/9q2m/UaNGyW7fHip1dXX69ttvQzrXZ599ZtjOzc1tc0Xt9qqqqlJVVZWhbOLEiUEf//PPP3d0kwCTxPHmwV31N8kWe7as5rskyWczlCUfwKoo2LUQKwhFNPSXwDa618eqcaP5NZetqf46ybAdN7RBrl7mV2u6spoU09c4GVnzXaJCWVDMV29T7aJEQ1nSAdWm/axWgqn/OfSVZySpYWWsafWZ2AGhvaoUrSNWjIgVRApiE4g+0RC3iH6fbvjIVHbM4ONDquOogX+U025c++atVW+0q107+mSjsY0jeu6hoenDQqoj8GdaVLRQG6s3mPbbVL3RsN0nua/6pwwI6VySlBKTogGpAw1lgauno3v4dK1FjA0PMcaGWsTYrx0TY5sqN5rKDuw3Kay6Alcd31xdqKJa48I3gefrk9pX/dPCiLHYFA1ID4ixImIM7cf1F0IVDX2GebC2MQ+GzhINfyMAAOjuSPRG1Jg+fboSEoxP4d50002mJOpgPP/883r55ZcNZfvuu6+mTJli2jcpKUl77bWXoezFF18M6XyvvfaaYfvYY48N6fhwNDSYB41xccENGr1er1599dWObhJgEjesQTF9jK/BrPwwRb56WwtHGPl9UvmbaYay2CENiu1vfpUnEM2IFYQiGvpLytQqU1n5W+lBH9+wMlb1y4zXhSmHmOts6XxNJS7TpH9rKt5Llb/eOHRKtThf/O4NsrmMr8qs/iq0ydBtKv+bZipL3Cu0FWXQOmKl7fMRK+gKxCYQfaIhbhH9fihaqBVlvxnKzhpxnhJdiS0cYWSTTZfteaWhbFHRD/q17JeOaqJe+dU8Z3zpmCuCPn7PrLGmFb1n/fK85b6fbvzYVHbByIuDPtc2xw45wZSYa1U3dn0/FC7UipKAGBsTYoztdaWhbFHhD/q1pGNi7LO1Fn1+bOh9flD6YI3ptaex7nWfmPb7tIPOd+xuFjFmUTcQKq6/EKpo6DPMg7WNeTB0lmj4GwEAQHdHojeiRt++ffX3v//dUNbU1KTLL79cd9xxh2pq2n4asLa2VjfeeKP+8Y9/GMp79eql+++/v8XjTjvtNMP2t99+q08+MU/+WZk9e7ZWrFjRvO10OndKondaWppsNuOF99KlS4M69h//+IfWrQv9lYdAqGw2Kf34ckOZt8Kpkhd7BHV82asZalxrfIAhfVpFRzUPiBjECkIRDf0lfvcGxe1ufDVl5Qepalgd28IR2/kabNryYLahzJ7gVcqUlift046qlC3OOJle8nRP+eranqRs3OhSacDvLn5knWIHmCco7bF+JR9ovCZtXBWnqs9Dm7ivXZigyo9TDGWuHLfi9wj9dZ5oGbFiRqwgEhCbQPSJhrjFruHhJQ8atrMSsnTTxFuDOvaaCTdodOYYQ9m/lz3aUU2TJC3YMl/zC78zlJ0z4nyN6jm6zWPjnfF6dMpThrLKxkq9uuIly/3/u/YdU9mfx1yuET32CLq9GXEZpt9fRWOF3l39VtB1YNfy8IKAGEvM0k0HBhlj+92g0b3GGMr+vajjYuz7ggXaXL3ZUDY+dy+ds+f5Qddhk033HvqQHHaHofyFpc+a9v3vSosYm3C5RmSGEGPxGbrpgIAYa6jQu78RY2g/rr8QqmjoM8yDtY55MHSmaPgbAQBAd0eiN6LKH//4R11zzTWGMq/XqxdffFFTpkzRjTfeqM8++0xr165VVVWVGhsbtWnTJs2bN0+33XabpkyZotmzZxuOT01N1aOPPqoePVq+SJ0yZYp22203Q9l1112n+fPnt9reefPmmZLKzzjjDOXl5QXz47ZLTEyMRo4caSi77777VF1tfmXUNmVlZbrqqqs0a9Ys02c+n8/iCKD9Ug6pkrOnx1BW/ka6Kj9pfYKj8sMUlTxvjNuYPm5WwsMui1hBKHZ2f1lxyFDTP88WZ6vH9DilzFjgtanwthx5trZ8nK/epsI7c9S4xjhhmHFSuRzJLV+rOJJ9SjuqwlDm2RKjwn/kyOdueeLeXehSwc258tUZbwL3PLekxWMCJ0MlacuD2apdEt/iMTuq/jJJBbf1Nr3isOfZpbI5WjgIYSNWjIgVRApiE4g+jFewM7z06wsqqM43lF0x9mqdMvz0Vo87Y/ezdevetxvKVpT9plm/Wq+WvU39FX7Tv77J/Vo95t4fjHPBLodLr/xhtvKSWp4PTnQl6oUjXtWYLOMqww8tuk8VjRWWx/xW9qteX/GKoSzeGa/Z0+ZoaPqwVtsoSblJuXrnjx+oT3JfQ/mMJQ+p0l3Z5vHYNb207AUVVAXE2N5X65SRbcTY6LN160EBMVbym2YtayPG/uY3/eubah1jfvl159d/N5U/cOjDmjbsmFbPI0mxjlg9dtRTOnLIUYbyL9d/rq83fGna/7eSX/X6zwEx5orX7JPnaGiPIGIsOVfv/OkD9UkNiLEFD6mykRhDx+D6C6FirG3EPBhgxPcKAACRjURvRJ0LLrhA99xzjxISjK9eqqio0OzZs3XJJZfoiCOO0IQJEzRq1ChNnTpVZ599tl566SWVlxsHUXl5eXrhhRc0YsSIVs/pcDh0zz33KCYmprmsrq5OZ599tq677jp98803Ki4ultvt1tatWzVv3jxNnz5d5513nmGl8ZycHF122WUd8FsIznHHHWfYXrFihY477ji98847Kisrk9vt1qZNm/Tll1/q+uuv15QpU/T+++9LkjIzM5WWltZ8bHFxsX7++Wc1NDRo69atO+1nwK7PHuNX1uUBfcpv05b7emnLP7PkLnAZPmpYEavCf/TSlgd6GSc07H5l/3ULExrYZRErCEU09JekvWuVtJ9xJRPP5hhtuKSvKt5PNbwS0Oe2qWpustZf0k+1842vz4wZ0Kj0E80T5YF6nlEqZ6ZxkrJ2QZI2Xt5HtQsT5PduL2+qcKjs9XRt+HNfeQpjDMek/qFCCXs0tHieuCGNSv1DhaHM32BX/vV52nzv75P3O55Lkry1dtXMS1T+jbkqvKO3/A3GYVri3jVKPrjlh/UQPmLFjFhBJCA2gegTDXGL6NfobdSVX1xqKLPb7Hrq0Oc1Y/LjGpg6yPDZ2Kxxeu7wl/TvQ541rODr9Xl1yWfny+fv+IdsPlj3nmlF7IFpg/TdKYt1zojzlehKbC6PdcTq5GGn6LtTFumogdMMxywvWaaHFt/X6rlu+uY6ldaXGsr6pfTXd6cs0p373WO5kvjA1EG6ceIt+v60ZRrfay/DZ0uLf9SDP9wb1M+JXVOjt1FXfmgRY9Oe14wjH9fA9IAYyxmn5455Sf8+2iLG3uv4GHtm8ZNakD/PUBbvitcrJ8zWS8e9rgP7TVKMwzgu6ZnQU2eNOVffX7hUZ4051/BZVWOV/vLBn1s8302fXafSuoAYS+uv785bpDsn36NR2RYxlj5INx5wi76/cJnG5wbE2JYf9eA8Ygwdh+svhCoa+gzzYNsxD4adLRr+RgAA0J3Z/H6/v6sbAYRj7dq1uuWWW7Rw4cKQj7Xb7TrxxBN1zTXXKCUlpe0D/uf999/Xtddeq6amppDPmZGRoVmzZmnQoEEt7vPmm29q+vTpzdvHHnus7r77btN++fn5mjJlSvN2bm6u5s6da9rP4/Ho5JNP1s8//xxSW3v27Kmnn35aDzzwgL766ivT52eeeaZuuummkNsd7aZtbPtVXQjf1n/3VPl/Miw/c6Q3yZ7gU1Op0zSRsU3mhcXKIEGiU73bN7hLBmKlcxErkS+SYmVn9ZcVhww1lQ18ca1cvVq/ZvJW27Xh8r7yFMSYP3T45cr0yO+1qanUaVqtRJIcKV71eWijYvt6zMdbqF8Rq01X95G/0fzz2uJ8cvZokq/WLm+F9QoxccPr1efefNnjW/9/7Ku3acNf+sq9voXXijr8cqR65Uj0yVtrl7fCYfnzSVLskAb1fWBTm+cMVeVHKdpyfy9D2bBPVnboOdpCrPyOWCFW2hJJsbKzEZuh2Xxvtqo+STWUBfNz7Cq6c6xEEsYrkS3YOIn/V2THyT/2v1dXjbvW8rMttVtU7a5STmJvJcUkWe4z/etr9M/FD7R5nvorzL+vYc/018bqDa0elxabpq9OXqAh6ebvF4/Xo/yaTXLancpJ7C2n3Xw9VVJfoin/2V8ry1e02cY9s8bq/eM+U1psmuXntZ5aldQXy+11q0d8T2XEWcdncV2xDnp9b62rXNvmOdvy2znr1C+lf/P2hqr1Gv7sgHbXuzNZ/b+3En9HZMdKuP4x5V5dtU8LMVazRdWNVcpJbiXGPr1G/5wfRIz9zSLGZvTXxsqWYyw9Ll0fnvG5ZZK1JLm9bpXUFqvaXa2U2FTlJOdY7tfka9Lps0/SOyvesvx8mz17jdX7p3+mtLg0y89r3bUqqftfjCX0VEZ8CzFWW6yDnttb68rbH2ORxOr/oRWuvzoX11+RL9LGKoy1jZgH266r58EiLVa6K75XIl+wsQJY6T+Pv6HttX4fYhBdgxW9EbUGDhyoWbNm6eWXX9Zhhx2m5OTWXxkjSVlZWTrrrLP04Ycf6rbbbgspyVuSjjzySM2cOdOw0nUwhg8frueee67VJG/p9xW4V6xY0fyvpWTpvLw8w35WSd6S5HK59Nhjj2ngwIFBt3XvvffW7NmzNXz4cB122GFBHwe0V+YFJUo/1nrQ5y13ylMQYz1gtPvV81wGjOg+iBWEItL7iyPZpz735SumX6NFA23ybIlRU7HLckLb2dOjvLvzQ0qOix/WqLw7C2RP8Jo+8zfY5SmIaXHCPn5E/e/HBjF5bo/3q+8DmxQ3vN56B69N3jKn3Jti5C2zviEhScmTqzplwh5mxIoRsYJIQWwC0SfS4xa7hhu/uU4zl/zT8rNeib00JH2oZQKq1+fVzd9ODyrJuz0qGit0xJuT9UupefENl8OlAakD1Se5r2WSd0F1/v+zd9/xUdT5H8ff25LspicQSkIHQUCqAoqKiJ6eBYVDz3LceWc5PcXTU8/+sxx39lPPenbP7tkrVhRFQZGuKL2EEkJ622Tb7w/OwGQ3yWyySWbJ6/l48Hg43535znfjfOY7853Pfkcnvv4LU0nekrRk52Id9d/DtGTn4oifJ7uS1SetrwZl7tdokveqoh9iluSNfcM1n/xV9y+8J+Jn3VO6a1B2EzH26dWmkrxbqsRbomOfmaxXvn8p4ucJjgT1TMvV4C5DGk3yLvOWafqLJzSb5C1JS3Ys1lFPH6Yl2xuJsYRk9cnoq0HZ+zWa5L2q8Id9Mskb1sH1F6Jl9WOGcTDGwdCxrH6OAACgs4p8RQrEkbFjx2rs2LEKBAL6/vvvtXbtWpWUlKisrEw2m02pqanq2bOn9t9/f/Xr1/qZQyZPnqwPPvhA999/v95++22VlpY2uu7gwYN1yimn6IwzzpDD0THvpunWrZv++9//6u6779bLL7+surq6iOuNGDFC55xzjiG5e9q0aZozZ46++OKLJvcxffp0TZ8+PabtRudjs0k5fyqU+4Aa7Xy4q/w7Xc1uk9C7VjkXFip5THU7tBCwBmIF0YiH48XV1a8+923Wrie7qPSddIV8zfwW1RZS6uQK5ZxfKGdm+OB7czwja9T335tUcF+Oqr6JPPuYYXdJQWWdUqLsM4pki+LuyZEWVK8781XyRoZKXs5SoNz8tWDSkBplnVqi1MMqm18ZMUGshCNWYAXEJhB/4iFusW+4Yt6lmr/tC9122D/VO61Ps+uvKvp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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -462,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 11, "id": "5b04fc35", "metadata": {}, "outputs": [], @@ -498,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 12, "id": "4735430a", "metadata": {}, "outputs": [], @@ -530,20 +531,31 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 13, "id": "7ef42dc5", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4f/nzw4pyy13p90pvc1lkbpbjpc0000gn/T/ipykernel_39325/2556837833.py:5: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " ax = sns.violinplot(data=df_exploded, x=\"model\", y=\"f1score\", cut=0, inner=\"point\", palette=shades_of_green)\n", + "/var/folders/4f/nzw4pyy13p90pvc1lkbpbjpc0000gn/T/ipykernel_39325/2556837833.py:5: UserWarning: The palette list has more values (17) than needed (5), which may not be intended.\n", + " ax = sns.violinplot(data=df_exploded, x=\"model\", y=\"f1score\", cut=0, inner=\"point\", palette=shades_of_green)\n" + ] + }, { "data": { - "image/png": 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\n", + "image/png": 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ToxinFinder (RNN)2.00.71180.70570.71170.72790.416571759.6
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" - ], - "text/plain": [ - " kfold_iteration accuracy f1score \\\n", - "model \n", - "Baseline Model - Logistic Regression 2.0 0.6490 0.6320 \n", - "PVP-SVM (SVM) 2.0 0.7214 0.7009 \n", - "ToxinFinder (FFNN) 2.0 0.7230 0.7162 \n", - "ToxinFinder (RNN) 2.0 0.7118 0.7057 \n", - "\n", - " precision recall \\\n", - "model \n", - "Baseline Model - Logistic Regression 0.6489 0.6706 \n", - "PVP-SVM (SVM) 0.7215 0.7831 \n", - "ToxinFinder (FFNN) 0.7231 0.7437 \n", - "ToxinFinder (RNN) 0.7117 0.7279 \n", - "\n", - " execution_time_seconds dataset_size \n", - "model \n", - "Baseline Model - Logistic Regression 0.001099 759.6 \n", - "PVP-SVM (SVM) 1.150334 759.6 \n", - "ToxinFinder (FFNN) 0.142976 759.6 \n", - "ToxinFinder (RNN) 0.416571 759.6 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "ename": "TypeError", + "evalue": "agg function failed [how->mean,dtype->object]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1942\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[0;34m(self, how, values, ndim, alt)\u001b[0m\n\u001b[1;32m 1941\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1942\u001b[0m res_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_grouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43mser\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreserve_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1943\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/groupby/ops.py:864\u001b[0m, in \u001b[0;36mBaseGrouper.agg_series\u001b[0;34m(self, obj, func, preserve_dtype)\u001b[0m\n\u001b[1;32m 862\u001b[0m preserve_dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 864\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_aggregate_series_pure_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 866\u001b[0m npvalues \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mmaybe_convert_objects(result, try_float\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/groupby/ops.py:885\u001b[0m, in \u001b[0;36mBaseGrouper._aggregate_series_pure_python\u001b[0;34m(self, obj, func)\u001b[0m\n\u001b[1;32m 884\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(splitter):\n\u001b[0;32m--> 885\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 886\u001b[0m res \u001b[38;5;241m=\u001b[39m extract_result(res)\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:2454\u001b[0m, in \u001b[0;36mGroupBy.mean..\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 2451\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2452\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[1;32m 2453\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m-> 2454\u001b[0m alt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 2455\u001b[0m numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[1;32m 2456\u001b[0m )\n\u001b[1;32m 2457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/series.py:6549\u001b[0m, in \u001b[0;36mSeries.mean\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 6541\u001b[0m \u001b[38;5;129m@doc\u001b[39m(make_doc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m 6542\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[1;32m 6543\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6547\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 6548\u001b[0m ):\n\u001b[0;32m-> 6549\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/generic.py:12420\u001b[0m, in \u001b[0;36mNDFrame.mean\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 12413\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[1;32m 12414\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 12415\u001b[0m axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12418\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 12419\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m> 12420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 12421\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 12422\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/generic.py:12377\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[0;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 12375\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m> 12377\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 12378\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[1;32m 12379\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/series.py:6457\u001b[0m, in \u001b[0;36mSeries._reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m 6453\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 6454\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not allow \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwd_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnumeric_only\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 6455\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith non-numeric dtypes.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 6456\u001b[0m )\n\u001b[0;32m-> 6457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelegate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/nanops.py:720\u001b[0m, in \u001b[0;36mnanmean\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 719\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39msum(axis, dtype\u001b[38;5;241m=\u001b[39mdtype_sum)\n\u001b[0;32m--> 720\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m \u001b[43m_ensure_numeric\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthe_sum\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 722\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(the_sum, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/nanops.py:1701\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1699\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 1700\u001b[0m \u001b[38;5;66;03m# GH#44008, GH#36703 avoid casting e.g. strings to numeric\u001b[39;00m\n\u001b[0;32m-> 1701\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not convert string \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1702\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[0;31mTypeError\u001b[0m: Could not convert string 'PROTEINSEQPROTEINSEQPROTEINSEQPROTEINSEQPROTEINSEQPROTEINSEQPROTEINSEQPROTEINSEQPROTEINSEQPROTEINSEQ' to numeric", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m mean_f1_per_model \u001b[38;5;241m=\u001b[39m \u001b[43mdf_exploded\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m mean_f1_per_model\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:2452\u001b[0m, in \u001b[0;36mGroupBy.mean\u001b[0;34m(self, numeric_only, engine, engine_kwargs)\u001b[0m\n\u001b[1;32m 2445\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_numba_agg_general(\n\u001b[1;32m 2446\u001b[0m grouped_mean,\n\u001b[1;32m 2447\u001b[0m executor\u001b[38;5;241m.\u001b[39mfloat_dtype_mapping,\n\u001b[1;32m 2448\u001b[0m engine_kwargs,\n\u001b[1;32m 2449\u001b[0m min_periods\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 2450\u001b[0m )\n\u001b[1;32m 2451\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2452\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cython_agg_general\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2453\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2454\u001b[0m \u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2455\u001b[0m \u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2456\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1998\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m 1995\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_py_fallback(how, values, ndim\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim, alt\u001b[38;5;241m=\u001b[39malt)\n\u001b[1;32m 1996\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 1998\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouped_reduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1999\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_agged_manager(new_mgr)\n\u001b[1;32m 2000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmax\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/internals/managers.py:1469\u001b[0m, in \u001b[0;36mBlockManager.grouped_reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m 1465\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mis_object:\n\u001b[1;32m 1466\u001b[0m \u001b[38;5;66;03m# split on object-dtype blocks bc some columns may raise\u001b[39;00m\n\u001b[1;32m 1467\u001b[0m \u001b[38;5;66;03m# while others do not.\u001b[39;00m\n\u001b[1;32m 1468\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sb \u001b[38;5;129;01min\u001b[39;00m blk\u001b[38;5;241m.\u001b[39m_split():\n\u001b[0;32m-> 1469\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[43msb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1470\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m 1471\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/internals/blocks.py:393\u001b[0m, in \u001b[0;36mBlock.apply\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[1;32m 389\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 390\u001b[0m \u001b[38;5;124;03m apply the function to my values; return a block if we are not\u001b[39;00m\n\u001b[1;32m 391\u001b[0m \u001b[38;5;124;03m one\u001b[39;00m\n\u001b[1;32m 392\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 393\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 395\u001b[0m result \u001b[38;5;241m=\u001b[39m maybe_coerce_values(result)\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_split_op_result(result)\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1995\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general..array_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 1992\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m 1994\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m alt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1995\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_agg_py_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mndim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1996\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/pandas/core/groupby/groupby.py:1946\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[0;34m(self, how, values, ndim, alt)\u001b[0m\n\u001b[1;32m 1944\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magg function failed [how->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,dtype->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mser\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1945\u001b[0m \u001b[38;5;66;03m# preserve the kind of exception that raised\u001b[39;00m\n\u001b[0;32m-> 1946\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(err)(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 1948\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ser\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m 1949\u001b[0m res_values \u001b[38;5;241m=\u001b[39m res_values\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "\u001b[0;31mTypeError\u001b[0m: agg function failed [how->mean,dtype->object]" + ] } ], "source": [ @@ -1627,10 +1559,110 @@ "plt.show()\n" ] }, + { + "cell_type": "markdown", + "id": "9c29e087", + "metadata": {}, + "source": [ + "# Toxin database analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "97063174", + "metadata": {}, + "outputs": [], + "source": [ + "def get_go_names(filepath: Path):\n", + " \"\"\"Returns a set of all of the GO names (biological functions). \"\"\"\n", + " gotermCounter = Counter()\n", + " with open(filepath, 'r') as file:\n", + " lines = file.readlines()\n", + " for line in lines:\n", + " go_terms = line.split('\\t')[2]\n", + " if len(go_terms) < 1: continue\n", + " for i in go_terms.split(';'):\n", + " gotermCounter[i] += 1\n", + " return gotermCounter" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "176bb043", + "metadata": {}, + "outputs": [], + "source": [ + "all_toxins = [(k, v) for k,v in get_go_names(FilePaths.toxin_uniprot_info).items()]\n", + "all_toxins = sorted(all_toxins, key=lambda a: a[1], reverse=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "5820debb", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "'explode' must be of length 'x'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[79], line 16\u001b[0m\n\u001b[1;32m 13\u001b[0m explode \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.01\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mlen\u001b[39m(toxin2count)\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# plotting data on chart \u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpie\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshades_of_green\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mexplode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43mautopct\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m%.0f\u001b[39;49;00m\u001b[38;5;132;43;01m%%\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# displaying chart \u001b[39;00m\n\u001b[1;32m 23\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/matplotlib/pyplot.py:3676\u001b[0m, in \u001b[0;36mpie\u001b[0;34m(x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels, normalize, hatch, data)\u001b[0m\n\u001b[1;32m 3653\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mpie)\n\u001b[1;32m 3654\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpie\u001b[39m(\n\u001b[1;32m 3655\u001b[0m x: ArrayLike,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3674\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 3675\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mlist\u001b[39m[Wedge], \u001b[38;5;28mlist\u001b[39m[Text]] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mlist\u001b[39m[Wedge], \u001b[38;5;28mlist\u001b[39m[Text], \u001b[38;5;28mlist\u001b[39m[Text]]:\n\u001b[0;32m-> 3676\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpie\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3677\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3678\u001b[0m \u001b[43m \u001b[49m\u001b[43mexplode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3679\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3680\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3681\u001b[0m \u001b[43m \u001b[49m\u001b[43mautopct\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautopct\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3682\u001b[0m \u001b[43m \u001b[49m\u001b[43mpctdistance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpctdistance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3683\u001b[0m \u001b[43m \u001b[49m\u001b[43mshadow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshadow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3684\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabeldistance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabeldistance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3685\u001b[0m \u001b[43m \u001b[49m\u001b[43mstartangle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstartangle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3686\u001b[0m \u001b[43m \u001b[49m\u001b[43mradius\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mradius\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3687\u001b[0m \u001b[43m \u001b[49m\u001b[43mcounterclock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcounterclock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3688\u001b[0m \u001b[43m \u001b[49m\u001b[43mwedgeprops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwedgeprops\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3689\u001b[0m \u001b[43m \u001b[49m\u001b[43mtextprops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtextprops\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3690\u001b[0m \u001b[43m \u001b[49m\u001b[43mcenter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcenter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3691\u001b[0m \u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3692\u001b[0m \u001b[43m \u001b[49m\u001b[43mrotatelabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrotatelabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3693\u001b[0m \u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3694\u001b[0m \u001b[43m \u001b[49m\u001b[43mhatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3695\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3696\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/matplotlib/__init__.py:1473\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1470\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1471\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1472\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1474\u001b[0m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1475\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1476\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1478\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1479\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1480\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", + "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/matplotlib/axes/_axes.py:3300\u001b[0m, in \u001b[0;36mAxes.pie\u001b[0;34m(self, x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels, normalize, hatch)\u001b[0m\n\u001b[1;32m 3298\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be of length \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(x) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(explode):\n\u001b[0;32m-> 3300\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexplode\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be of length \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3301\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m colors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3302\u001b[0m get_next_color \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_patches_for_fill\u001b[38;5;241m.\u001b[39mget_next_color\n", + "\u001b[0;31mValueError\u001b[0m: 'explode' must be of length 'x'" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# parameters\n", + "max_goterms_to_plot = 10\n", + "\n", + "# declaring data\n", + "data = []\n", + "keys = []\n", + "for i, (k, v) in enumerate(all_toxins[1:]):\n", + " if i > max_goterms_to_plot: break\n", + " data.append(v)\n", + " keys.append(k)\n", + "\n", + "# declaring exploding pie \n", + "explode = [0.01]*len(toxin2count)\n", + "\n", + "# plotting data on chart \n", + "plt.pie(data,\n", + " labels=keys,\n", + " colors=shades_of_green, \n", + " explode=explode, \n", + " autopct='%.0f%%') \n", + "\n", + "# displaying chart \n", + "plt.show()\n", + "\n", + "plt.savefig('toxin_piechart.png', dpi=300, bbox_inches='tight')" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "74fc194e", + "id": "95108df7", "metadata": {}, "outputs": [], "source": [] @@ -1652,7 +1684,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/results/uniprot_toxin_info.tsv b/results/uniprot_toxin_info.tsv new file mode 100644 index 0000000..846df13 --- /dev/null +++ b/results/uniprot_toxin_info.tsv @@ -0,0 +1,837 @@ +Entry Reviewed Gene Ontology (biological process) Organism Entry Name Sequence +A0A193CHJ5 reviewed arachidonic acid secretion [GO:0050482]; lipid catabolic process [GO:0016042]; negative regulation of T cell proliferation [GO:0042130]; phospholipid metabolic process [GO:0006644] Crotalus tzabcan (Yucatan neotropical rattlesnake) (Crotalus simus tzabcan) PA2BC_CROTA MRALWIVAVLLVGVEGHLLQFNKMIKFETRKNAIPFYAFYGCYCGWGGRGRPKDATDRCCFVHDCCYGKLAKCNTKWDIYPYSLKSGYITCGKGTWCEEQICECDRVAAECLRRSLSTYKYGYMFYPDSRCRGPSETC +A7GBG3 reviewed proteolysis [GO:0006508] Clostridium botulinum (strain Langeland / NCTC 10281 / Type F) BXF_CLOBL MPVVINSFNYNDPVNDDTILYMQIPYEEKSKKYYKAFEIMRNVWIIPERNTIGTDPSDFDPPASLENGSSAYYDPNYLTTDAEKDRYLKTTIKLFKRINSNPAGEVLLQEISYAKPYLGNEHTPINEFHPVTRTTSVNIKSSTNVKSSIILNLLVLGAGPDIFENSSYPVRKLMDSGGVYDPSNDGFGSINIVTFSPEYEYTFNDISGGYNSSTESFIADPAISLAHELIHALHGLYGARGVTYKETIKVKQAPLMIAEKPIRLEEFLTFGGQDLNIITSAMKEKIYNNLLANYEKIATRLSRVNSAPPEYDINEYKDYFQWKYGLDKNADGSYTVNENKFNEIYKKLYSFTEIDLANKFKVKCRNTYFIKYGFLKVPNLLDDDIYTVSEGFNIGNLAVNNRGQNIKLNPKIIDSIPDKGLVEKIVKFCKSVIPRKGTKAPPRLCIRVNNRELFFVASESSYNENDINTPKEIDDTTNLNNNYRNNLDEVILDYNSETIPQISNQTLNTLVQDDSYVPRYDSNGTSEIEEHNVVDLNVFFYLHAQKVPEGETNISLTSSIDTALSEESQVYTFFSSEFINTINKPVHAALFISWINQVIRDFTTEATQKSTFDKIADISLVVPYVGLALNIGNEVQKENFKEAFELLGAGILLEFVPELLIPTILVFTIKSFIGSSENKNKIIKAINNSLMERETKWKEIYSWIVSNWLTRINTQFNKRKEQMYQALQNQVDAIKTVIEYKYNNYTSDERNRLESEYNINNIREELNKKVSLAMENIERFITESSIFYLMKLINEAKVSKLREYDEGVKEYLLDYISEHRSILGNSVQELNDLVTSTLNNSIPFELSSYTNDKILILYFNKLYKKIKDNSILDMRYENNKFIDISGYGSNISINGDVYIYSTNRNQFGIYSSKPSEVNIAQNNDIIYNGRYQNFSISFWVRIPKYFNKVNLNNEYTIIDCIRNNNSGWKISLNYNKIIWTLQDTAGNNQKLVFNYTQMISISDYINKWIFVTITNNRLGNSRIYINGNLIDEKSISNLGDIHVSDNILFKIVGCNDTRYVGIRYFKVFDTELGKTEIETLYSDEPDPSILKDFWGNYLLYNKRYYLLNLLRTDKSITQNSNFLNINQQRGVYQKPNIFSNTRLYTGVEVIIRKNGSTDISNTDNFVRKNDLAYINVVDRDVEYRLYADISIAKPEKIIKLIRTSNSNNSLGQIIVMDSIGNNCTMNFQNNNGGNIGLLGFHSNNLVASSWYYNNIRKNTSSNGCFWSFISKEHGWQEN +B1INP5 reviewed proteolysis [GO:0006508] Clostridium botulinum (strain Okra / Type B1) BXB_CLOBK MPVTINNFNYNDPIDNNNIIMMEPPFARGTGRYYKAFKITDRIWIIPERYTFGYKPEDFNKSSGIFNRDVCEYYDPDYLNTNDKKNIFLQTMIKLFNRIKSKPLGEKLLEMIINGIPYLGDRRVPLEEFNTNIASVTVNKLISNPGEVERKKGIFANLIIFGPGPVLNENETIDIGIQNHFASREGFGGIMQMKFCPEYVSVFNNVQENKGASIFNRRGYFSDPALILMHELIHVLHGLYGIKVDDLPIVPNEKKFFMQSTDAIQAEELYTFGGQDPSIITPSTDKSIYDKVLQNFRGIVDRLNKVLVCISDPNININIYKNKFKDKYKFVEDSEGKYSIDVESFDKLYKSLMFGFTETNIAENYKIKTRASYFSDSLPPVKIKNLLDNEIYTIEEGFNISDKDMEKEYRGQNKAINKQAYEEISKEHLAVYKIQMCKSVKAPGICIDVDNEDLFFIADKNSFSDDLSKNERIEYNTQSNYIENDFPINELILDTDLISKIELPSENTESLTDFNVDVPVYEKQPAIKKIFTDENTIFQYLYSQTFPLDIRDISLTSSFDDALLFSNKVYSFFSMDYIKTANKVVEAGLFAGWVKQIVNDFVIEANKSNTMDKIADISLIVPYIGLALNVGNETAKGNFENAFEIAGASILLEFIPELLIPVVGAFLLESYIDNKNKIIKTIDNALTKRNEKWSDMYGLIVAQWLSTVNTQFYTIKEGMYKALNYQAQALEEIIKYRYNIYSEKEKSNINIDFNDINSKLNEGINQAIDNINNFINGCSVSYLMKKMIPLAVEKLLDFDNTLKKNLLNYIDENKLYLIGSAEYEKSKVNKYLKTIMPFDLSIYTNDTILIEMFNKYNSEILNNIILNLRYKDNNLIDLSGYGAKVEVYDGVELNDKNQFKLTSSANSKIRVTQNQNIIFNSVFLDFSVSFWIRIPKYKNDGIQNYIHNEYTIINCMKNNSGWKISIRGNRIIWTLIDINGKTKSVFFEYNIREDISEYINRWFFVTITNNLNNAKIYINGKLESNTDIKDIREVIANGEIIFKLDGDIDRTQFIWMKYFSIFNTELSQSNIEERYKIQSYSEYLKDFWGNPLMYNKEYYMFNAGNKNSYIKLKKDSPVGEILTRSKYNQNSKYINYRDLYIGEKFIIRRKSNSQSINDDIVRKEDYIYLDFFNLNQEWRVYTYKYFKKEEEKLFLAPISDSDEFYNTIQIKEYDEQPTYSCQLLFKKDEESTDEIGLIGIHRFYESGIVFEEYKDYFCISKWYLKEVKRKPYNLKLGCNWQFIPKDEGWTE +C0HM14 reviewed arachidonic acid secretion [GO:0050482]; lipid catabolic process [GO:0016042]; negative regulation of T cell proliferation [GO:0042130]; phospholipid metabolic process [GO:0006644] Crotalus durissus terrificus (South American rattlesnake) PA2BD_CRODU HLLQFNKMIKFETRKNAIPFYAFYGCYCGWGGRGRPKDATDRCCFVHDCCYGKLAKCNTKWDIYRYSLKSGYITCGKGTWCEEQICECDRVAAECLRRSLSTYKYGYMFYPDSRCRGPSETC +P00588 reviewed Corynephage beta DTX_CORBE MLVRGYVVSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +P01391 reviewed Naja kaouthia (Monocled cobra) (Naja siamensis) 3L21_NAJKA IRCFITPDITSKDCPNGHVCYTKTWCDAFCSIRGKRVDLGCAATCPTVKTGVDIQCCSTDNCNPFPTRKRP +P08878 reviewed arachidonic acid secretion [GO:0050482]; lipid catabolic process [GO:0016042]; negative regulation of T cell proliferation [GO:0042130]; phospholipid metabolic process [GO:0006644] Crotalus durissus terrificus (South American rattlesnake) PA2H_CRODU MRALWIVAVLLVGVEGSLVEFETLMMKIAGRSGISYYSSYGCYCGAGGQGWPQDASDRCCFEHDCCYAKLTGCDPTTDVYTYRQEDGEIVCGEDDPCGTQICECDKAAAICFRNSMDTYDYKYLQFSPENCQGESQPC +P09385 reviewed modulation of host virulence by virus [GO:0098676]; negative regulation of translation [GO:0017148] Escherichia phage 933W (Bacteriophage 933W) STXA_BP933 MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +P0A0L2 reviewed Staphylococcus aureus ETXA_STAAU MKKTAFTLLLFIALTLTTSPLVNGSEKSEEINEKDLRKKSELQGTALGNLKQIYYYNEKAKTENKESHDQFLQHTILFKGFFTDHSWYNDLLVDFDSKDIVDKYKGKKVDLYGAYYGYQCAGGTPNKTACMYGGVTLHDNNRLTEEKKVPINLWLDGKQNTVPLETVKTNKKNVTVQELDLQARRYLQEKYNLYNSDVFDGKVQRGLIVFHTSTEPSVNYDLFGAQGQYSNTLLRIYRDNKTINSENMHIDIYLYTS +P0CG56 reviewed arachidonic acid secretion [GO:0050482]; lipid catabolic process [GO:0016042]; negative regulation of T cell proliferation [GO:0042130]; phospholipid metabolic process [GO:0006644] Crotalus durissus terrificus (South American rattlesnake) PA2BB_CRODU HLLQFNKMIKFETRKNAVPFYAFYGCYCGWGGQGRPKDATDRCCFVHDCCYGKLAKCNTKWDIYRYSLKSGYITCGKGTWCEEQICECDRVAAECLRRSLSTYKNGYMFYPDSRCRGPSETC +P0DPI0 reviewed proteolysis [GO:0006508] Clostridium botulinum BXA1_CLOBO MPFVNKQFNYKDPVNGVDIAYIKIPNVGQMQPVKAFKIHNKIWVIPERDTFTNPEEGDLNPPPEAKQVPVSYYDSTYLSTDNEKDNYLKGVTKLFERIYSTDLGRMLLTSIVRGIPFWGGSTIDTELKVIDTNCINVIQPDGSYRSEELNLVIIGPSADIIQFECKSFGHEVLNLTRNGYGSTQYIRFSPDFTFGFEESLEVDTNPLLGAGKFATDPAVTLAHELIHAGHRLYGIAINPNRVFKVNTNAYYEMSGLEVSFEELRTFGGHDAKFIDSLQENEFRLYYYNKFKDIASTLNKAKSIVGTTASLQYMKNVFKEKYLLSEDTSGKFSVDKLKFDKLYKMLTEIYTEDNFVKFFKVLNRKTYLNFDKAVFKINIVPKVNYTIYDGFNLRNTNLAANFNGQNTEINNMNFTKLKNFTGLFEFYKLLCVRGIITSKTKSLDKGYNKALNDLCIKVNNWDLFFSPSEDNFTNDLNKGEEITSDTNIEAAEENISLDLIQQYYLTFNFDNEPENISIENLSSDIIGQLELMPNIERFPNGKKYELDKYTMFHYLRAQEFEHGKSRIALTNSVNEALLNPSRVYTFFSSDYVKKVNKATEAAMFLGWVEQLVYDFTDETSEVSTTDKIADITIIIPYIGPALNIGNMLYKDDFVGALIFSGAVILLEFIPEIAIPVLGTFALVSYIANKVLTVQTIDNALSKRNEKWDEVYKYIVTNWLAKVNTQIDLIRKKMKEALENQAEATKAIINYQYNQYTEEEKNNINFNIDDLSSKLNESINKAMININKFLNQCSVSYLMNSMIPYGVKRLEDFDASLKDALLKYIYDNRGTLIGQVDRLKDKVNNTLSTDIPFQLSKYVDNQRLLSTFTEYIKNIINTSILNLRYESNHLIDLSRYASKINIGSKVNFDPIDKNQIQLFNLESSKIEVILKNAIVYNSMYENFSTSFWIRIPKYFNSISLNNEYTIINCMENNSGWKVSLNYGEIIWTLQDTQEIKQRVVFKYSQMINISDYINRWIFVTITNNRLNNSKIYINGRLIDQKPISNLGNIHASNNIMFKLDGCRDTHRYIWIKYFNLFDKELNEKEIKDLYDNQSNSGILKDFWGDYLQYDKPYYMLNLYDPNKYVDVNNVGIRGYMYLKGPRGSVMTTNIYLNSSLYRGTKFIIKKYASGNKDNIVRNNDRVYINVVVKNKEYRLATNASQAGVEKILSALEIPDVGNLSQVVVMKSKNDQGITNKCKMNLQDNNGNDIGFIGFHQFNNIAKLVASNWYNRQIERSSRTLGCSWEFIPVDDGWGERPL +P0DPI1 reviewed proteolysis [GO:0006508] Clostridium botulinum (strain Hall / ATCC 3502 / NCTC 13319 / Type A) BXA1_CLOBH MPFVNKQFNYKDPVNGVDIAYIKIPNAGQMQPVKAFKIHNKIWVIPERDTFTNPEEGDLNPPPEAKQVPVSYYDSTYLSTDNEKDNYLKGVTKLFERIYSTDLGRMLLTSIVRGIPFWGGSTIDTELKVIDTNCINVIQPDGSYRSEELNLVIIGPSADIIQFECKSFGHEVLNLTRNGYGSTQYIRFSPDFTFGFEESLEVDTNPLLGAGKFATDPAVTLAHELIHAGHRLYGIAINPNRVFKVNTNAYYEMSGLEVSFEELRTFGGHDAKFIDSLQENEFRLYYYNKFKDIASTLNKAKSIVGTTASLQYMKNVFKEKYLLSEDTSGKFSVDKLKFDKLYKMLTEIYTEDNFVKFFKVLNRKTYLNFDKAVFKINIVPKVNYTIYDGFNLRNTNLAANFNGQNTEINNMNFTKLKNFTGLFEFYKLLCVRGIITSKTKSLDKGYNKALNDLCIKVNNWDLFFSPSEDNFTNDLNKGEEITSDTNIEAAEENISLDLIQQYYLTFNFDNEPENISIENLSSDIIGQLELMPNIERFPNGKKYELDKYTMFHYLRAQEFEHGKSRIALTNSVNEALLNPSRVYTFFSSDYVKKVNKATEAAMFLGWVEQLVYDFTDETSEVSTTDKIADITIIIPYIGPALNIGNMLYKDDFVGALIFSGAVILLEFIPEIAIPVLGTFALVSYIANKVLTVQTIDNALSKRNEKWDEVYKYIVTNWLAKVNTQIDLIRKKMKEALENQAEATKAIINYQYNQYTEEEKNNINFNIDDLSSKLNESINKAMININKFLNQCSVSYLMNSMIPYGVKRLEDFDASLKDALLKYIYDNRGTLIGQVDRLKDKVNNTLSTDIPFQLSKYVDNQRLLSTFTEYIKNIINTSILNLRYESNHLIDLSRYASKINIGSKVNFDPIDKNQIQLFNLESSKIEVILKNAIVYNSMYENFSTSFWIRIPKYFNSISLNNEYTIINCMENNSGWKVSLNYGEIIWTLQDTQEIKQRVVFKYSQMINISDYINRWIFVTITNNRLNNSKIYINGRLIDQKPISNLGNIHASNNIMFKLDGCRDTHRYIWIKYFNLFDKELNEKEIKDLYDNQSNSGILKDFWGDYLQYDKPYYMLNLYDPNKYVDVNNVGIRGYMYLKGPRGSVMTTNIYLNSSLYRGTKFIIKKYASGNKDNIVRNNDRVYINVVVKNKEYRLATNASQAGVEKILSALEIPDVGNLSQVVVMKSKNDQGITNKCKMNLQDNNGNDIGFIGFHQFNNIAKLVASNWYNRQIERSSRTLGCSWEFIPVDDGWGERPL +P0DPK1 reviewed proteolysis [GO:0006508] Clostridium botulinum BXX_CLOBO MKLEINKFNYNDPIDGINVITMRPPRHSDKINKGKGPFKAFQVIKNIWIVPERYNFTNNTNDLNIPSEPIMEADAIYNPNYLNTPSEKDEFLQGVIKVLERIKSKPEGEKLLELISSSIPLPLVSNGALTLSDNETIAYQENNNIVSNLQANLVIYGPGPDIANNATYGLYSTPISNGEGTLSEVSFSPFYLKPFDESYGNYRSLVNIVNKFVKREFAPDPASTLMHELVHVTHNLYGISNRNFYYNFDTGKIETSRQQNSLIFEELLTFGGIDSKAISSLIIKKIIETAKNNYTTLISERLNTVTVENDLLKYIKNKIPVQGRLGNFKLDTAEFEKKLNTILFVLNESNLAQRFSILVRKHYLKERPIDPIYVNILDDNSYSTLEGFNISSQGSNDFQGQLLESSYFEKIESNALRAFIKICPRNGLLYNAIYRNSKNYLNNIDLEDKKTTSKTNVSYPCSLLNGCIEVENKDLFLISNKDSLNDINLSEEKIKPETTVFFKDKLPPQDITLSNYDFTEANSIPSISQQNILERNEELYEPIRNSLFEIKTIYVDKLTTFHFLEAQNIDESIDSSKIRVELTDSVDEALSNPNKVYSPFKNMSNTINSIETGITSTYIFYQWLRSIVKDFSDETGKIDVIDKSSDTLAIVPYIGPLLNIGNDIRHGDFVGAIELAGITALLEYVPEFTIPILVGLEVIGGELAREQVEAIVNNALDKRDQKWAEVYNITKAQWWGTIHLQINTRLAHTYKALSRQANAIKMNMEFQLANYKGNIDDKAKIKNAISETEILLNKSVEQAMKNTEKFMIKLSNSYLTKEMIPKVQDNLKNFDLETKKTLDKFIKEKEDILGTNLSSSLRRKVSIRLNKNIAFDINDIPFSEFDDLINQYKNEIEDYEVLNLGAEDGKIKDLSGTTSDINIGSDIELADGRENKAIKIKGSENSTIKIAMNKYLRFSATDNFSISFWIKHPKPTNLLNNGIEYTLVENFNQRGWKISIQDSKLIWYLRDHNNSIKIVTPDYIAFNGWNLITITNNRSKGSIVYVNGSKIEEKDISSIWNTEVDDPIIFRLKNNRDTQAFTLLDQFSIYRKELNQNEVVKLYNYYFNSNYIRDIWGNPLQYNKKYYLQTQDKPGKGLIREYWSSFGYDYVILSDSKTITFPNNIRYGALYNGSKVLIKNSKKLDGLVRNKDFIQLEIDGYNMGISADRFNEDTNYIGTTYGTTHDLTTDFEIIQRQEKYRNYCQLKTPYNIFHKSGLMSTETSKPTFHDYRDWVYSSAWYFQNYENLNLRKHTKTNWYFIPKDEGWDED +P0DUB4 reviewed proteolysis [GO:0006508] Paraclostridium sordellii (Clostridium sordellii) TCSL2_PARSO MSLVNKAQLQKMAYVKFRIQEDEYVAILNALEEYHNMSESSVVEKYLKLKDINNLTDNYLNTYKKSGRNKALKKFKEYLTMEVLELKNNSLTPVEKNLHFIWIGGQINDTAINYINQWKDVNSDYTVKVFYDSNAFLINTLKKTIVESATNNTLESFRENLNDPEFDYNKFYRKRMEIIYDKQKHFIDYYKSQIEENPEFIIDNIIKTYLSNEYSKDLEALNKYIEESLNKITANNGNDIRNLEKFADEDLVRLYNQELVERWNLAAASDILRISMLKEDGGVYLDVDMLPGIQPDLFKSINKPDSITDTSWEMIKLEAIMKYKEYIPGYTSKNFDMLDEEVQSSFESALSSTSDKSEIFLPLDDIKVSPLEVKIAFANNSVINQALISLKDSYGSDLVISQIKNRYKILNDNLNPAINEGNDFNTTMKTFNDNLVSISNEDNIMFMIKIADYLKVGFAPDVRSTINLSGPGVYTGAYQDLLMFKDNSINIHLLEPELRNFEFPKTKISQLTEQEITSLWSFNQARAKSQFEEYKKGYFEGALGEDDILDFSQNTVLDKDYVVEKISSSMRTPNKEYVHYIVQLQGDNVSYEAACNLFAKNPYYNILFQKNIENSETAYYYNLIYNKLQEIDKYRIPNLISNRHKIKLTFIGHGKSEFNTDTFANLDVNSLSSEIETILNLAKEDISPKSIEINLLGCNMFSYNVNVEETYPGKLLLKIKDIVSKLMPSISQDSITVSANQYEVRINKEGRRELLDHSGKWINKEESIIKDISSKEYISFNPKENKIIVKSKNLHELSTLLQEIKNNSNSSDIDLEKKVMLTECEINVASNIDTQIVEERIEEAKNLTSDSINYIKNEFKLIESISDALYDLKHQNGLDDSHFISFEDISKTENGFRIRFINKETGNSIFIETEKEIFSEYAAHISKEISNIKDTIFDNVNGKLVKKVNLDAAHEVNTLNSAFFIQSLIEYNTTKESLSNLSVAMKVQVYAQLFSTGLNTITDASKVVELVSTALDETIDLLPTLSEGLPIIATIIDGVSLGAAIKELSETNDPLLRQEIEAKIGIMAVNLTAASTAIVTSALGIASGFSILLVPLAGISAGIPSLVNNELILQDKATKVIDYFKHISLAETEGAFTLLDDKIIMPQDDLVLSEIDFNNNSITLGKCEIWRTEGGSGHTFTDDIDHFFSSPSITYRKPWLSIYDVLNIKKEKIDFSKDLMVLPNAPNRVFSYEMGWTPGFRSLDNDGTKLLDRIRDHYEGQFYWRYFAFIADALITKLKPRYEDTNVRINLDGNTRSFIVPVITTEQIRKNLSYSFYGSGGSYSLSLSPYNMNIDLNLVENDTWVIDVDNVVKNITIESDEIQKGELIENILSKLNIEDNKIVLNNHTINFYGAINESNRFISLTFSILEDINIIIEIDLVSKSYKILLSGNCIKLIENSSDIQQKIDHIGFNGEHQKYIPYSYIDNETKYNGFIDYSKKEGLFTAEFSNESIIRNIYMPDSNNLFIYSSKDLKDIRIINKGDVKLLIGNYFKDNMKVSLSFTIEDTNTIKLNGVYLDENGVAQILKFMNNAKSALNTSNSLMNFLESINIKNIFYNNLDPNIKFILDTNFIISGSNSIGQFELICDKDKNIQPYFIKFKIKETSYTLYAGNRQNLIVEPSYHLDDSGNISSTVINFSQKYLYGIDRYVNKVIITPNLYTDEINITPVYKPNYICPEVIILDANYINEKINININDLSIRYVWDNDGSDLILIANSEEDNQPQVKIRFVNVFKSDTAADKLSFNFSDKQDVSVSKIISTFSLAAYSDGVFDYEFGLVSLDNECFYINSFGNMVSGLIYINDSLYYFKPPKNNLITGFTTIDDNKYYFDPTKSGAASIGEITIDGKDYYFNKQGILQVGVINTSDGLKYFAPAGTLDENLEGESVNFIGKLNIDGKIYYFEDNYRAAVEWKSLDGETYYFNPKTGEALKGLHQIGDNKYYFDNNGIMQTGFITINDKVFYFNNDGVMQVGYIEVNGKYFYFGKNGERQLGVFNTPDGFKFFGPKDDDLGTEEGELTLYNGILNFNGKIYFFDISNTAVVGWGILDDGSTYYFDDNTAEACIGLTVINDCKYYFDDNGIRQLGFITINDNIFYFSESGKIELGYQNINGNYFYIDESGLVLIGVFDTPDGYKYFAPLNTVNDNIYGQAVEYSGLVRLNEDVYYFGETYKIETGWIENETDKYYFDPETKKAYKGINVVDDIKYYFDENGIMKTGLISFENNNYYFNEDGKMQFGYLNIKDKMFYFGKDGKMQIGVFNTPDGFKYFAHQNTLDENFEGESINYTGWLDLDGKRYYFTDEYIAATSSLTIDGYNYYFDPDTAELVVSE +P10297 reviewed defense response to virus [GO:0051607]; negative regulation of translation [GO:0017148] Phytolacca americana (American pokeweed) (Phytolacca decandra) RIP1_PHYAM MKSMLVVTISIWLILAPTSTWAVNTIIYNVGSTTISKYATFLNDLRNEAKDPSLKCYGIPMLPNTNTNPKYVLVELQGSNKKTITLMLRRNNLYVMGYSDPFETNKCRYHIFNDISGTERQDVETTLCPNANSRVSKNINFDSRYPTLESKAGVKSRSQVQLGIQILDSNIGKISGVMSFTEKTEAEFLLVAIQMVSEAARFKYIENQVKTNFNRAFNPNPKVLNLQETWGKISTAIHDAKNGVLPKPLELVDASGAKWIVLRVDEIKPDVALLNYVGGSCQTTYNQNAMFPQLIMSTYYNYMVNLGDLFEGF +P10844 reviewed proteolysis [GO:0006508] Clostridium botulinum BXB_CLOBO MPVTINNFNYNDPIDNNNIIMMEPPFARGTGRYYKAFKITDRIWIIPERYTFGYKPEDFNKSSGIFNRDVCEYYDPDYLNTNDKKNIFLQTMIKLFNRIKSKPLGEKLLEMIINGIPYLGDRRVPLEEFNTNIASVTVNKLISNPGEVERKKGIFANLIIFGPGPVLNENETIDIGIQNHFASREGFGGIMQMKFCPEYVSVFNNVQENKGASIFNRRGYFSDPALILMHELIHVLHGLYGIKVDDLPIVPNEKKFFMQSTDAIQAEELYTFGGQDPSIITPSTDKSIYDKVLQNFRGIVDRLNKVLVCISDPNININIYKNKFKDKYKFVEDSEGKYSIDVESFDKLYKSLMFGFTETNIAENYKIKTRASYFSDSLPPVKIKNLLDNEIYTIEEGFNISDKDMEKEYRGQNKAINKQAYEEISKEHLAVYKIQMCKSVKAPGICIDVDNEDLFFIADKNSFSDDLSKNERIEYNTQSNYIENDFPINELILDTDLISKIELPSENTESLTDFNVDVPVYEKQPAIKKIFTDENTIFQYLYSQTFPLDIRDISLTSSFDDALLFSNKVYSFFSMDYIKTANKVVEAGLFAGWVKQIVNDFVIEANKSNTMDKIADISLIVPYIGLALNVGNETAKGNFENAFEIAGASILLEFIPELLIPVVGAFLLESYIDNKNKIIKTIDNALTKRNEKWSDMYGLIVAQWLSTVNTQFYTIKEGMYKALNYQAQALEEIIKYRYNIYSEKEKSNINIDFNDINSKLNEGINQAIDNINNFINGCSVSYLMKKMIPLAVEKLLDFDNTLKKNLLNYIDENKLYLIGSAEYEKSKVNKYLKTIMPFDLSIYTNDTILIEMFNKYNSEILNNIILNLRYKDNNLIDLSGYGAKVEVYDGVELNDKNQFKLTSSANSKIRVTQNQNIIFNSVFLDFSVSFWIRIPKYKNDGIQNYIHNEYTIINCMKNNSGWKISIRGNRIIWTLIDINGKTKSVFFEYNIREDISEYINRWFFVTITNNLNNAKIYINGKLESNTDIKDIREVIANGEIIFKLDGDIDRTQFIWMKYFSIFNTELSQSNIEERYKIQSYSEYLKDFWGNPLMYNKEYYMFNAGNKNSYIKLKKDSPVGEILTRSKYNQNSKYINYRDLYIGEKFIIRRKSNSQSINDDIVRKEDYIYLDFFNLNQEWRVYTYKYFKKEEEKLFLAPISDSDEFYNTIQIKEYDEQPTYSCQLLFKKDEESTDEIGLIGIHRFYESGIVFEEYKDYFCISKWYLKEVKRKPYNLKLGCNWQFIPKDEGWTE +P11439 reviewed Pseudomonas aeruginosa (strain ATCC 15692 / DSM 22644 / CIP 104116 / JCM 14847 / LMG 12228 / 1C / PRS 101 / PAO1) TOXA_PSEAE MHLTPHWIPLVASLGLLAGGSFASAAEEAFDLWNECAKACVLDLKDGVRSSRMSVDPAIADTNGQGVLHYSMVLEGGNDALKLAIDNALSITSDGLTIRLEGGVEPNKPVRYSYTRQARGSWSLNWLVPIGHEKPSNIKVFIHELNAGNQLSHMSPIYTIEMGDELLAKLARDATFFVRAHESNEMQPTLAISHAGVSVVMAQAQPRREKRWSEWASGKVLCLLDPLDGVYNYLAQQRCNLDDTWEGKIYRVLAGNPAKHDLDIKPTVISHRLHFPEGGSLAALTAHQACHLPLETFTRHRQPRGWEQLEQCGYPVQRLVALYLAARLSWNQVDQVIRNALASPGSGGDLGEAIREQPEQARLALTLAAAESERFVRQGTGNDEAGAASADVVSLTCPVAAGECAGPADSGDALLERNYPTGAEFLGDGGDISFSTRGTQNWTVERLLQAHRQLEERGYVFVGYHGTFLEAAQSIVFGGVRARSQDLDAIWRGFYIAGDPALAYGYAQDQEPDARGRIRNGALLRVYVPRSSLPGFYRTGLTLAAPEAAGEVERLIGHPLPLRLDAITGPEEEGGRLETILGWPLAERTVVIPSAIPTDPRNVGGDLDPSSIPDKEQAISALPDYASQPGKPPREDLK +P13487 reviewed defense response to bacterium [GO:0042742]; defense response to fungus [GO:0050832]; killing of cells of another organism [GO:0031640] Leiurus hebraeus (Deathstalker scorpion) (Leiurus quinquestriatus hebraeus) KAX11_LEIHE MKILSVLLLALIICSIVGWSEAQFTNVSCTTSKECWSVCQRLHNTSRGKCMNKKCRCYS +P18640 reviewed membrane protein proteolysis [GO:0033619]; negative regulation of calcium ion-dependent exocytosis [GO:0045955]; symbiont-mediated suppression of host neurotransmitter secretion [GO:0044762] Clostridium botulinum C phage (Clostridium botulinum C bacteriophage) BXC_CBCP MPITINNFNYSDPVDNKNILYLDTHLNTLANEPEKAFRITGNIWVIPDRFSRNSNPNLNKPPRVTSPKSGYYDPNYLSTDSDKDTFLKEIIKLFKRINSREIGEELIYRLSTDIPFPGNNNTPINTFDFDVDFNSVDVKTRQGNNWVKTGSINPSVIITGPRENIIDPETSTFKLTNNTFAAQEGFGALSIISISPRFMLTYSNATNDVGEGRFSKSEFCMDPILILMHELNHAMHNLYGIAIPNDQTISSVTSNIFYSQYNVKLEYAEIYAFGGPTIDLIPKSARKYFEEKALDYYRSIAKRLNSITTANPSSFNKYIGEYKQKLIRKYRFVVESSGEVTVNRNKFVELYNELTQIFTEFNYAKIYNVQNRKIYLSNVYTPVTANILDDNVYDIQNGFNIPKSNLNVLFMGQNLSRNPALRKVNPENMLYLFTKFCHKAIDGRSLYNKTLDCRELLVKNTDLPFIGDISDVKTDIFLRKDINEETEVIYYPDNVSVDQVILSKNTSEHGQLDLLYPSIDSESEILPGENQVFYDNRTQNVDYLNSYYYLESQKLSDNVEDFTFTRSIEEALDNSAKVYTYFPTLANKVNAGVQGGLFLMWANDVVEDFTTNILRKDTLDKISDVSAIIPYIGPALNISNSVRRGNFTEAFAVTGVTILLEAFPEFTIPALGAFVIYSKVQERNEIIKTIDNCLEQRIKRWKDSYEWMMGTWLSRIITQFNNISYQMYDSLNYQAGAIKAKIDLEYKKYSGSDKENIKSQVENLKNSLDVKISEAMNNINKFIRECSVTYLFKNMLPKVIDELNEFDRNTKAKLINLIDSHNIILVGEVDKLKAKVNNSFQNTIPFNIFSYTNNSLLKDIINEYFNNINDSKILSLQNRKNTLVDTSGYNAEVSEEGDVQLNPIFPFDFKLGSSGEDRGKVIVTQNENIVYNSMYESFSISFWIRINKWVSNLPGYTIIDSVKNNSGWSIGIISNFLVFTLKQNEDSEQSINFSYDISNNAPGYNKWFFVTVTNNMMGNMKIYINGKLIDTIKVKELTGINFSKTITFEINKIPDTGLITSDSDNINMWIRDFYIFAKELDGKDINILFNSLQYTNVVKDYWGNDLRYNKEYYMVNIDYLNRYMYANSRQIVFNTRRNNNDFNEGYKIIIKRIRGNTNDTRVRGGDILYFDMTINNKAYNLFMKNETMYADNHSTEDIYAIGLREQTKDINDNIIFQIQPMNNTYYYASQIFKSNFNGENISGICSIGTYRFRLGGDWYRHNYLVPTVKQGNYASLLESTSTHWGFVPVSE +P19321 reviewed proteolysis [GO:0006508] Clostridium botulinum D phage (Clostridium botulinum D bacteriophage) BXD_CBDP MTWPVKDFNYSDPVNDNDILYLRIPQNKLITTPVKAFMITQNIWVIPERFSSDTNPSLSKPPRPTSKYQSYYDPSYLSTDEQKDTFLKGIIKLFKRINERDIGKKLINYLVVGSPFMGDSSTPEDTFDFTRHTTNIAVEKFENGSWKVTNIITPSVLIFGPLPNILDYTASLTLQGQQSNPSFEGFGTLSILKVAPEFLLTFSDVTSNQSSAVLGKSIFCMDPVIALMHELTHSLHQLYGINIPSDKRIRPQVSEGFFSQDGPNVQFEELYTFGGLDVEIIPQIERSQLREKALGHYKDIAKRLNNINKTIPSSWISNIDKYKKIFSEKYNFDKDNTGNFVVNIDKFNSLYSDLTNVMSEVVYSSQYNVKNRTHYFSRHYLPVFANILDDNIYTIRDGFNLTNKGFNIENSGQNIERNPALQKLSSESVVDLFTKVCLRLTKNSRDDSTCIKVKNNRLPYVADKDSISQEIFENKIITDETNVQNYSDKFSLDESILDGQVPINPEIVDPLLPNVNMEPLNLPGEEIVFYDDITKYVDYLNSYYYLESQKLSNNVENITLTTSVEEALGYSNKIYTFLPSLAEKVNKGVQAGLFLNWANEVVEDFTTNIMKKDTLDKISDVSVIIPYIGPALNIGNSALRGNFNQAFATAGVAFLLEGFPEFTIPALGVFTFYSSIQEREKIIKTIENCLEQRVKRWKDSYQWMVSNWLSRITTQFNHINYQMYDSLSYQADAIKAKIDLEYKKYSGSDKENIKSQVENLKNSLDVKISEAMNNINKFIRECSVTYLFKNMLPKVIDELNKFDLRTKTELINLIDSHNIILVGEVDRLKAKVNESFENTMPFNIFSYTNNSLLKDIINEYFNSINDSKILSLQNKKNALVDTSGYNAEVRVGDNVQLNTIYTNDFKLSSSGDKIIVNLNNNILYSAIYENSSVSFWIKISKDLTNSHNEYTIINSIEQNSGWKLCIRNGNIEWILQDVNRKYKSLIFDYSESLSHTGYTNKWFFVTITNNIMGYMKLYINGELKQSQKIEDLDEVKLDKTIVFGIDENIDENQMLWIRDFNIFSKELSNEDINIVYEGQILRNVIKDYWGNPLKFDTEYYIINDNYIDRYIAPESNVLVLVQYPDRSKLYTGNPITIKSVSDKNPYSRILNGDNIILHMLYNSRKYMIIRDTDTIYATQGGECSQNCVYALKLQSNLGNYGIGIFSIKNIVSKNKYCSQIFSSFRENTMLLADIYKPWRFSFKNAYTPVAVTNYETKLLSTSSFWKFISRDPGWVE +P24027 reviewed arachidonic acid secretion [GO:0050482]; lipid catabolic process [GO:0016042]; negative regulation of T cell proliferation [GO:0042130]; phospholipid metabolic process [GO:0006644] Crotalus durissus terrificus (South American rattlesnake) PA2BA_CRODU MRALWIVAVLLVGVEGSLLQFNKMIKFETRKNAVPFYAFYGCYCGWGGQGRPKDATDRCCFVHDCCYGKLAKCNTKWDIYRYSLKSGYITCGKGTWCKEQICECDRVAAECLRRSLSTYKNEYMFYPDSRCREPSETC +P30995 reviewed proteolysis [GO:0006508] Clostridium butyricum BXE_CLOBU MPTINSFNYNDPVNNRTILYIKPGGCQQFYKSFNIMKNIWIIPERNVIGTIPQDFLPPTSLKNGDSSYYDPNYLQSDQEKDKFLKIVTKIFNRINDNLSGRILLEELSKANPYLGNDNTPDGDFIINDASAVPIQFSNGSQSILLPNVIIMGAEPDLFETNSSNISLRNNYMPSNHGFGSIAIVTFSPEYSFRFKDNSMNEFIQDPALTLMHELIHSLHGLYGAKGITTKYTITQKQNPLITNIRGTNIEEFLTFGGTDLNIITSAQSNDIYTNLLADYKKIASKLSKVQVSNPLLNPYKDVFEAKYGLDKDASGIYSVNINKFNDIFKKLYSFTEFDLATKFQVKCRQTYIGQYKYFKLSNLLNDSIYNISEGYNINNLKVNFRGQNANLNPRIITPITGRGLVKKIIRFCKNIVSVKGIRKSICIEINNGELFFVASENSYNDDNINTPKEIDDTVTSNNNYENDLDQVILNFNSESAPGLSDEKLNLTIQNDAYIPKYDSNGTSDIEQHDVNELNVFFYLDAQKVPEGENNVNLTSSIDTALLEQPKIYTFFSSEFINNVNKPVQAALFVGWIQQVLVDFTTEANQKSTVDKIADISIVVPYIGLALNIGNEAQKGNFKDALELLGAGILLEFEPELLIPTILVFTIKSFLGSSDNKNKVIKAINNALKERDEKWKEVYSFIVSNWMTKINTQFNKRKEQMYQALQNQVNALKAIIESKYNSYTLEEKNELTNKYDIEQIENELNQKVSIAMNNIDRFLTESSISYLMKLINEVKINKLREYDENVKTYLLDYIIKHGSILGESQQELNSMVIDTLNNSIPFKLSSYTDDKILISYFNKFFKRIKSSSVLNMRYKNDKYVDTSGYDSNININGDVYKYPTNKNQFGIYNDKLSEVNISQNDYIIYDNKYKNFSISFWVRIPNYDNKIVNVNNEYTIINCMRDNNSGWKVSLNHNEIIWTLQDNSGINQKLAFNYGNANGISDYINKWIFVTITNDRLGDSKLYINGNLIDKKSILNLGNIHVSDNILFKIVNCSYTRYIGIRYFNIFDKELDETEIQTLYNNEPNANILKDFWGNYLLYDKEYYLLNVLKPNNFINRRTDSTLSINNIRSTILLANRLYSGIKVKIQRVNNSSTNDNLVRKNDQVYINFVASKTHLLPLYADTATTNKEKTIKISSSGNRFNQVVVMNSVGNCTMNFKNNNGNNIGLLGFKADTVVASTWYYTHMRDNTNSNGFFWNFISEEHGWQEK +P30996 reviewed proteolysis [GO:0006508] Clostridium botulinum BXF_CLOBO MPVAINSFNYNDPVNDDTILYMQIPYEEKSKKYYKAFEIMRNVWIIPERNTIGTNPSDFDPPASLKNGSSAYYDPNYLTTDAEKDRYLKTTIKLFKRINSNPAGKVLLQEISYAKPYLGNDHTPIDEFSPVTRTTSVNIKLSTNVESSMLLNLLVLGAGPDIFESCCYPVRKLIDPDVVYDPSNYGFGSINIVTFSPEYEYTFNDISGGHNSSTESFIADPAISLAHELIHALHGLYGARGVTYEETIEVKQAPLMIAEKPIRLEEFLTFGGQDLNIITSAMKEKIYNNLLANYEKIATRLSEVNSAPPEYDINEYKDYFQWKYGLDKNADGSYTVNENKFNEIYKKLYSFTESDLANKFKVKCRNTYFIKYEFLKVPNLLDDDIYTVSEGFNIGNLAVNNRGQSIKLNPKIIDSIPDKGLVEKIVKFCKSVIPRKGTKAPPRLCIRVNNSELFFVASESSYNENDINTPKEIDDTTNLNNNYRNNLDEVILDYNSQTIPQISNRTLNTLVQDNSYVPRYDSNGTSEIEEYDVVDFNVFFYLHAQKVPEGETNISLTSSIDTALLEESKDIFFSSEFIDTINKPVNAALFIDWISKVIRDFTTEATQKSTVDKIADISLIVPYVGLALNIIIEAEKGNFEEAFELLGVGILLEFVPELTIPVILVFTIKSYIDSYENKNKAIKAINNSLIEREAKWKEIYSWIVSNWLTRINTQFNKRKEQMYQALQNQVDAIKTAIEYKYNNYTSDEKNRLESEYNINNIEEELNKKVSLAMKNIERFMTESSISYLMKLINEAKVGKLKKYDNHVKSDLLNYILDHRSILGEQTNELSDLVTSTLNSSIPFELSSYTNDKILIIYFNRLYKKIKDSSILDMRYENNKFIDISGYGSNISINGNVYIYSTNRNQFGIYNSRLSEVNIAQNNDIIYNSRYQNFSISFWVRIPKHYKPMNHNREYTIINCMGNNNSGWKISLRTVRDCEIIWTLQDTSGNKENLIFRYEELNRISNYINKWIFVTITNNRLGNSRIYINGNLIVEKSISNLGDIHVSDNILFKIVGCDDETYVGIRYFKVFNTELDKTEIETLYSNEPDPSILKNYWGNYLLYNKKYYLFNLLRKDKYITLNSGILNINQQRGVTEGSVFLNYKLYEGVEVIIRKNGPIDISNTDNFVRKNDLAYINVVDRGVEYRLYADTKSEKEKIIRTSNLNDSLGQIIVMDSIGNNCTMNFQNNNGSNIGLLGFHSNNLVASSWYYNNIRRNTSSNGCFWSSISKENGWKE +P50983 reviewed Conus imperialis (Imperial cone) CA1_CONIM IVRRGCCSDPRCAWRCG +P56636 reviewed Conus magus (Magical cone) CA12_CONMA MGMRMMFTVFLLVVLATTVVSFPSDRASDGRNAAANDKASDVITLALKGCCSNPVCHLEHSNLCGRRR +P60305 reviewed killing of cells of another organism [GO:0031640] Naja kaouthia (Monocled cobra) (Naja siamensis) 3SA8_NAJKA LKCNKLIPIASKTCPAGKNLCYKMFMMSDLTIPVKRGCIDVCPKNSLLVKYVCCNTDRCN +P60770 reviewed Naja atra (Chinese cobra) 3S1CB_NAJAT MKTLLLTLLVVTIVCLDLGYTLECHNQQSSQTPTTTGCSGGETNCYKKRWRDHRGYRTERGCGCPSVKNGIEINCCTTDRCNN +P62022 reviewed arachidonic acid secretion [GO:0050482]; envenomation resulting in induction of edema in another organism [GO:0044398]; envenomation resulting in muscle damage in another organism [GO:0044521]; envenomation resulting in myocyte killing in another organism [GO:0044522]; envenomation resulting in positive regulation of platelet aggregation in another organism [GO:0044478]; lipid catabolic process [GO:0016042]; negative regulation of T cell proliferation [GO:0042130]; phospholipid metabolic process [GO:0006644] Crotalus durissus terrificus (South American rattlesnake) PA2BC_CRODU MRALWIVAVLLVGVEGHLLQFNKMIKFETRKNAIPFYAFYGCYCGWGGRGRPKDATDRCCFVHDCCYGKLAKCNTKWDIYPYSLKSGYITCGKGTWCEEQICECDRVAAECLRRSLSTYKYGYMFYPDSRCRGPSETC +P69179 reviewed hemolysis by symbiont of host erythrocytes [GO:0019836]; modulation of host virulence by virus [GO:0098676] Enterobacteria phage H19B (Bacteriophage H19B) STXB_BPH19 MKKTLLIAASLSFFSASALATPDCVTGKVEYTKYNDDDTFTVKVGDKELFTNRWNLQSLLLSAQITGMTVTIKTNACHNGGGFSEVIFR +P85253 reviewed cytolysis by host of symbiont cells [GO:0051838]; defense response to Gram-negative bacterium [GO:0050829]; defense response to Gram-positive bacterium [GO:0050830] Lachesana tarabaevi (Spider) CTX11_LACTA MKYFVVALALVAAFACIAESKPAESEHELAEVEEENELADLEDAVWLEHLADLSDLEEARGFFGNTWKKIKGKADKIMLKKAVKIMVKKEGISKEEAQAKVDAMSKKQIRLYLLKYYGKKALQKASEKL +Q00496 reviewed proteolysis [GO:0006508] Clostridium botulinum BXE_CLOBO MPKINSFNYNDPVNDRTILYIKPGGCQEFYKSFNIMKNIWIIPERNVIGTTPQDFHPPTSLKNGDSSYYDPNYLQSDEEKDRFLKIVTKIFNRINNNLSGGILLEELSKANPYLGNDNTPDNQFHIGDASAVEIKFSNGSQDILLPNVIIMGAEPDLFETNSSNISLRNNYMPSNHRFGSIAIVTFSPEYSFRFNDNCMNEFIQDPALTLMHELIHSLHGLYGAKGITTKYTITQKQNPLITNIRGTNIEEFLTFGGTDLNIITSAQSNDIYTNLLADYKKIASKLSKVQVSNPLLNPYKDVFEAKYGLDKDASGIYSVNINKFNDIFKKLYSFTEFDLRTKFQVKCRQTYIGQYKYFKLSNLLNDSIYNISEGYNINNLKVNFRGQNANLNPRIITPITGRGLVKKIIRFCKNIVSVKGIRKSICIEINNGELFFVASENSYNDDNINTPKEIDDTVTSNNNYENDLDQVILNFNSESAPGLSDEKLNLTIQNDAYIPKYDSNGTSDIEQHDVNELNVFFYLDAQKVPEGENNVNLTSSIDTALLEQPKIYTFFSSEFINNVNKPVQAALFVSWIQQVLVDFTTEANQKSTVDKIADISIVVPYIGLALNIGNEAQKGNFKDALELLGAGILLEFEPELLIPTILVFTIKSFLGSSDNKNKVIKAINNALKERDEKWKEVYSFIVSNWMTKINTQFNKRKEQMYQALQNQVNAIKTIIESKYNSYTLEEKNELTNKYDIKQIENELNQKVSIAMNNIDRFLTESSISYLMKIINEVKINKLREYDENVKTYLLNYIIQHGSILGESQQELNSMVTDTLNNSIPFKLSSYTDDKILISYFNKFFKRIKSSSVLNMRYKNDKYVDTSGYDSNININGDVYKYPTNKNQFGIYNDKLSEVNISQNDYIIYDNKYKNFSISFWVRIPNYDNKIVNVNNEYTIINCMRDNNSGWKVSLNHNEIIWTFEDNRGINQKLAFNYGNANGISDYINKWIFVTITNDRLGDSKLYINGNLIDQKSILNLGNIHVSDNILFKIVNCSYTRYIGIRYFNIFDKELDETEIQTLYSNEPNTNILKDFWGNYLLYDKEYYLLNVLKPNNFIDRRKDSTLSINNIRSTILLANRLYSGIKVKIQRVNNSSTNDNLVRKNDQVYINFVASKTHLFPLYADTATTNKEKTIKISSSGNRFNQVVVMNSVGNCTMNFKNNNGNNIGLLGFKADTVVASTWYYTHMRDHTNSNGCFWNFISEEHGWQEK +Q28735 reviewed COPI-coated vesicle budding [GO:0035964]; cytosol to ERGIC protein transport [GO:0106273]; endoplasmic reticulum to Golgi vesicle-mediated transport [GO:0006888]; Golgi organization [GO:0007030]; intracellular protein transport [GO:0006886]; positive regulation of interleukin-1 production [GO:0032732]; positive regulation of protein secretion [GO:0050714]; protein localization to ERGIC [GO:0106272]; regulated exocytosis [GO:0045055]; regulation of amyloid-beta formation [GO:1902003]; retrograde vesicle-mediated transport, Golgi to endoplasmic reticulum [GO:0006890] Oryctolagus cuniculus (Rabbit) TMEDA_RABIT MSGWSGPLARRGPGPLALLFLFLLGPSSVLAISFHLPVNSRKCLREEIHKDLLVTGAYEITDQSGGAGGLRTHLKITDSAGHILYSKEDASKGKFAFTTEDYDMFEVCFESKGTGRIPDQLVILDMKHGVEAKNYEEIAKVEKLKPLEVELRRLEDLSESIVNDFAYMKKREEEMRDTNESTNTRVLYFSIFSMFCLIGLATWQVFYLRRFFKAKKLIE +Q40772 reviewed defense response to virus [GO:0051607]; negative regulation of translation [GO:0017148] Phytolacca americana (American pokeweed) (Phytolacca decandra) RIP2_PHYAM MKMKVLEVVGLAISIWLMLTPPASSNIVFDVENATPETYSNFLTSLREAVKDKKLTCHGMIMATTLTEQPKYVLVDLKFGSGTFTLAIRRGNLYLEGYSDIYNGKCRYRIFKDSESDAQETVCPGDKSKPGTQNNIPYEKSYKGMESKGGARTKLGLGKITLKSRMGKIYGKDATDQKQYQKNEAEFLLIAVQMVTEASRFKYIENKVKAKFDDANGYQPDPKAISLEKNWDSVSKVIAKVGTSGDSTVTLPGDLKDENNKPWTTATMNDLKNDIMALLTHVTCKVKSSMFPEIMSYYYRTSISNLGEFE +Q45894 reviewed proteolysis [GO:0006508] Clostridium botulinum (strain Kyoto / Type A2) BXA2_CLOBJ MPFVNKQFNYKDPVNGVDIAYIKIPNAGQMQPVKAFKIHNKIWVIPERDTFTNPEEGDLNPPPEAKQVPVSYYDSTYLSTDNEKDNYLKGVTKLFERIYSTDLGRMLLTSIVRGIPFWGGSTIDTELKVIDTNCINVIQPDGSYRSEELNLVIIGPSADIIQFECKSFGHDVLNLTRNGYGSTQYIRFSPDFTFGFEESLEVDTNPLLGAGKFATDPAVTLAHELIHAEHRLYGIAINPNRVFKVNTNAYYEMSGLEVSFEELRTFGGHDAKFIDSLQENEFRLYYYNKFKDVASTLNKAKSIIGTTASLQYMKNVFKEKYLLSEDTSGKFSVDKLKFDKLYKMLTEIYTEDNFVNFFKVINRKTYLNFDKAVFRINIVPDENYTIKDGFNLKGANLSTNFNGQNTEINSRNFTRLKNFTGLFEFYKLLCVRGIIPFKTKSLDEGYNKALNDLCIKVNNWDLFFSPSEDNFTNDLDKVEEITADTNIEAAEENISLDLIQQYYLTFDFDNEPENISIENLSSDIIGQLEPMPNIERFPNGKKYELDKYTMFHYLRAQEFEHGDSRIILTNSAEEALLKPNVAYTFFSSKYVKKINKAVEAFMFLNWAEELVYDFTDETNEVTTMDKIADITIIVPYIGPALNIGNMLSKGEFVEAIIFTGVVAMLEFIPEYALPVFGTFAIVSYIANKVLTVQTINNALSKRNEKWDEVYKYTVTNWLAKVNTQIDLIREKMKKALENQAEATKAIINYQYNQYTEEEKNNINFNIDDLSSKLNESINSAMININKFLDQCSVSYLMNSMIPYAVKRLKDFDASVRDVLLKYIYDNRGTLVLQVDRLKDEVNNTLSADIPFQLSKYVDNKKLLSTFTEYIKNIVNTSILSIVYKKDDLIDLSRYGAKINIGDRVYYDSIDKNQIKLINLESSTIEVILKNAIVYNSMYENFSTSFWIKIPKYFSKINLNNEYTIINCIENNSGWKVSLNYGEIIWTLQDNKQNIQRVVFKYSQMVNISDYINRWIFVTITNNRLTKSKIYINGRLIDQKPISNLGNIHASNKIMFKLDGCRDPRRYIMIKYFNLFDKELNEKEIKDLYDSQSNSGILKDFWGNYLQYDKPYYMLNLFDPNKYVDVNNIGIRGYMYLKGPRGSVVTTNIYLNSTLYEGTKFIIKKYASGNEDNIVRNNDRVYINVVVKNKEYRLATNASQAGVEKILSALEIPDVGNLSQVVVMKSKDDQGIRNKCKMNLQDNNGNDIGFIGFHLYDNIAKLVASNWYNRQVGKASRTFGCSWEFIPVDDGWGESSL +Q46342 reviewed proteolysis [GO:0006508] Paraclostridium sordellii (Clostridium sordellii) TCSL1_PARSO MNLVNKAQLQKMVYVKFRIQEDEYVAILNALEEYHNMSESSVVEKYLKLKDINNLTDNYLNTYKKSGRNKALKKFKEYLTMEVLELKNNSLTPVEKNLHFIWIGGQINDTAINYINQWKDVNSDYTVKVFYDSNAFLINTLKKTIVESATNNTLESFRENLNDPEFDYNKFYRKRMEIIYDKQKHFIDYYKSQIEENPEFIIDNIIKTYLSNEYSKDLEALNKYIEESLNKITANNGNDIRNLEKFADEDLVRLYNQELVERWNLAAASDILRISMLKEDGGVYLDVDILPGIQPDLFKSINKPDSITNTSWEMIKLEAIMKYKEYIPGYTSKNFDMLDEEVQRSFESALSSKSDKSEIFLPLDDIKVSPLEVKIAFANNSVINQALISLKDSYCSDLVINQIKNRYKILNDNLNPSINEGTDFNTTMKIFSDKLASISNEDNMMFMIKITNYLKVGFAPDVRSTINLSGPGVYTGAYQDLLMFKDNSTNIHLLEPELRNFEFPKTKISQLTEQEITSLWSFNQARAKSQFEEYKKGYFEGALGEDDNLDFAQNTVLDKDYVSKKILSSMKTRNKEYIHYIVQLQGDKISYEASCNLFSKDPYSSILYQKNIEGSETAYYYYVADAEIKEIDKYRIPYQISNKRNIKLTFIGHGKSEFNTDTFANLDVDSLSSEIETILNLAKADISPKYIEINLLGCNMFSYSISAEETYPGKLLLKIKDRVSELMPSISQDSITVSANQYEVRINEEGKREILDHSGKWINKEESIIKDISSKEYISFNPKENKIIVKSKYLHELSTLLQEIRNNANSSDIDLEKKVMLTECEINVASNIDRQIVEGRIEEAKNLTSDSINYIKNEFKLIESISDSLYDLKHQNGLDDSHFISFEDISKTENGFRIRFINKETGNSIFIETEKEIFSEYATHISKEISNIKDTIFDNVNGKLVKKVNLDAAHEVNTLNSAFFIQSLIEYNTTKESLSNLSVAMKVQVYAQLFSTGLNTITDASKVVELVSTALDETIDLLPTLSEGLPIIATIIDGVSLGAAIKELSETNDPLLRQEIEAKIGIMAVNLTAASTAIVTSALGIASGFSILLVPLAGISAGIPSLVNNELILQDKATKVIDYFKHISLAETEGAFTLLDDKIIMPQDDLVLSEIDFNNNSITLGKCEIWRAEGGSGHTLTDDIDHFFSSPSITYRKPWLSIYDVLNIKKEKIDFSKDLMVLPNAPNRVFGYEMGWTPGFRSLDNDGTKLLDRIRDHYEGQFYWRYFAFIADALITKLKPRYEDTNVRINLDGNTRSFIVPVITTEQIRKNLSYSFYGSGGSYSLSLSPYNMNIDLNLVENDTWVIDVDNVVKNITIESDEIQKGELIENILSKLNIEDNKIILNNHTINFYGDINESNRFISLTFSILEDINIIIEIDLVSKSYKILLSGNCMKLIENSSDIQQKIDHIGFNGEHQKYIPYSYIDNETKYNGFIDYSKKEGLFTAEFSNESIIRNIYMPDSNNLFIYSSKDLKDIRIINKGDVKLLIGNYFKDDMKVSLSFTIEDTNTIKLNGVYLDENGVAQILKFMNNAKSALNTSNSLMNFLESINIKNIFYNNLDPNIEFILDTNFIISGSNSIGQFELICDKDKNIQPYFINFKIKETSYTLYVGNRQNLIVEPSYHLDDSGNISSTVINFSQKYLYGIDRYVNKVIIAPNLYTDEINITPVYKPNYICPEVIILDANYINEKINVNINDLSIRYVWDNDGSDLILIANSEEDNQPQVKIRFVNVFKSDTAADKLSFNFSDKQDVSVSKIISTFSLAAYSDGFFDYEFGLVSLDNDYFYINSFGNMVSGLIYINDSLYYFKPPKNNLITGFTTIDGNKYYFDPTKSGAASIGEITIDGKDYYFNKQGILQVGVINTSDGLKYFAPAGTLDENLEGESVNFIGKLNIDGKIYYFEDNYRAAVEWKLLDDETYYFNPKTGEALKGLHQIGDNKYYFDDNGIMQTGFITINDKVFYFNNDGVMQVGYIEVNGKYFYFGKNGERQLGVFNTPDGFKFFGPKDDDLGTEEGELTLYNGILNFNGKIYFFDISNTAVVGWGTLDDGSTYYFDDNRAEACIGLTVINDCKYYFDDNGIRQLGFITINDNIFYFSESGKIELGYQNINGNYFYIDESGLVLIGVFDTPDGYKYFAPLNTVNDNIYGQAVKYSGLVRVNEDVYYFGETYKIETGWIENETDKYYFDPETKKAYKGINVVDDIKYYFDENGIMRTGLISFENNNYYFNEDGKMQFGYLNIKDKMFYFGKDGKMQIGVFNTPDGFKYFAHQNTLDENFEGESINYTGWLDLDGKRYYFTDEYIAATGSLTIDGYNYYFDPDTAELVVSE +Q60393 reviewed proteolysis [GO:0006508] Clostridium botulinum BXG_CLOBO MPVNIKXFNYNDPINNDDIIMMEPFNDPGPGTYYKAFRIIDRIWIVPERFTYGFQPDQFNASTGVFSKDVYEYYDPTYLKTDAEKDKFLKTMIKLFNRINSKPSGQRLLDMIVDAIPYLGNASTPPDKFAANVANVSINKKIIQPGAEDQIKGLMTNLIIFGPGPVLSDNFTDSMIMNGHSPISEGFGARMMIRFCPSCLNVFNNVQENKDTSIFSRRAYFADPALTLMHELIHVLHGLYGIKISNLPITPNTKEFFMQHSDPVQAEELYTFGGHDPSVISPSTDMNIYNKALQNFQDIANRLNIVSSAQGSGIDISLYKQIYKNKYDFVEDPNGKYSVDKDKFDKLYKALMFGFTETNLAGEYGIKTRYSYFSEYLPPIKTEKLLDNTIYTQNEGFNIASKNLKTEFNGQNKAVNKEAYEEISLEHLVIYRIAMCKPVMYKNTGKSEQCIIVNNEDLFFIANKDSFSKDLAKAETIAYNTQNNTIENNFSIDQLILDNDLSSGIDLPNENTEPFTNFDDIDIPVYIKQSALKKIFVDGDSLFEYLHAQTFPSNIENLQLTNSLNDALRNNNKVYTFFSTNLVEKANTVVGASLFVNWVKGVIDDFTSESTQKSTIDKVSDVSIIIPYIGPALNVGNETAKENFKNAFEIGGAAILMEFIPELIVPIVGFFTLESYVGNKGHIIMTISNALKKRDQKWTDMYGLIVSQWLSTVNTQFYTIKERMYNALNNQSQAIEKIIEDQYNRYSEEDKMNINIDFNDIDFKLNQSINLAINNIDDFINQCSISYLMNRMIPLAVKKLKDFDDNLKRDLLEYIDTNELYLLDEVNILKSKVNRHLKDSIPFDLSLYTKDTILIQVFNNYISNISSNAILSLSYRGGRLIDSSGYGATMNVGSDVIFNDIGNGQFKLNNSENSNITAHQSKFVVYDSMFDNFSINFWVRTPKYNNNDIQTYLQNEYTIISCIKNDSGWKVSIKGNRIIWTLIDVNAKSKSIFFEYSIKDNISDYINKWFSITITNDRLGNANIYINGSLKKSEKILNLDRINSSNDIDFKLINCTDTTKFVWIKDFNIFGRELNATEVSSLYWIQSSTNTLKDFWGNPLRYDTQYYLFNQGMQNIYIKYFSKASMGETAPRTNFNNAAINYQNLYLGLRFIIKKASNSRNINNDNIVREGDYIYLNIDNISDESYRVYVLVNSKEIQTQLFLAPINDDPTFYDVLQIKKYYEKTTYNCQILCEKDTKTFGLFGIGKFVKDYGYVWDTYDNYFCISQWYLRRISENINKLRLGCNWQFIPVDEGWTE +Q7BQ98 reviewed hemolysis by symbiont of host erythrocytes [GO:0019836] Shigella dysenteriae STXB_SHIDY MKKTLLIAASLSFFSASALATPDCVTGKVEYTKYNDDDTFTVKVGDKELFTNRWNLQSLLLSAQITGMTVTIKTNACHNGGGFSEVIFR +Q9FBI2 reviewed negative regulation of translation [GO:0017148] Shigella dysenteriae STXA_SHIDY MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +T0D3N5 reviewed proteolysis [GO:0006508] Paraclostridium sordellii (strain ATCC 9714 / DSM 2141 / JCM 3814 / LMG 15708 / NCIMB 10717 / 211) (Clostridium sordellii) TCSL3_PARS9 MNLVNKAQLQKMAYVKFRIQEDEYVAILNALEEYHNMSESSVVEKYLKLKDINNLTDNYLNTYKKSGRNKALKKFKEYLTMEVLELKNNSLTPVEKNLHFIWIGGQINDTAINYINQWKDVNSDYTVKVFYDSNAFLINTLKKTIVESATNNTLESFRENLNDPEFDYNKFYRKRMEIIYDKQKHFIDYYKSQIEENPEFIIDNIIKTYLSNEYSKDLEALNKYIEESLNKITANNGNDIRNLEKFADEDLVRLYNQELVERWNLAAASDILRISMLKEDGGVYLDVDMLPGIQPDLFKSINKPDSITNTSWEMIKLEAIMKYKEYIPGYTSKNFDMLDEEVQRSFESALSSKSDKSEIFLPLDDIKVSPLEVKIAFANNSVINQALISLKDSYCSDLVINQIKNRYKILNDNLNPSINEGTDFNTTMKIFSDKLASISNEDNMMFMIKITNYLKVGFAPDVRSTINLSGPGVYTGAYQDLLMFKDNSTNIHLLEPELRNFEFPKTKISQLTEQEITSLWSFNQARAKSQFEEYKKGYFEGALGEDDNLDFAQNTVLDKDYVSKKILSSMKTRNKEYIHYIVQLQGDKISYEASCNLFSKDPYSSILYQKNIEGSETAYYYSVADAEIKEIDKYRIPYQISNKRKIKLTFIGHGKSEFNTDTFANLDVDSLSSEIETILNLAKADISPKYIEINLLGCNMFSYSISAEETYPGKLLLKIKDRVSELMPSISQDSITVSANQYEVRINEEGKREILDHSGKWINKEESIIKDISSKEYISFNPKENKIIVKSKYLHELSTLLQEIRNNANSSDIDLEKKVMLTECEINVASNIDRQIVEGRIEEAKNLTSDSINYIKNEFKLIESISDSLYDLKHQNGLDDSHFISFEDISKTENGFRIRFINKETGNSIFIETEKEIFSEYATHISKEISNIKDTIFDNVNGKLVKKVNLDAAHEVNTLNSAFFIQSLIEYNTTKESLSNLSVAMKVQVYAQLFSTGLNTITDASKVVELVSTALDETIDLLPTLSEGLPIIATIIDGVSLGAAIKELSETNDPLLRQEIEAKIGIMAVNLTAASTAIVTSALGIASGFSILLVPLAGISAGIPSLVNNELILQDKATKVIDYFKHISLAETEGAFTLLDDKIIMPQDDLVLSEIDFNNNSITLGKCEIWRAEGGSGHTLTDDIDHFFSSPSITYRKPWLSIYDVLNIKKEKIDFSKDLMVLPNAPNRVFGYEMGWTPGFRSLDNDGTKLLDRIRDHYEGQFYWRYFAFIADALITKLKPRYEDTNVRINLDGNTRSFIVPVITTEQIRKNLSYSFYGSGGSYSLSLSPYNMNIDLNLVENDTWVIDVDNVVKNITIESDEIQKGELIENILSKLNIEDNKIILNNHTINFYGDINESNRFISLTFSILEDINIIIEIDLVSKSYKILLSGNCMKLIENSSDIQQKIDHIGFNGEHQKYIPYSYIDNETKYNGFIDYSKKEGLFTAEFSNESIIRNIYMPDSNNLFIYSSKDLKDIRIINKGDVKLLIGNYFKDDMKVSLSFTIEDTNTIKLNGVYLDENGVAQILKFMNNAKSALNTSNSLMNFLESINIKNIFYNNLDPNIEFILDTNFIISGSNSIGQFELICDKDKNIQPYFIKFKIKETSYTLYVGNRQNLIVEPSYHLDDSGNISSTVINFSQKYLYGIDRYVNKVIIAPNLYTDEINITPVYKPNYICPEVIILDANYINEKINVNINDLSIRYVWDNDGSDLILIANSEEDNQPQVKIRFVNVFKSDTAADKLSFNFSDKQDVSVSKIISTFSLAAYSDGFFDYEFGLVSLDNDYFYINSFGNMVSGLIYINDSLYYFKPPKNNLITGFTTIDGNKYYFDPTKSGAASIGEITIDGKDYYFNKQGILQVGVINTSDGLKYFAPAGTLDENLEGESVNFIGKLNIDGKIYYFEDNYRAAVEWKLLDDETYYFNPKTGEALKGLHQIGDNKYYFDDNGIMQTGFITINDKVFYFNNDGVMQVGYIEVNGKYFYFGKNGERQLGVFNTPDGFKFFGPKDDDLGTEEGELTLYNGILNFNGKIYFFDISNTAVVGWGTLDDGSTYYFDDNTAEACIGLTVINDCKYYFDDNGIRQLGFITINDNIFYFSESGKIELGYQNINGNYFYIDESGLVLIGVFDTPDGYKYFAPLNTVNDNIYGQAVKYSGLVRVNEDVYYFGETYKIETGWIENETDKYYFDPETKKAYKGINVVDDIKYYFDENGIMRTGLISFENNNYYFNEDGKMQFGYLNIKDKMFYFGKDGKMQIGVFNTPDGFKYFAHQNTLDENFEGESINYTGWLDLDGKRYYFTDEYIAATGSLTIDGYNYYFDPDTAELVVSE +B2DCR8 reviewed Sepia esculenta (Golden cuttlefish) (Diphtherosepion dabryi) CTX_SEPES MMGTSRCVILLFALLLWAANAAPPEIHTTRPNVPEEIKRPNSTEIETPAVKQLETPSIFLLTTLEVAEADVDSTLETMKDRNKKNSAKLSKIGNNMKSLLSVFSVFGGFLSLLSVVTTTSDLQVISDMFTGVNRKLDQINDKLDKLDNSVELQGLLTNYIPWQYSVKNGIEKLIETYKKMVEETDMNKRRLMAENFILFFENNQIESNINNLIKLTTTTDAVHQNMLFNELLDEAGCDIIRLTRIYMHVRRIFYQGTQLVLAYNSFKQMDPPEMKKYLNALIFIRNMYQSRVWHCKETTIAQSKKDIKDIVKTNAKFGITTVLRKINSELSRKYPWYSWSIVTVKKMLANQRNSTLGNQFYEMEAVGPHGSNFVVIWQGFKEHSQCEDIQKANTIAVLTICKSCHQSHVFTPSNMLNKNTCPNNQYPQVKAFIDRREPFRDEIQRKKSDVFWVAAGFKAPGNPCNHGCNGHGECKVVPYTDQFQCFCHGNYEGKMCQKKIQMKRDISKLISDLQTGYKNAFNVPSLTNILIQGENLAKQLKKMIQRIDNQFELTHILVKYISDLQKLDYILKISFNYSKKKITVDAFSRRMKAFLSLNPVDFIFQQLSNAILAEGFTDIQGKDFFNTFKRMIASNRDACTAPYGNEATILLERLSRLDLTAAESILAYYSFESNYLNPANMKRMLENAKQLVRDSKRRMRSYARYWERTSCPPLNVTHLTQTGCGALLSFEGMKVKLSCDGGRAAVPQNIECVNVNGNLQWSATPKCESSWSRWSKWSACASTCGNATQSRRRRCLGQSESEKCIGPSKQVRKCFVEDCCQEKYGKFKCDNNKCISLSRVCDGNDDCRNAEDESKSRCKYLRSGDRIALRNMAYSQEWLSVQYTDAVQADLYYGRAYLNHCIKGDHVTSSEWNSCAGQSLLIYGNYENGKIGKAIRFGDKIAMYYRKTNYHYRWFICYPTYCMTYTCPKKAGSFTFGPNGGCDEYEFYIINYNDKLSRDPVKPGDVITLANNRGSVKGNGYNRNININDCTVKRAQDDRIECNANAWQIFIK +C0HJV6 reviewed defense response to bacterium [GO:0042742] Lachesana tarabaevi (Spider) CTX4_LACTA MKCFILAAALVLAFACIAASEPAETENEDLDDLSDLEDEEWLDELEEAAEYLESLREFEESRGYKDYMSKAKDLYKDIKKDKRVKAVMKSSYMKEAKKLYKDNPVRDAYQVYKGVKAGGKLLFG +P00587 reviewed Corynephage omega DTX_COROM MSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +P01442 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SA2_NAJAT MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMFMVSNLTVPVKRGCIDVCPKNSALVKYVCCNTDRCN +P01443 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SA4_NAJAT MKTLLLTLVVVTIVCLDLGYTRKCNKLVPLFYKTCPAGKNLCYKMFMVSNLTVPVKRGCIDVCPKNSALVKYVCCNTDRCN +P01452 reviewed killing of cells of another organism [GO:0031640] Naja mossambica (Mozambique spitting cobra) 3SA4_NAJMO LKCNKLIPIAYKTCPEGKNLCYKMMLASKKMVPVKRGCINVCPKNSALVKYVCCSTDRCN +P01467 reviewed killing of cells of another organism [GO:0031640] Naja mossambica (Mozambique spitting cobra) 3SA1_NAJMO LKCNQLIPPFWKTCPKGKNLCYKMTMRAAPMVPVKRGCIDVCPKSSLLIKYMCCNTNKCN +P01469 reviewed killing of cells of another organism [GO:0031640] Naja mossambica (Mozambique spitting cobra) 3SA2_NAJMO LKCNQLIPPFWKTCPKGKNLCYKMTMRGASKVPVKRGCIDVCPKSSLLIKYMCCNTDKCN +P07525 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SAT_NAJAT LKCNKLVPLFYKTCPAGKNLCYKMFMVSNKMVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +P08026 reviewed modulation of host virulence by virus [GO:0098676]; negative regulation of translation [GO:0017148] Enterobacteria phage H19B (Bacteriophage H19B) STXA_BPH19 MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGSGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +P09386 reviewed hemolysis by symbiont of host erythrocytes [GO:0019836]; modulation of host virulence by virus [GO:0098676] Escherichia phage 933W (Bacteriophage 933W) STXB_BP933 MKKMFMAVLFALASVNAMAADCAKGKIEFSKYNEDDTFTVKVDGKEYWTSRWNLQPLLQSAQLTGMTVTIKSSTCESGSGFAEVQFNND +P10149 reviewed modulation of host virulence by virus [GO:0098676]; negative regulation of translation [GO:0017148] Bacteriophage H30 STXA_BPH30 MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +P58809 reviewed Conus marmoreus (Marble cone) CTAX_CONMR MRCLPVLIILLLLTASAPGVDVLPKTEDDVPLSSVYGNGKSILRGILRKGICCGVSFCYPC +P60301 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SA3_NAJAT MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMFMVATPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +P60303 reviewed killing of cells of another organism [GO:0031640] Naja kaouthia (Monocled cobra) (Naja siamensis) 3SA4_NAJKA MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMFMVATPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +P60304 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SA1_NAJAT MKTLLLTLVVVTIVCLDLGYTLKCNKLIPIASKTCPAGKNLCYKMFMMSDLTIPVKRGCIDVCPKNSLLVKYVCCNTDRCN +P62375 reviewed Naja atra (Chinese cobra) 3SOF5_NAJAT MKTLLLTMVVVTIVCLDLGYTLKCHNTQLPFIYKTCPEGKNLCFKATLKKFPLKFPVKRGCADNCPKNSALLKYVCCSTDKCN +P80245 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SAN_NAJAT MKTLLLTLVVVTIVCLDLGYTLKCNQLIPPFYKTCAAGKNLCYKMFMVAAPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +P85258 reviewed cytolysis by host of symbiont cells [GO:0051838]; defense response to Gram-negative bacterium [GO:0050829]; defense response to Gram-positive bacterium [GO:0050830] Lachesana tarabaevi (Spider) CTX17_LACTA MKYFVVALALVAAFACIAESKPAESEHELAEVEEENELADLEDAVWLEHLADLSDLEEARGFFGNTWKKIKGKADKIMLKKAVKIMVKKEGISKEEAQAKVDAMSKKQIRLYLLKHYGKKALQKASEKL +P85259 reviewed cytolysis by host of symbiont cells [GO:0051838]; defense response to Gram-negative bacterium [GO:0050829]; defense response to Gram-positive bacterium [GO:0050830] Lachesana tarabaevi (Spider) CTX18_LACTA MKYFVVALALVAAFACIAESKPAESEHELAEVEEENELADLEDAVWLEHLADLSDLEEARGFFGNTWKKIKGKADKIMLKKAVKIMVKKEGISKEEAQAKVDAMSKKQIRLYVLKHYGKKALQKASEKL +Q5EK40 reviewed Vibrio cholerae CHXA_VIBCL MYLTFYLEKVMKKMLLIAGATVISSMAHPTFAVEDELNIFDECRSPCSLTPEPGKPIQSKLSIPSDVVLDEGVLYYSMTINDEQNDIKDEDKGESIITIGEFATVRATRHYVNQDAPFGVIHLDITTENGTKTYSYNRKEGEFAINWLVPIGEDSPASIKISVDELDQQRNIIEVPKLYSIDLDNQTLEQWKTQGNVSFSVTRPEHNIAISWPSVSYKAAQKEGSRHKRWAHWHTGLALCWLVPMDAIYNYITQQNCTLGDNWFGGSYETVAGTPKVITVKQGIEQKPVEQRIHFSKGNAMSALAAHRVCGVPLETLARSRKPRDLTDDLSCAYQAQNIVSLFVATRILFSHLDSVFTLNLDEQEPEVAERLSDLRRINENNPGMVTQVLTVARQIYNDYVTHHPGLTPEQTSAGAQAADILSLFCPDADKSCVASNNDQANINIESRSGRSYLPENRAVITPQGVTNWTYQELEATHQALTREGYVFVGYHGTNHVAAQTIVNRIAPVPRGNNTENEEKWGGLYVATHAEVAHGYARIKEGTGEYGLPTRAERDARGVMLRVYIPRASLERFYRTNTPLENAEEHITQVIGHSLPLRNEAFTGPESAGGEDETVIGWDMAIHAVAIPSTIPGNAYEELAIDEEAVAKEQSISTKPPYKERKDELK +Q69CK0 reviewed Ophiophagus hannah (King cobra) (Naja hannah) 3SDC7_OPHHA MKTLLLTLVVVTIVCLDLGYTRKCLNTPLPLIYTTCPIGQDKCVKMTIKKLPSKYDVIRGCIDICPKSSADVEVLCCDTNKCNK +Q9BPE9 reviewed Conus pennaceus (Feathered cone) (Conus episcopus) CTA1_CONPE MRCLPVFVILLLLTASGPSVDAKVHLKTKGDGPLSSFRDNAKSTLQRLQDKSTCCGYRMCVPCG +Q9GV72 reviewed killing of cells of another organism [GO:0031640]; monoatomic ion transport [GO:0006811] Carybdea rastonii (Box jellyfish) JTX21_CARRA MILKHLPWLFIVLAITSAKHGKRSDVNSLLTKVETALKEASGSNEAALEALEGLKGEIQTKPDRVGQATKILGSVGSALGKLNSGDATKIISGCLDIVAGIATTFGGPVGMGIGAVASFVSSILSLFTGSSAKNSVAAVIDRALSKHRDEAIQRHAAGAKRDFAESSAFIQVMKQQSNLTDSDLSIIAANVPVYKFSNFIGQLESRISQGAATTSLSDAKRAVDFILLYCQLVVMRETLLVDLAILYRKGNAEHVASAVENANRVNKELAADTLDFLHKLIPEQALIGAVYHPISASETSKAILNYTKYFGVPDVPRPIGNRRYKFTNSYWNTYSICSEAYMGNYMFRGCSNVRNPNIRVSKMSDGFYTMENSDRRKLYITKHDQGWGWGTLDEDPGDQGHMRFIPLRHGKYMVSSKRWPNWFMYMESSASGYIRSWENNPGPQGHWSIT +A0A2P1BRP3 reviewed killing of cells of another organism [GO:0031640] Scorpaena plumieri (Spotted scorpionfish) CTXB_SCOPL MPSDILVVAALGRPFTLGALYDARKDKLYPGFTLWEHEVLEESTVESDQPSSTFEITASDSIDDKSSLMDIEASLKASFLGGLIEVGGSAKYLNDTKKFKNQSRVTLQYKATTSFKQLMTNLETKHVEYSEYFQNIEATHVVIGILYGANAFFVFDSDKVDSSNVQDIQGSMEAVIKKIPSVEISGQGSVQLTSEESDITNSFSCKFHGDFHLPSNPTTFEDAVKTYQQLPQMMGKETAVPMTVWLVPMTNFYSEAPQLMADSSTPILRKVRNTLEAMRQLDMRCNDSLERRHSEAGFHCLKKKLKTFQKHYERLHVNPFRKNHFPETFSPSGKGTKMKLQCLPTFRNKLRSPSNINSLNMWMDCAEREINVLRSCIDIIEEAKHKVVLSKSQMARELDDSEVKHAVCYVFTYVTDYDPFLNALSDFSKSIKPKKYSPSKKDYWYTSDDVPEMMREKAHHFYNLAKDMENRCVRFLVASIVNPKEEGAAIHYYREGIQIINDFSNPRIPPVETIQDQESYSGMTVSSPWKETAHPALHLSEGNKKAMSGKPQPSDNNPKRFDHYQQVLCNKGLSKRHYWEVEWCGYVRAGITYKGIQRKTFASECSLGHTDMSWVFDYYPKSGYHHIYNNKKVRVKVASPGFDRLGVYLDWPAGTLSFYMVTSTWVTHLHTFSIRFNEAVYPAFLIGHGQKNANGQIKLKGE +A0A2P1BRQ0 reviewed killing of cells of another organism [GO:0031640] Scorpaena plumieri (Spotted scorpionfish) CTXA_SCOPL MSSDIIMAGLGRPFTLGFLYDARREKLIPGFSLFGDETLQKYATSTPQHSSDFQIVASDSVESKSNVMDIEASLGVSFLGGLVEVGGSAKYLNNTKKYQNQSRVTLQYKVTTTFKQFKAPPGKVNVQQTAISDKNVATHVVTAILFGANAFFVFDSDKVEDSNLQDIQGKMEAVIKKIPSVSIEGSGSVQLTDEEKSLASNLSCKFHGDFLLESLPTTFEEAVMTYQKLPELVGEEASDGVPMKVWLVPLTRFYSKADLLVRDISQGLVRKVHSILEDLHKLKRRANDSLEDDTVKLFPLLEKKLKNFQKNYSDYMTTFRRTISQKLQSIRKGDEDETAMLQLFEDRLRSPFNIDSLNMWMEITEREINVLRSCIDIIEETKHKAVLSQSQMVKDLLDSEVMHAVCYVFTYVTDKDHYLDALRDYLKSPNSRPARVRPVVTWVASNTVLETMREKAHLFRSLAKDMENRCVHFLVASIVNLKVEGAAIHYYRESVLIEPNFKHPIISAVEKIVDRRDLLWYDCELTLDPNTSHPSLYLSEGNKKAVTGTLRAFDNNPERFGLWQQVLCNKGLSRRHYWEVEWNGYVIVGVTYSSIGRKNIDIQSFIGFSETSWTLMFIPKNGVAVKGARRSVSYYFISDLAFPPLGLYHDCHASTLSFYKVSDNVLNHFHTIEIKPKLSEPVYAIIRIGDEDRPYHGTVRLL +A0A7T7DMY7 reviewed killing of cells of another organism [GO:0031640] Naja sumatrana (Equatorial spitting cobra) (Naja tripudians var. sumatrana) 3SA1_NAJSU MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMYMVATPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +A7L035 reviewed monoatomic ion transport [GO:0006811] Chironex fleckeri (Australian box jellyfish) JTX11_CHIFL MVKMLFFAFLPLLFMTGIAAESTISSGLNSLKTKIDAKMPSGKQLFDKVVEMQKQIDAKFSNDDERAKVMGAIGSLSTAVGKFQSGDPAKIASGCLDILVGISSVLKDFAKFSPIFSILSLVVGLFSGTKAEESVGSVVKKAVQEQSDQELQEALYGVKREYAVSKAFLDGVRNETSDLSPTEVSALAANVPIYQGVRFIAMVVQRIKYIKPKTESEIKRMLTMLELFTDLCSLRDLILLDLYQLVATPGHSPNIASGIKEVSNLGREEYKKVFEDLLKNDDKETYLFLSYLYPREKNEQSRKIFNFFDLMKVKYDDRLKQDLTGVKIFSNVHWPNYFMCSSNDYLALICTKPYGSLKLDKLNDGYYSIKTTQHDPKICHRYGNYILFTHKRNDDLEKFNFVPVKLEKREIYLLSSKESPNKFAYVPQNADGALFFVDGIPSKVGYGNQGYFTLVE +A7L036 reviewed monoatomic ion transport [GO:0006811] Chironex fleckeri (Australian box jellyfish) JTX12_CHIFL MILVSLLPLLFMTGIASESTISSGLASLKAKIDIKKPTGKQLFDKVKSMEQALENKFSDDDERAKVMGAIGSLGTAIGKFQSGDPASIASGCLDILVGISSVLKDFAKFSPVFSILSLVVGLFSGTKAEESVSSVVTKAIQEQSDQELQEALYGVKREFAVSKAFLDGVRNEESDLRPTEVSALAANIPVYQGVRFIAMVVQRIKYIKPKTESEIKRMLTMLELFTDLCSIRDLILLDLHQLIATPGHSPNIASGIKEVTSLGREEYQRVFEDLLKTDDEETFLFLSYLYPKEKNEQSRKIFKFFDLIEVKYDDRFKLDLSGGQALSTLQWPNYYLCPHNDYLANNCHDLRVGLKLEKLSDGFYTIKTYGRDPRTCYWTDDYVKISSTSNGELEKFSFVPVQVKGQKAYLLSTKKWPHNFAYSQKTANGLLSILKDVPSKLGYGNQGFFTISTYSNPKNRHA +C0HJV3 reviewed defense response to bacterium [GO:0042742] Lachesana tarabaevi (Spider) CTX21_LACTA SWDSIWKSAKNKMDKIMRQKVAKWMAKKEGKSVEEVQAKVDAMSKKDIRLHVISHYGKKAFEQLSKSLE +C0HJV4 reviewed defense response to bacterium [GO:0042742] Lachesana tarabaevi (Spider) CTX23_LACTA SWDSIWKSAKNKMDKIMRQKVAKWMAKKEGKSVEEVQAKVDAMSKKDIRMHVISHYGKKAFEQLSKSLEE +C0HJV5 reviewed defense response to bacterium [GO:0042742] Lachesana tarabaevi (Spider) CTX3_LACTA KSKWGKLWGKAKGVAKKGAKKVKKALLKALKKKLAKEMAKSKGVDYKEMKAKVMAMDKAKVLQEAMKILGKKAMDKYLS +C7FPW8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C7FPW8_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNTIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +D0PX84 reviewed Conus imperialis (Imperial cone) CANA_CONIM MIMRMTLTLFVLVVMTAASASGDALTEAKRIPYCGQTGAECYSWCIKQDLSKDWCCDFVKDIRMNPPADKCP +D0PX85 reviewed Conus imperialis (Imperial cone) CKNB_CONIM MIMRMTLTLFVLVVMTAASASGDALTEAKRIPYCGQTGAECYSWCIKQDLSKDWCCDFVKTIARLPPAHICSQ +L8BU87 reviewed Conus lividus (Livid cone) CA1A_CONLI FRGRDAAAKASGLVGLTDRRGCCSHPACNVDHPEICG +O73856 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA4B_NAJSP MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMYMVAMPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +O73857 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA5A_NAJSP MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMFMVSNLTVPVKRGCIDVCPKNSALVKYVCCNTDRCN +O93471 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA1_NAJSP MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKIFMVATPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +O93472 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA2C_NAJSP MKTLLLTLVLVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMYMVATPKVPVKRGCIDVYPKSSLLVKYVCCNTDRCN +O93473 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA4A_NAJSP MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMFMVAMPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +P01468 reviewed killing of cells of another organism [GO:0031640] Naja pallida (Red spitting cobra) 3SA1_NAJPA LKCNQLIPPFWKTCPKGKNLCYKMTMRAAPMVPVKRGCIDVCPKSSLLIKYMCCNTDKCN +P01470 reviewed killing of cells of another organism [GO:0031640] Naja mossambica (Mozambique spitting cobra) 3SA3_NAJMO LKCNRLIPPFWKTCPEGKNLCYKMTMRLAPKVPVKRGCIDVCPKSSLLIKYMCCNTNKCN +P06886 reviewed Staphylococcus aureus TSST_STAAU MNKKLLMNFFIVSPLLLATTATDFTPVPLSSNQIIKTAKASTNDNIKDLLDWYSSGSDTFTNSEVLDNSLGSMRIKNTDGSISLIIFPSPYYSPAFTKGEKVDLNTKRTKKSQHTSEGTYIHFQISGVTNTEKLPTPIELPLKVKVHGKDSPLKYGPKFDKKQLAISTLDFEIRHQLTQIHGLYRSSDKTGGYWKITMNDGSTYQSDLSKKFEYNTEKPPINIDEIKTIEAEIN +P09978 reviewed killing of cells of another organism [GO:0031640]; sphingomyelin metabolic process [GO:0006684] Staphylococcus aureus PHLC_STAAU MVKKTKSNSLKKVATLALANLLLVGALTDNSAKAESKKDDTDLKLVSHNVYMLSTVLYPNWGQYKRADLIGQSSYIKNNDVVIFNEAFDNGASDKLLSNVKKEYPYQTPVLGRSQSGWDKTEGSYSSTVAEDGGVAIVSKYPIKEKIQHVFKSGCGFDNDSNKGFVYTKIEKNGKNVHVIGTHTQSEDSRCGAGHDRKIRAEQMKEISDFVKKKNIPKDETVYIGGDLNVNKGTPEFKDMLKNLNVNDVLYAGHNSTWDPQSNSIAKYNYPNGKPEHLDYIFTDKDHKQPKQLVNEVVTEKPKPWDVYAFPYYYVYNDFSDHYPIKAYSK +P0DJY7 reviewed Streptococcus pyogenes SPEA_STRPY MENNKEVLKKMVFFVLMKFLGLTILPKGICSTRPKPSQLQRSNLVKTFKIYIFFMRVTLVTHENVKSVDQLLSHDLIYNVSGPNYDKLKTELKNQEMATLFKDKNVDIYGVEYYHLCYLCENAERSACLYGGVTNHEGNHLEIPKKIVVKVSIDGIQSLSFDIEQIKNGNCSRISYTVRKYLTDNKQLYTNGPSKYETGYIKFIPKNKESFWFDFFPEPEFTQSKYLMIYKDNETLDSNTSQIEVYLTTK +P0DQY6 reviewed Conus litoglyphus (Lithograph cone) CU1A_CONLO LFHYCQCQCPPGFKGKFCQFKLRPP +P0DQZ0 reviewed Conus miliaris (Thousand-spot cone) CX1A_CONML CHFWVCP +P15222 reviewed Mesobuthus eupeus (Lesser Asian scorpion) (Buthus eupeus) CTX5A_MESEU MCMPCFTTDPNMAKKCRDCCGGNGKCFGPQCLCNR +P25517 reviewed killing of cells of another organism [GO:0031640] Naja mossambica (Mozambique spitting cobra) 3SA5_NAJMO LKCKKLIPLFSKTCPEGKNLCYKMTMRLAPKVPVKRGCIDVCPKSSFLVKYECCDTDRCN +P43261 reviewed cell adhesion [GO:0007155] Escherichia coli O157:H7 EAE_ECO57 MITHGCYTRTRHKHKLKKTLIMLSAGLGLFFYVNQNSFANGENYFKLGSDSKLLTHDSYQNRLFYTLKTGETVADLSKSQDINLSTIWSLNKHLYSSESEMMKAAPGQQIILPLKKLPFEYSALPLLGSAPLVAAGGVAGHTNKLTKMSPDVTKSNMTDDKALNYAAQQAASLGSQLQSRSLNGDYAKDTALGIAGNQASSQLQAWLQHYGTAEVNLQSGNNFDGSSLDFLLPFYDSEKMLAFGQVGARYIDSRFTANLGAGQRFFLPANMLGYNVFIDQDFSGDNTRLGIGGEYWRDYFKSSVNGYFRMSGWHESYNKKDYDERPANGFDIRFNGYLPSYPALGAKLIYEQYYGDNVALFNSDKLQSNPGAATVGVNYTPIPLVTMGIDYRHGTGNENDLLYSMQFRYQFDKSWSQQIEPQYVNELRTLSGSRYDLVQRNNNIILEYKKQDILSLNIPHDINGTEHSTQKIQLIVKSKYGLDRIVWDDSALRSQGGQIQHSGSQSAQDYQAILPAYVQGGSNIYKVTARAYDRNGNSSNNVQLTITVLSNGQVVDQVGVTDFTADKTSAKADNADTITYTATVKKNGVAQANVPVSFNIVSGTATLGANSAKTDANGKATVTLKSSTPGQVVVSAKTAEMTSALNASAVIFFDQTKASITEIKADKTTAVANGKDAIKYTVKVMKNGQPVNNQSVTFSTNFGMFNGKSQTQATTGNDGRATITLTSSSAGKATVSATVSDGAEVKATEVTFFDELKIDNKVDIIGNNVRGELPNIWLQYGQFKLKASGGDGTYSWYSENTSIATVDASGKVTLNGKGSVVIKATSGDKQTVSYTIKAPSYMIKVDKQAYYADAMSICKNLLPSTQTVLSDIYDSWGAANKYSHYSSMNSITAWIKQTSSEQRSGVSSTYNLITQNPLPGVNVNTPNVYAVCVE +P45639 reviewed Leiurus quinquestriatus quinquestriatus (Egyptian scorpion) (Deathstalker scorpion) CTXL_LEIQU MCMPCFTTDHQMARKCDDCCGGKGRGKCYGPQCLCR +P60302 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA3_NAJSP MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMFMVATPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +P69178 reviewed hemolysis by symbiont of host erythrocytes [GO:0019836]; modulation of host virulence by virus [GO:0098676] Bacteriophage H30 STXB_BPH30 MKKTLLIAASLSFFSASALATPDCVTGKVEYTKYNDDDTFTVKVGDKELFTNRWNLQSLLLSAQITGMTVTIKTNACHNGGGFSEVIFR +P82601 reviewed Coremiocnemis valida (Singapore tarantula) (Blue femur tarantula) NTA_CORVA ACSRAGENCYKSGRCCDGLYCKAYVVTCYKP +P85247 reviewed cytolysis by host of symbiont cells [GO:0051838]; defense response to Gram-negative bacterium [GO:0050829]; defense response to Gram-positive bacterium [GO:0050830] Lachesana tarabaevi (Spider) CT111_LACTA MKYFVVALALVAAFACIAESKPAESEHELAEVEEENELADLEDAVWLEDLADLSDLEETRGFFGNAWKKIKGKAEKFFRKKAAKIIAKKEGITKEEAEAKVDTMSKKQIKVYLLKHYGKKALQKASEKL +P85254 reviewed defense response to Gram-negative bacterium [GO:0050829]; defense response to Gram-positive bacterium [GO:0050830]; killing of cells of another organism [GO:0031640] Lachesana tarabaevi (Spider) CTX12_LACTA MKYFVVALALVAAFACIAESKPAESEHELAEVEEENELADLEDAVWLEHLADLSDLEEARGFFGNTWKKIKGKADKIMLKKAVKLMVKKEGISKEEAQAKVDAMSKKQIRLYLLKYYGKKALQKASEKL +P85255 reviewed defense response to Gram-negative bacterium [GO:0050829]; defense response to Gram-positive bacterium [GO:0050830]; killing of cells of another organism [GO:0031640] Lachesana tarabaevi (Spider) CTX13_LACTA MKYFVVALALVAAFACIAESKPAESEHELAEVEEENELADLEDAVWLEHLADLSDLEEARGFFGNTWKKIKGKADKIMLKKAVKIMVKKEGISKEEAQAKVDAMSKKQIRLYVLKYYGKKALQKASEKL +P85256 reviewed defense response to Gram-negative bacterium [GO:0050829]; defense response to Gram-positive bacterium [GO:0050830]; killing of cells of another organism [GO:0031640] Lachesana tarabaevi (Spider) CTX14_LACTA MKYFVVALALVAAFACIAESKPAESEHELAEVEEENELADLEDAVWLEHLADLSDLEEARGFFGNTWKKIKGKADKIMLKKAVKIMVKKEGITKEEAQAKVDAMSKKQIRLYLLKYYGKKALQKASEKL +P85257 reviewed defense response to Gram-negative bacterium [GO:0050829]; defense response to Gram-positive bacterium [GO:0050830]; killing of cells of another organism [GO:0031640] Lachesana tarabaevi (Spider) CTX15_LACTA MKYFVVALALVAAFVCIAESKPAESEHELAEVEEENELADLEDAVWLEHLADLSDLEEARGFFGNTWKKIKGKSDKIMLKKAVKIMVKKEGISKEEAQAKVDAMSKKQIRLYLLKYYGKKALQKASEKL +Q02454 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA0_NAJSP MKTLLLTTVVVTIVCLDLEYTLKCNKLVPLFYKTCPAGKNLCYKMFMVATPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +Q2ACF1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 Q2ACF1_ECO57 MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q32GM1 reviewed negative regulation of translation [GO:0017148] Shigella dysenteriae serotype 1 (strain Sd197) STXA_SHIDS MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q47636 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47636_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q47640 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47640_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSVNAILGSVALILNCHHHASRVARIVPNEFPSMCPVDGRVRGITHNKILWDSSTLGAILIRRAISS +Q779K4 reviewed modulation of host virulence by virus [GO:0098676]; negative regulation of translation [GO:0017148] Shigella phage 7888 (Shigella sonnei bacteriophage 7888) STXA_BP788 MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q7DB66 unreviewed actin filament network formation [GO:0051639]; adhesion of symbiont to host epithelial cell [GO:0044651]; positive regulation of actin filament polymerization [GO:0030838]; protein secretion by the type III secretion system [GO:0030254] Escherichia coli O157:H7 Q7DB66_ECO57 MKKHIKNLFLLAAICLTVACKEQLYTGLTEKEANQMQALLLSNDVNVSKEMDKSGNMTLSVEKEDFVRAITILNNNGFPKKKFADIEVIFPPSQLVASPSQENAKINYLKEQDIERLLSKIPGVIDCSVSLNVNNNESQPSSAAVLVISSPEVNLAPSVIQIKNLVKNSVDDLKLENISVVIKSSSGQDG +Q7DI68 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 Q7DI68_ECO57 MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q83XK3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q83XK3_ECOLX MKIMIFRALTFFFVIFSVNAIAKEFTLDFSTAKKYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDIMGLEPEEERFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTRAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSYSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSILPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMTPDEFPSMCPTDGSGRGITHNKILWDSSTLGAILIRRTISS +Q8I6R5 reviewed Conus imperialis (Imperial cone) CA12_CONIM IVRRACCSDRRCRWRCG +Q8KTU8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8KTU8_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q8KTV6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8KTV6_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNTIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q8NKX2 reviewed Streptococcus pyogenes serotype M18 (strain MGAS8232) SPEC_STRP8 MKKINIIKIVFIITVILISTISPIIKSDSKKDISNVKSDLLYAYTITPYDYKDCRVNFSTTHTLNIDTQKYRGKDYYISSEMSYEASQKFKRDDHVDVFGLFYILNSHTGEYIYGGITPAQNNKVNHKLLGNLFISGESQQNLNNKIILEKDIVTFQEIDFKIRKYLMDNYKIYDATSPYVSGRIEIGTKDGKHEQIDLFDSPNEGTRSDIFAKYKDNRIINMKNFSHFDIYLEK +Q8VLD2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8VLD2_ECOLX MKCILLKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +Q8X696 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8X696_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q8X987 reviewed anaerobic glutamate catabolic process [GO:0019670]; glutamate catabolic process via L-citramalate [GO:0019553] Escherichia coli O157:H7 GLME_ECO57 MELRNKKLTHDEFMTERQQVLKTWETGKDVENFEDGVKYQQTIPEHKRFSLALLKADKEGKTLSQPRAGVALMDEHIELLKTLQEECDLLPSTIDAYTRLNRYEEAAVGIKKSIEAGTSKLNGLPVVNHGVAACRRLTETLQKPLQIRHGTPDARLLAEISMASGFTSYEGGGISYNIPYAKRVTLEKSIRDWQYCDRLMGMYEEHGIRINREPFGPLTGTLIPPFISHSIAIIEGLLALEQGVKSITVGYGQVGSLTQDVAAIQSLRELAHEYFQSYGYTDYELSTVFHQWMGGFPEDESKAFAIISWGAAVAGMSGATKVITKSPHEAWGIPTAAANIQGLKASRQMLNMVNEQKFPPCPAVELEIELIKSEVRAVLNKVFELGNGDIARGTVLAFEAGVLDVPFAPAACNAGKILPVRDNTGAIRVLEAGAVPLPKDILDLHHDYVAERARCEGRQPTFQMVVDDINAVSHSKLIGRP +Q8XBV2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8XBV2_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q91124 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SA8B_NAJAT MKTLLLTLVVVTIVCLDLGYTLKCNKLIPIASKTCPAGKNLCYKMFMVATPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +Q98959 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SA3A_NAJAT MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMFMVATPKVPVKRGCIDVCPKNSLLVKYVCCNTDRCN +Q9PST4 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA2A_NAJSP MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMYMVATPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +A0A060PSK2 unreviewed Helicobacter pylori NY40 A0A060PSK2_HELPX MEIQQTHRKMNRPLVSLVLAGALISAIPQESHAAFFTTVIIPAIVGGIATGTAVGTVSGLLSWGLKQAEEANKTPDKPDKVWRIQAGKGFNEFPNKEYDLYKSLLSSKIDGGWDWGNAARHYWVKGGQQNKLEVDMKDAVGTYKLSGLRNFTGGDLDVNMQKATLRLGQFNGNSFTSYKDSADRTTRVDFNAKNISIDNLVEINNRVGSGAGRKASSTVLTLQASEKITSRENAEISLYDGATLNLASSSVKLNGNVWMGRLQYVGAYLAPSYSTINTSKVTGEVDFNHLTVGDKNAAQAGIIASNKTHIGTLDLWQSAGLNIIAPPEGGYKNQTNNTPSQSGAKNDKNESAKNDKQESSQNNSNTQVINPPNNTQKTEIQPTQVIDGPFAGGKNTVVNIDRINTKADGTIKVGGFKASLTTNAAHLNIGKGGVNLSNQASGRTLLVENLTGNITVDGPLRVNNQVGGYALAGSSANFEFKAGVDTKNGTATFNNDISLGRFVNLKVDAHTANFKGIDTGNGGFNTLDFSGVTGKVNINKLITASTNVAVKNFNINELIVKTNGISVGEYTHFSEDIGSQSRINTVRLETGTRSIFSGGVKFKSGEKLVIDEFYYSPWNYFDARNVKNVEITRKFASSTPENPWGTSKLMFNNLTLGQNAVMDYSQFSNLTIQGDFINNQGTINYLVRGGQVATLNVGNAAAMMFSNDIDSTTGFYKPLIKINSAQDLIKNTEHVLLKAKIIGYGNVSTGTNGISNVNLEEQFKERLALYNNNNRMDTCVVRNTDDIKACGMAIGNQSMVNNPDNYKYLIGKAWRNIGISKTANGSKISVYYLGNSTPTENGGNTTNLPTNTTSNVRSANYALVKNAPFAHSATPNLVAINQHDFGTIESVFELANRSKDIDTLYTHSGAQGRDLLQTLLIDSHDAGYARQMIDNTSTGEITKQLNAATTTLNNIASLEHKTSGLQTLSLSNAMILNSRLVNLSRKHTNHINSFAQRLQALKGQRFASLESAAEVLYQFAPKYEKPTNVWANAIGGASLNSGSNASLYGTSAGVDAYLNGEVEAIVGGFGSYGYSSFSNQANSLNSGANNANFGVYSRFFANQHEFDFEAQGALGSDQSSLNFKSALLRDLNQSYNYLAYSATTRASYGYDFAFFRNALVLKPSVGVSYNHLGSTNFKSNSNQKVALSNGSSSQHLFNASANVEARYYYGDTSYFYMNAGVLQEFARFGSNDVASLNTFKINAARSPLSTYARAMMGGELQLAKEVFLNLGVVYLHNLISNASHFASNLGMRYSF +A0A075M2Z2 unreviewed negative regulation of translation [GO:0017148] Escherichia Stx1-converting recombinant phage HUN/2013 A0A075M2Z2_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A088CE58 unreviewed negative regulation of translation [GO:0017148] Shigella phage POCJ13 A0A088CE58_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A096XEM7 unreviewed negative regulation of translation [GO:0017148] Escherichia phage phi191 A0A096XEM7_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0A6ZRF4 unreviewed negative regulation of translation [GO:0017148] Shigella dysenteriae 1617 A0A0A6ZRF4_SHIDY MCNMKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A0A7LU38 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0A7LU38_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDVRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0A7LYE3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0A7LYE3_ECOLX MKCILFKSVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNSLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0A8U9B8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O26:H11 A0A0A8U9B8_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0B0U7Z2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0B0U7Z2_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0D5CU54 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0D5CU54_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSPIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRSTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A0D5CU87 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0D5CU87_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTVVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRSTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A0D5CU99 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0D5CU99_ECOLX MKCILFKWELCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILSTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0D5CUB0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0D5CUB0_ECOLX MKCILFKWELCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0D5CUJ9 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0D5CUJ9_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A0A0D5CVE8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0D5CVE8_ECOLX MKCILFKWELCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0D5CVM7 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0D5CVM7_ECOLX MKCILFKWELCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0E0Y1D0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O104:H4 (strain 2011C-3493) A0A0E0Y1D0_ECO1C MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0H2Z8B0 unreviewed modulation of molecular function in another organism [GO:0044359] Pseudomonas aeruginosa (strain UCBPP-PA14) A0A0H2Z8B0_PSEAB MHLIPHWIPLVASLGLLAGGSFASAAEEAFDLWNECAKACVLDLKDGVRSSRMSVDPAIADTNGQGVLHYSMVLEGGNDALKLAIDNALSITSDGLTIRLEGGVEPNKPVRYSYTRQARGSWSLNWLVPIGHEKPSNIKVFIHELNAGNQLSHMSPIYTIEMGDELLAKLARDATFFVRAHESNEMQPTLAISHAGVSVVMAQTQPRREKRWSEWASGKVLCLLDPLDGVYNYLAQQRCNLDDTWEGKIYRVLAGNPAKHDLDIKPTVISHRLHFPEGGSLAALTAHQACHLPLETFTRHRQPRGWEQLEQCGYPVQRLVALYLAARLSWNQVDQVIRNALASPGSGGDLGEAIREQPEQARLALTLAAAESERFVRQGTGNDEASADVVSLTCPVAAGECAGPADSGDALLERNYPTGAEFLGDGGDVSFSTRGTQNWTVERLLQAHRQLEERGYVFVGYHGTFLEAAQSIVFGGVRARSQDLDAIWRGFYIAGDPALAYGYAQDQEPDARGRIRNGALLRVYVPRSSLPGFYRTGLTLAAPEAAGEVERLIGHPLPLRLDAITGPEEEGGRLETILGWPLAERTVVIPSAIPTDPRNVGGDLDPSSIPDQEQAISALPDYASQPGKPSREDLK +A0A0H3PFA2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 (strain EC869) A0A0H3PFA2_ECO5C MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A0H3PKI8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 (strain EC869) A0A0H3PKI8_ECO5C MPSKQSVTGSNPVQRATLIFPGSLLRAFFISAPGLVLITSAKRNTLYMKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0H4IPK4 unreviewed negative regulation of translation [GO:0017148] Shigella phage Ss-VASD A0A0H4IPK4_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A0N6WEJ3 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA2 A0A0N6WEJ3_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N6WEM8 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA11 A0A0N6WEM8_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N6WET4 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA29 A0A0N6WET4_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7BST0 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA8 A0A0N7BST0_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7BUH0 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA36 A0A0N7BUH0_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7BUP9 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA42 A0A0N7BUP9_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7BV90 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA50 A0A0N7BV90_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7BYZ7 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA4 A0A0N7BYZ7_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C0J1 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA44 A0A0N7C0J1_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C0S8 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA45 A0A0N7C0S8_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C165 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA16 A0A0N7C165_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C1I2 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA21 A0A0N7C1I2_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C1K7 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA5 A0A0N7C1K7_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C1Q4 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA27 A0A0N7C1Q4_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C1X2 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA28 A0A0N7C1X2_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C279 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA18 A0A0N7C279_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C2B6 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA30 A0A0N7C2B6_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C2M7 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA33 A0A0N7C2M7_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C369 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA32 A0A0N7C369_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7C3R7 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA52 A0A0N7C3R7_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7CH17 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA12 A0A0N7CH17_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7CH92 unreviewed negative regulation of translation [GO:0017148] Escherichia phage PA51 A0A0N7CH92_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N7KZ88 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_F403 A0A0N7KZ88_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0N9ZJ91 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0N9ZJ91_ECOLX MKIMIFRALTFFFVIFSVNAIAKEFTLDFSTAKKYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGPGNNLFAVDIMGLEPEEERFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTRAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSYSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSILPDYHGQDSVRVGRISFGSINAIVGSVALIINCHHHASRVARMTPDEFPSMCPTDGSGRGITHNKILWDSSTLGAILIRRTISS +A0A0P0ZAW5 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_F451 A0A0P0ZAW5_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZC66 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_F422 A0A0P0ZC66_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZCQ1 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_F765 A0A0P0ZCQ1_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZCW8 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_F723 A0A0P0ZCW8_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZDD2 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_F349 A0A0P0ZDD2_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZDH0 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_WGPS2 A0A0P0ZDH0_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPSVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZDL5 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_WGPS9 A0A0P0ZDL5_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZDQ7 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_1447 A0A0P0ZDQ7_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPSVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZE66 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_WGPS8 A0A0P0ZE66_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZE70 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_WGPS6 A0A0P0ZE70_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZEJ1 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a_WGPS4 A0A0P0ZEJ1_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZFK0 unreviewed negative regulation of translation [GO:0017148] Escherichia phage P13803 A0A0P0ZFK0_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZFK8 unreviewed negative regulation of translation [GO:0017148] Escherichia phage P14437 A0A0P0ZFK8_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZGB1 unreviewed negative regulation of translation [GO:0017148] Escherichia phage P13771 A0A0P0ZGB1_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A0P0ZH05 unreviewed negative regulation of translation [GO:0017148] Escherichia phage P8983 A0A0P0ZH05_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A125RPW6 unreviewed negative regulation of translation [GO:0017148] Escherichia phage phiON-2011 A0A125RPW6_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A161GX06 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A161GX06_ECOLX LGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A0A161GX98 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A161GX98_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A161H051 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A161H051_ECOLX LLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A0A161H076 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A161H076_ECOLX LLLGFSSVSYSREFMIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +A0A161H089 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A161H089_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +A0A166G7N2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A166G7N2_ECOLX LLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +A0A166G9G5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A166G9G5_ECOLX LLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A166G9K6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A166G9K6_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDAPRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A0A166G9L3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A166G9L3_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPERQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A0A1B5FPE4 unreviewed negative regulation of translation [GO:0017148] Escherichia phage phi467 A0A1B5FPE4_9CAUD MRYILLKLVLFFCVCLSSASYADEFTVDFSSQKSYVDSLNSIRSAISTPLGNISQGGVSVSVINHVPGGNYISLNVRGLDPYSERFNHLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQDLTEPNQ +A0A1L9QK23 unreviewed Roseofilum reptotaenium AO1-A A0A1L9QK23_9CYAN MRYLLDTCVISDFIKGEVGTQIRLKQTPPADIAVSVITVMELRYGLALNHQRAQKVEPAIASFLASVTILPLNQPEAEQAAQIRAMLKTQGQPIGAYDILIAATALHHNLLMVTANQREFDRVSGLQTENWRGF +A0A1L9QKW2 unreviewed Roseofilum reptotaenium AO1-A A0A1L9QKW2_9CYAN MAIIQGTNNSELLEGTAQNDVITGLFGNDTLQGLAGNDFLNGGRDNDSIEGGDNDDNLIGDAGNDTLKGGEGNDLLDGGTENDELQGENGNDTLSGGDNDDTLLGGEGDDLLDGGHWPDSLDGGAGNDTLLGGTGVDTLDGKDGNDSLVGGEQEDTLIGGAGNDTQTGDAGNDTIQGDAGADSLLGGTGNDQITGGTENDYLNGEDGNDSILGGDGNDIVLGGTGNDTLEGEAGTDSLEGGTENDSILGGADNDTLKGGAGVDTLQGGTGDDSLLGEDDGDQLDGGDGNDYLDGGAGQNAVSGDAGNDTLVAGIDATSSTFDGGANTDWLSFAQATVGFTIDLSAGTATDTNSTISYTIQNIEAVLGSAQADVITGGTSGSLTLNAGAGNDTYNLNKTNGAGTVINDVAGTDTLSISDATQLAITTPGQTATTGDVGVFRESETSTSLIIDLNNDGAFNAANDLKIDNYFSTTEGIAGGIGVIETVGNLTSTAILAAFPTPNPTPTPTPTPTPTPTPTPTPTPGITDPVAPPSSSPTPTPTPTPGLNPVPGTSGDDLLTGTTGSDSLQGLDGNDQLYGLGGDDVMTGDTGNDIVSGNTGNDQISGGIGNDTGFGGQGNDTVNGDEGDDSLMGDRGNDTVSGGTGNDTSFGGQGNDSLIGGDGNDVLSGDFGTDTLLGGAGNDLFVLRTSTAVATVAQADLIQDYQVGVDIIGLTGNLTPAALTATINGNNTVLSITATNQILGVLTGQFTLQQLTFVSVQLPNGLI +A0A1L9QPU8 unreviewed Roseofilum reptotaenium AO1-A A0A1L9QPU8_9CYAN MSFLIDTNILLRSADPNHPMHADAVRATNILLDRGEDVCIIAQNLIEFWNVYTRPADRNGLGYTPAEAAAEVNRLKGIFSLLPDTLAIYTEWERLVRTYEVRGVNVHDARLVAAMVVHGLTHILTFNTRDFARYAEVTVVQPMEMRSPPAE +A0A1L9QQB2 unreviewed Roseofilum reptotaenium AO1-A A0A1L9QQB2_9CYAN MGYLLDTNIITALVKRNSRVVTRLRYIEGQGAKIYMSAVSYYEVKRGLLYINATRQLANLEILCERIPLLYLDNLNIFQKAVEIHANLRRKGEPMEDADILIAATAIAHNLILVSHDSDMQRVKNLMLYQVR +A0A1L9QSJ4 unreviewed Roseofilum reptotaenium AO1-A A0A1L9QSJ4_9CYAN MIYLLDTNVIVTYLNNRSTAIRERLQQIPIQDICVCSIVKAELFYGANKSNNPVKTLFRKKDFLSEFISLPFDDHSAEVYGSIRADLERKGTPIGAYDLQIAAIAIAYNLTLATHNTREFARIDRLSYEDWNA +A0A1L9QWG7 unreviewed Roseofilum reptotaenium AO1-A A0A1L9QWG7_9CYAN MALNPGDIAFVQYNADNPDNFAFVALVNIADSESILFTDNGWQSNNTFRANENTITWTPPAGGITAGTVVTITGTTASSGTLSATTPALSTSGDQILAYQGASDFIAAINNEGTTGVWQADATSSNTSALPQGLTNGTNAVALIEIDNAQYVGTTSGTQAELLAAINNSANWTGSNSTAQTFSGSFTVSGSPSPSPSPAPSITSIHDIQGSSAASALDGNTVTIEGIVVGDFQDGAGAGTNGDLNGFFVQEEDADADGNAVTSEGIFVFDGFSPAVNVQVGDKVQVTGEVEEFFGLTELTNVSNVTVVSSGNTLPTATDISLPVASTVTNADGELIPDLEQYEGMLVRFTDTLSVTEMFQLDRFGELRVSQGGRLEQFTQNNAPNVAGFATHQQDIARRNIVIDDGQTVQNPDPIIFPDGSLSTADGFRMGDTVTNLTGVVNFSRGSGGSGDQNYRIHPTVAPTFVSTNPRPATPNAVGGSLKVAGFNVLNYFTTLDEGSNTTANGNNPRGADNATEFTRQTEKLVTALSTLDADIVGLVEVENDFQAGSSGNAIENLVNELNASVGAGTYDWVNPGTQFVGDDVIAVGFIYKPSQVSLSGNSSILNTAAFLDPNNSGQNRNRAALAQTFTETTSGESFTAVVNHFKSKGSSGLTDTSNPDFDQLDGQGFWSDTRADAATALANWVNTDPTNSGDSDFLILGDLNAYAQEDAITNLENAGYTDLAQQFLGSDAYSFVFDGQTGTLDYALANSSLLSQVTGTTEWHVNADEPDAIDYNTDFGRNTGIFDGSVPFRNSDHDPVIVGLNLGASAPTPTPTPTPTPTPTPTPTPGNGTVEISLNAEQVAAVVAALDNPPPFPSPNLASVTITGINTFDLLLGDNQNETFNARGGPDIVVAGGGDDNVNGEAGADLLIGNQGRDIISGDAGNDVIFGGQGDDAARGGDGGDLVSGDRGNDLLQGNGGNDQLFGGSGNDNLNGGPGNDLAYGGRGDDLLRGGQNDDILIGNLGNDGLRGDAGNDRLLGNQGADYLDGGVGNDTLRGGRDSDTLLGGTGDDLLYGDIGNDSLVGGVGSDVFVLQNGRGTDRIADFGTGDRIGLAGGLTVQQLQITQTGANVTLSFGNQILAVVEQTNVSALTPDLFLSV +A0A1U9AJ92 unreviewed negative regulation of translation [GO:0017148] Stx1 converting phage AU5Stx1 A0A1U9AJ92_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A1U9AJL1 unreviewed negative regulation of translation [GO:0017148] Stx1 converting phage AU6Stx1 A0A1U9AJL1_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A1X9WXH5 unreviewed negative regulation of translation [GO:0017148] Shigella sonnei A0A1X9WXH5_SHISO MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A290GF73 unreviewed Klebsiella pneumoniae A0A290GF73_KLEPN MLDTNICSFIMREQPESVLKRLEQAVLRGDRIVVSAVTYAEMRFGATGPKASPRHIQLVDAFCARLDAILPWDRAAVDATTDIRVALRLAGTPIGPNDTAIAGHAIAAGAILVTNNTREFERVPALVLEDWVK +A0A292G3G9 unreviewed negative regulation of translation [GO:0017148] Escherichia albertii A0A292G3G9_ESCAL MRHILLKLVLFFCVCLSSASYADEFTVDFSSQKSYVDSLNSIRSAISTPLGNISQGGVSVSVINHVPGGNYISLNVRGLDPYSERFNHLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQDLTEPNQ +A0A2R2X2B1 unreviewed negative regulation of translation [GO:0017148] Escherichia phage ArgO145 A0A2R2X2B1_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A2S0M3H8 unreviewed negative regulation of translation [GO:0017148] Escherichia phage SH2026Stx1 A0A2S0M3H8_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A2S5V1P6 unreviewed Pseudoclavibacter sp. AY1F1 A0A2S5V1P6_9MICO MIVVDTNVLSEPLRREPDRRVLEWLARNAGQLVLTTITIAELRYGAMRLPEGTRRTALLLAVDRLVDEAGDRAVPFDVEAAHAYAAIRSMREARGRSISVEDTMIAAICRSRGYALATRNIRDFDDVGLSLFDPWSAQDLHRDD +A0A2S5W0H7 unreviewed Subtercola sp. Z020 A0A2S5W0H7_9MICO MIVLDTNVLSEPLRRQPDDAVLEWLQVAAGEAAITSISVGELLVGARRLPVGRRRDALLSAVENIVDNFGAEILAYDSRAATAYAALQERRAAMGRPLSVEDGMIAAICVSAGASLATRNTKDFVGLGLDLIDPWANV +A0A2S5W1E4 unreviewed Subtercola sp. Z020 A0A2S5W1E4_9MICO MGVIYLDSCVVIHAVEDDGPIGQGMRQRLADLGDEQAAISSLVKLECLVGPLRSGDLALADHYRRAFEQFALVEVGDAQYIRAAELRARHGIKTPDALHLAAAQGAGCRQLWTADSRLAAVSHGLAVDLLQAAEKTPDDRL +A0A2S5WT61 unreviewed Pseudoclavibacter sp. RFBB5 A0A2S5WT61_9MICO MIVVDTNVLSEPLRREPDRRVLEWLALNAGQLALTTITIAELRYGAVRLPEGSRRTALLLAVDRLVAEAGDRVCSFDVEAARAYAAIRSEREARGRSISVEDTMIASICLARGYALATRNVGDFDDVGLSLVDPWA +A0A2S5X2M8 unreviewed Pseudoclavibacter sp. RFBA6 A0A2S5X2M8_9MICO MIVVGTDALSEPLRREPDRRVLEWLALNAGQLVLTTIAIAELRYGAVRLPEGSRRTALLLAVDRLVAEAGDRVCSFDVEAARAFAAIRSEREARGRSISVEDTMIASICSARGYALATRNVRDFDDVGLSLVDP +A0A2T2XUX2 unreviewed Kluyvera genomosp. 2 A0A2T2XUX2_9ENTR MNKTYMLDTCICSFIMREQPEAVLKRLEQAVLRGQRIVVSAITYSEMRFGATGPKASPRHVELVDAFCARLDAILPWDRAAVDATTEIKVALRMAGTPIGPNDTAIAGHAIAAGAVLVTNNTREFERVPGLVLEDWVR +A0A2T3SSX2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A2T3SSX2_ECOLX MKCILLKWILCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIVEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +A0A2Z5U1L4 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O145 A0A2Z5U1L4_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTKGK +A0A2Z5U1S3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O145 A0A2Z5U1S3_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A3T0T504 unreviewed Rathayibacter festucae DSM 15932 A0A3T0T504_9MICO MRYLLDTNVLIEAKNRYYAFDLAPGFWEWLERDAAAGVIGSVEQVQEEIVAVEDELAVWARDHRELFSRVDARTAGALADLALWASAGRFTRAAVDEFLNVADCFLVAHAAAHGHTVVTHEGFQADARRRILIPNACRALDVPYCDTFTLLRARDVRLGLAA +A0A3Z5DTE1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A3Z5DTE1_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGIPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSVNAILGSVALILNCHHHASRVARIVPNEFPSMCPVDGRVRGITHNKILWDSSTLGAILIRRAISS +A0A4P8B987 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 A0A4P8B987_ECO57 MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A4P8BZD5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O145:NM A0A4P8BZD5_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A4Y1YLS3 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage Stx2a F578 A0A4Y1YLS3_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A5A4SKT1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O145:H28 A0A5A4SKT1_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTKGK +A0A5A5ZEY4 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O145:H28 A0A5A5ZEY4_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A5N3CV80 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A5N3CV80_ECOLX MKIIIFRVLTFFFVIFSVNVVAKDFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIEPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A650FQK2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A650FQK2_ECOLX MKCILLKWVLCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A0A6F8PI58 unreviewed negative regulation of translation [GO:0017148] Escherichia albertii A0A6F8PI58_ESCAL MRHILLKLVLFFCVCLSSASYADEFTVDFSSQKSYVDSLNSIRSAISTPLGNISQGGVSVSVINHVPGGNYISLNVRGLDPYSERFNHLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQGLTEPNQ +A0A6F8PI80 unreviewed negative regulation of translation [GO:0017148] Escherichia albertii A0A6F8PI80_ESCAL MRHILLKLVLFFCVCLSSASYADEFTVDFSSQKSYVDSLNSIRSAISTPLGNISQGGVSVSVINPVPGGNYISLNVRGLEPYSERFNHLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFATVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNILWEANTIAALLNRKPQGLTEANQ +A0A765T6I5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A765T6I5_ECOLX MRHILLKLVLFFCVCLSSVSYADEFTVDFSSQKSYVDSLNSIRSAISTPLGNISQGGISVSVINHVPGGNYISLNVRGLEPYSERFNHLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQGLTEPNQ +A0A7G3S022 unreviewed negative regulation of translation [GO:0017148] Escherichia phage Lys19259Vzw A0A7G3S022_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A7G3VSC5 unreviewed negative regulation of translation [GO:0017148] Escherichia phage Lys8385Vzw A0A7G3VSC5_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A7R7D0X6 unreviewed negative regulation of translation [GO:0017148] Stx1a-converting phage Stx1_699 A0A7R7D0X6_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A7R7D141 unreviewed negative regulation of translation [GO:0017148] Stx2a-converting phage Stx2_EH2201 A0A7R7D141_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A7R7D182 unreviewed negative regulation of translation [GO:0017148] Stx2d-converting phage Stx2_112808 A0A7R7D182_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVSVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A0A7R7D1L0 unreviewed negative regulation of translation [GO:0017148] Stx1a-converting phage Stx1_EH1992 A0A7R7D1L0_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A7R7D1S2 unreviewed negative regulation of translation [GO:0017148] Stx2a-converting phage Stx2_EH1910 A0A7R7D1S2_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A7R7D259 unreviewed negative regulation of translation [GO:0017148] Stx2a-converting phage Stx2_95 A0A7R7D259_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A7R7D2F8 unreviewed negative regulation of translation [GO:0017148] Stx2a-converting phage Stx2_EH1846 A0A7R7D2F8_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A7R7D536 unreviewed negative regulation of translation [GO:0017148] Stx2a-converting phage Stx2_12E129_yecE A0A7R7D536_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A7R7HJT1 unreviewed negative regulation of translation [GO:0017148] Stx1a-converting phage Stx1_499 A0A7R7HJT1_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A7R7HKP8 unreviewed negative regulation of translation [GO:0017148] Stx1a-converting phage Stx1_132418 A0A7R7HKP8_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A7R7HL45 unreviewed negative regulation of translation [GO:0017148] Stx1a-converting phage Stx1_EH1995 A0A7R7HL45_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A7R7HLE7 unreviewed negative regulation of translation [GO:0017148] Stx2a-converting phage Stx2_EH2246 A0A7R7HLE7_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A7U3LP85 unreviewed negative regulation of translation [GO:0017148] Stx1a-converting phage Stx1_14040 A0A7U3LP85_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A7U3LQF7 unreviewed negative regulation of translation [GO:0017148] Stx1a-converting phage Stx1_14744 A0A7U3LQF7_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A7U9LU58 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O145:H28 A0A7U9LU58_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A896ZAM0 unreviewed Shigella flexneri 3a A0A896ZAM0_SHIFL MLKFMLDTNICIFTIKNKPASVRERFNLNQGKMCISSVTLMELIYGAEKSQMPERNLAVIEGFVSRIDVLDYDAAAATHTGQIRAELARQGRPVGPFDQMIAGHARSRGLIIVTNNTREFERVGGLRTEDWS +A0A8E0FHM8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli 97.0246 A0A8E0FHM8_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGSGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A8E0FML8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli 97.0246 A0A8E0FML8_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A0A8E5U4R6 unreviewed Salmonella sp. SJTUF15034 A0A8E5U4R6_9ENTR MLILNGFSSATLALITPPFLPKGGKALSQSGPDGLASITLPLPISAERGFAPALALHYSSGGGNGPFGVGWSCATMSIARRTSHGVPQYNDSDEFLGPDGEVLVQTLSTGDAPNPVTCFAYGDVSFPQSYTVTRYQPRTESSFYRLEYWVGNSNGDDFWLLHDSNGILHLLGKTAAARLSDPQAASHTAQWLVEESVTPAGEHIYYSYLAENGDNVDLNGNEAGRDRSAMRYLSKVQYGNATPAADLYLWTSATPAVQWLFTLVFDYGERGVDPQVPPAFTAQNSWLARQDPFSLYNYGFEIRLHRLCRQVLMFHHFPDELGEADTLVSRLLLEYDENPILTQLCAARTLAYEGDGYRRAPVNNMMPPPPPPPMMGGNSSRPKSKWAIVEESKQIQALRYYSAQGYSVINKYLRGDDYPETQAKETLLSRDYLSTNEPSDEEFKNAMSVYINDIAEGLSSLPETDHRVVYRGLKLDKPALSDVLKEYTTIGNIIIDKAFMSTSPDKAWINDTILNIYLEKGHKGRILGDVAHFKGEAEMLFPPNTKLKIESIVNCGSQDFASQLSKLRLSDDATADTNRIKRIINMRVLNS +A0A8E5U7J1 unreviewed Salmonella sp. SJTUF14523 A0A8E5U7J1_9ENTR MLKFMLDTNTCIFTIKNKPEHIRERFNLNTSRMCISSITLMELIYGAEKSLAPERNLAVVEGFISRLEVLDYDTQAAIHTGQIRAELARKGTPVGPYDQMIAGHAGSRGLVVVTNNLREFERIPGIRIEDWC +A0A8E5UBY2 unreviewed Salmonella sp. SJTUF14523 A0A8E5UBY2_9ENTR MLILNGFSSATLALITPPFLPKGGKALSQSGPDGLASITLPLPISAERGFAPALALHYSSGGGNGPFGVGWSCATMSIARRTSHGVPQYNDSDEFLGPDGEVLVQTLSTGDAPNPVTCFAYGDVSFPQSYTVTRYQPRTESSFYRLEYWVGNSNGDDFWLLHDSNGILHLLGKTAAARLSDPQAASHTAQWLVEESVTPAGEHIYYSYLAENGDNVDLNGNEAGRDRSAMRYLSKVQYGNATPAADLYLWTSATPAVQWLFTLVFDYGERGVDPQVPPAFTAQNSWLARQDPFSLYNYGFEIRLHRLCRQVLMFHHFPDELGEADTLVSRLLLEYDENPILTQLCAARTLAYEGDGYRRAPVNNMMPPPPPPPMMGGNSSRPKSKWAIVEESKQIQALRYYSAQGYSVINKYLRGDDYPETQAKETLLSRDYLSTNEPSDEEFKNAMSVYINDIAEGLSSLPETDHRVVYRGLKLDKPALSDVLKEYTTIGNIIIDKAFMSTSPDKAWINDTILNIYLEKGHKGRILGDVAHFKGEAEMLFPPNTKLKIESIVNCGSQDFASQLSKLRLSDDATADTNRIKRIINMRVLNS +A0A8E5X6S5 unreviewed Salmonella sp. SJTUF15034 A0A8E5X6S5_9ENTR MLKFMLDTNTCIFTIKNKPEHIRERFNLNTSRMCISSITLMELIYGAEKSLAPERNLAVVEGFISRLEVLDYDTQAAIHTGQIRAELARKGTPVGPYDQMIAGHAGSRGLVVVTNNLREFERIPGIRIEDWC +A0A9E7S3N7 unreviewed negative regulation of translation [GO:0017148] Escherichia phage ASU11 A0A9E7S3N7_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGCVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A0A9P2R353 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157 A0A9P2R353_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A9P2R4C9 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157 A0A9P2R4C9_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISTILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A9P2VLK3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157 A0A9P2VLK3_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A0A9Q6Z964 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 A0A9Q6Z964_ECO57 MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0AA35AFG0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O26 str. RM8426 A0AA35AFG0_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A6MTS1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A6MTS1_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPSVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A6MTS3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A6MTS3_ECOLX MKCIIFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A6MTU5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A6MTU5_ECOLX MKCILFKWVLCLLLGFSSVSYSREFMIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIEREFRQALSETAPVYTMTPEEVDLTLNWGTISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTS +A7UMY0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A7UMY0_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A8B1H9 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 A8B1H9_ECO57 MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +A9JQD0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A9JQD0_ECOLX MKCILLKWILCLLLGFSSVPYSREFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGTRSVRAVNEVSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A9ZMR8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A9ZMR8_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +B0FEE0 unreviewed negative regulation of translation [GO:0017148] Escherichia phage Min27 B0FEE0_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +B1P7N8 unreviewed Escherichia coli B1P7N8_ECOLX MNKIYMLDTNICSFIMREQPEALLKHLEQSVLRGHRIVVSAITYSEMRFGATGPKASPRHVQLVDAFCERLDAVLPWDRAAVDATTEIKVALRLAGTPIGPNDTAIAGHAIAACAILVTNNVREFERVPGLVLEDWVR +B1VCL4 unreviewed Escherichia coli B1VCL4_ECOLX MNKTYMLDTCICSFIMREQPEAVLKRLEQAVLRGHRIVVSAITYSEMRFGATGPKASPRHVQLVDEFCARLDAILPWDRAAVDATTKIKVALRLAGTPIGPNDTAIAGHAIAAGAILVTNNTREFERVPDLVLEDWVK +B3GK88 unreviewed negative regulation of translation [GO:0017148] Escherichia coli B3GK88_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGSGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +B3VKH7 unreviewed negative regulation of translation [GO:0017148] Escherichia coli B3VKH7_ECOLX SYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +B3VKH9 unreviewed negative regulation of translation [GO:0017148] Escherichia coli B3VKH9_ECOLX SYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +B3VKI1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli B3VKI1_ECOLX SYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +B3VKI5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli B3VKI5_ECOLX SYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +B3VKJ9 unreviewed negative regulation of translation [GO:0017148] Escherichia coli B3VKJ9_ECOLX FFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTEVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +B3VKK1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli B3VKK1_ECOLX FFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +B3VKK3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli B3VKK3_ECOLX FFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDYLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +B6DZ86 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage YYZ-2008 B6DZ86_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +B6DZW0 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage 1717 B6DZW0_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +C5J4Y3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C5J4Y3_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +C6L1N0 unreviewed negative regulation of translation [GO:0017148] Escherichia albertii C6L1N0_ESCAL MRHILLKLVLFFCVCLSSVSYADEFTVDFSSQKSYVDSLNSIRSAISTPLGNISQGGISVSVINHVPGGNYISLNVRGLEPYSERFNHLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQGLTEPNQ +C7BEA8 unreviewed proteolysis [GO:0006508] Clostridium botulinum C7BEA8_CLOBO MPFVNKQFNYKDPVNGVDIAYIKIPNAGQMQPVKAFKIHNKIWVIPERDTFTNPEEGDLNPPPEAKQVPVSYYDSTYLSTDNEKDNYLKGVTKLFERIYSTELGRMLLTSIVRGIPFWGGSTIDTELKVIDTNCINVIQPDGSYRSEELNLVIIGPSADIIQFECKSFGHDVLNLTRNGYGSTQYIRFSPDFTFGFEESLEVDTNPLLGAGKFATDPAVTLAHELIHAGHRLYGIAINPNRVFKVNTNAYYEMSGLEVSFEELRTFGEHDAKFIDSLQENEFRLYYYNKFKDIASTLNKAKSIVGTTASLQYMKNVFKEKYLLSEDTSGKFSVDKLKFDKLYKMLTEIYTEDNFVKFFKVLNRKTYLNFDKAVFKINIVPEVNYTIYDGFNLRNTNLAANFNGQNTEINNMNFTKLKNFTGLFEFYKLLCVRGIITSKTKSLDEGYNKALNDLCIKVNNWDLFFSPSEDNFTNDLNKGEEITSDTNIEAAEENISLDLIQQYYLTFNFDNEPENISIENLSSDIIGQLELMPNIERFPNGKKYELDKYTMFHYLRAQEFEHGKSRIVLTNSVNEALLNPSSVYTFFSSDYVRKVNKATEAAMFLGWVEQLVYDFTDETSEVSTTDKIADITIIIPYIGPALNIGNMLYKDDFVGALIFSGAVILLEFIPEIAIPVLGTFALVSYIANKVLTVQTIDNALSKRNEKWGEVYKYIVTNWLAKVNTQIDLIRKKMKEALENQAEATKAIINYQYNQYTEEEKNNINFNIGDLSSKLNDSINKAMININKFLNQCSVSYLMNSMIPYGVKRLEDFDASLKDALLKYIYDNRGTLIGQVDRLKDKVNNTLSTDIPFQLSKYVDNQRLLSTFTEYIKNIINTSILNLRYESNHLIDLSRYASEINIGSKVNFDPIDKNQIQLFNLESSKIEIILKNAIVYNSMYENFSTSFWIKIPKYFSKINLNNEYTIINCIENNSGWKVSLNYGEIIWTLQDNKQNIQRVVFKYSQMVAISDYINRWIFITITNNRLNNSKIYINGRLIDQKPISNLGNIHASNNIMFKLDGCRDPQRYIWIKYFNLFDKELNEKEIKDLYDNQSNSGILKDFWGNYLQYDKPYYMLNLYDPNKYVDVNNVGIRGYMYLKGPRGSIVTTNIYLNSSLYMGTKFIIKKYASGNKDNIVRNNDRVYINVVVKNKEYRLATNASQAGVEKILSVLEIPDVGNLSQVVVMKSKNDQGIRNKCKMNLQDNNGNDIGFIGFHQFNNIDKLVASNWYNRQIERSSRTFGCSWEFIPVDDGWGESPL +C7FPU4 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C7FPU4_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFTDFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRGRGITHNKILWDSSTLGAILMRRTISS +C7FPV4 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C7FPV4_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSVNAILGSVALILNCHHHASRVARIVPDEFPSMCPVDGRVRGITHNKILWDSSTLGAILIRRAISS +C7FPV8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C7FPV8_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILSTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +C7S9Y5 unreviewed Escherichia coli C7S9Y5_ECOLX MNKIYMLDTNICSFIMREQPEAVLKNLEQAVLRGHRIVVSAITYSEMRFGATGPKASPRHVQLVDAFCARLDAILPWDRAAVDATTEVKVALRLAGTPIGPNDTAIAGHAIATGAILVTNNVREFERVPGLVLEDWAG +C7TQE6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C7TQE6_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPLGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQPLYTTGE +C7TQF0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C7TQF0_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRAEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVIAEALRFRQIQREFRQALSETAPVYTMMPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +C7TQF6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C7TQF6_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTSLEHISQGATSVSVINHTPPGSYISVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTQQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEDSVRVGRISFNNISAILGTVAVILNCHHQGTRSVRYVNEEMQPECQISGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +C7TQF8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C7TQF8_ECOLX MKCILLKWILCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +C7TQG2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C7TQG2_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTIISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQPLYTTGE +C7TQM6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli C7TQM6_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRIFFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQPLYTTGE +D2KHS9 unreviewed proteolysis [GO:0006508] Clostridium baratii D2KHS9_9CLOT MPVNINNFNYNDPINNTTILYMKMPYYEDSNKYYKAFEIMDNVWIIPERNIIGKKPSDFYPPISLDSGSSAYYDPNYLTTDAEKDRFLKTVIKLFNRINSNPAGQVLLEEIKNGKPYLGNDHTAVNEFCANNRSTSVEIKESNGTTDSMLLNLVILGPGPNILECSTFPVRIFPNNIAYDPSEKGFGSIQLMSFSTEYEYAFNDNTDLFIADPAISLAHELIHVLHGLYGAKGVTNKKVIEVDQGALMAAEKDIKIEEFITFGGQDLNIITNSTNQKIYDNLLSNYTAIASRLSQVNINNSALNTTYYKNFFQWKYGLDQDSNGNYTVNISKFNAIYKKLFSFTECDLAQKFQVKNRSNYLFHFKPFRLLDLLDDNIYSISEGFNIGSLRVNNNGQNINLNSRIVGPIPDNGLVERFVGLCKSIVSKKGTKNSLCIKVNNRDLFFVASESSYNENGINSPKEIDDTTITNNNYKKNLDEVILDYNSDAIPNLSSRLLNTTAQNDSYVPKYDSNGTSEIKEYTVDKLNVFFYLYAQKAPEGESAISLTSSVNTALLDASKVYTFFSSDFINTVNKPVQAALFISWIQQVINDFTTEATQKSTIDKIADISLVVPYVGLALNIGNEVQKGNFKEAIELLGAGILLEFVPELLIPTILVFTIKSFINSDDSKNKIIKAINNALRERELKWKEVYSWIVSNWLTRINTQFNKRKEQMYQALQNQVDGIKKIIEYKYNNYTLDEKNRLKAEYNIYSIKEELNKKVSLAMQNIDRFLTESSISYLMKLINEAKINKLSEYDKRVNQYLLNYILENSSTLGTSSVQELNNLVSNTLNNSIPFELSEYTNDKILISYFNRFYKRIIDSSILNMKYENNRFIDSSGYGSNISINGDIYIYSTNRNQFGIYSSRLSEVNITQNNTIIYNSRYQNFSVSFWVRIPKYNNLKNLNNEYTIINCMRNNNSGWKISLNYNNIIWTLQDTTGNNQKLVFNYTQMIDISDYINKWTFVTITNNRLGHSKLYINGNLTDQKSILNLGNIHVDDNILFKIVGCNDTRYVGIRYFKIFNMELDKTEIETLYHSEPDSTILKDFWGNYLLYNKKYYLLNLLKPNMSVTKNSDILNINRQRGIYSKTNIFSNARLYTGVEVIIRKVGSTDTSNTDNFVRKNDTVYINVVDGNSEYQLYADVSTSAVEKTIKLRRISNSNYNSNQMIIMDSIGDNCTMNFKTNNGNDIGLLGFHLNNLVASSWYYKNIRNNTRNNGCFWSFISKEHGWQE +D6QZ25 unreviewed negative regulation of translation [GO:0017148] Shigella dysenteriae D6QZ25_SHIDY MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISSACV +D8L2J9 unreviewed Klebsiella pneumoniae D8L2J9_KLEPN MNKTYMLDTCICSFIMREQPEAVLKRLEQAVLRGQRIVVSAITYSEMRFGATGPKASPRHVELVDAFCARLDAILPWDRAAVDATTEIKVALRMAGTPIGPNDTAIAGHAIAAGAVLVTNNTREFERVPGLVLEDWVR +E4W7W5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli E4W7W5_ECOLX MKCILFKWVLCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTREASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARFVRAVNEESQPQCQITGDRPVIKINNKLWESNTAAAFLNRKSQSLYTTGE +E7DYU8 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage VT2phi_272 E7DYU8_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +G8GWP4 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage GV8 G8GWP4_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +G8GWP6 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage GV2412 G8GWP6_9CAUD MKCILFKWVLCLLLGFFSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +G8GWP8 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage BR2 G8GWP8_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +G8GWQ0 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage L34 G8GWQ0_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMELSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESHPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +G8GYD2 unreviewed negative regulation of translation [GO:0017148] Escherichia phage 1720a-02 G8GYD2_9CAUD MKCILFKWVLCLLLGFSSVSYSREFMIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +G9L6I9 unreviewed negative regulation of translation [GO:0017148] Escherichia phage TL-2011c G9L6I9_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +H9TVI5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 H9TVI5_ECO57 MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNGESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +I0B573 unreviewed negative regulation of translation [GO:0017148] Escherichia coli I0B573_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVTRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILDCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +I0B576 unreviewed negative regulation of translation [GO:0017148] Escherichia coli I0B576_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGNINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTPGAILMRRTISS +J7I4W4 unreviewed negative regulation of translation [GO:0017148] Escherichia coli J7I4W4_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRPAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRCNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +J7I8Z7 unreviewed negative regulation of translation [GO:0017148] Escherichia coli J7I8Z7_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILSTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +K0JD26 unreviewed negative regulation of translation [GO:0017148] Escherichia phage P13374 K0JD26_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +K4JAT0 unreviewed defense response [GO:0006952]; negative regulation of translation [GO:0017148] Phytolacca americana (American pokeweed) (Phytolacca decandra) K4JAT0_PHYAM MKSMLVVTISIWLILAPTSTWAVNTIIYNVGSTTISKYATFLNDLRNEAKDPSLKCYGIPMLPNTNTNPKYVLVELQGSNKKTITLMLRRNNLYVMGYSDPFETNKCRYHIFNDISGTERQDVETTLCPNANSRVSKNINFDSRYPTLESKAGVKSRSQVQLGIQILDSNIGKISGVMSFTEKTEAEFLLVAIQMVSEAARFKYIENQVKTNFNRAFNPNPKVLNLQETWGKISTAIHDAKNGVLPKPLELVDASGAKWIVLRVDEIKPDVAFLNYVGGSCQTTYNQNAMFPQLIMSTYYNYMVNLGDLFEGF +L0I969 unreviewed negative regulation of translation [GO:0017148] Escherichia coli L0I969_ECOLX MKCILFKWVLCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQAHFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTREASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +M5AWY2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli M5AWY2_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVIPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +O54476 unreviewed Staphylococcus aureus O54476_STAAU MKKLISILLINIIILGVSNNASAQGDIGIDNLRNFYTKKDFVDLKDVKDNDTPIANQLQFSNESYDLISESKDFNKFSNFKGKKLDVFGISYNGQCNTKYIYGGVTATNEYLDKSRNIPINIWINGNHKTISTNKVSTNKKLVTAQEIDVKLRKYLQEEYNIYGHNGTKKGEEYGHKSKFYSGFNIGKVTFHLNNNDTFSYDLFYTGDDGLPKSFLKIYEDNKTVESEKFHLDVDISYKETI +O73858 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA6_NAJSP YTLKCNKLVPLFYKTCPAGKNLCYKMFMVSNKTVPVKRGCIDVCPKNSALVKYVCCNTDRCN +O73859 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA7_NAJSP YTLKCNKLVPLFYKTCPAGKNLCYKMFMMSNKTVPVKRGCIDVCPKNSALVKYVCCNTDRCN +P00589 reviewed Corynephage beta DTXH_CORBE MSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGDVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVKLEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGGSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNRVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRSAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIASVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +P0C0I5 reviewed Streptococcus pyogenes serotype M1 SPEC_STRP1 MKKINIIKIVFIITVILISTISPIIKSDSKKDISNVKSDLLYAYTITPYDYKNCRVNFSTTHTLNIDTQKYRGKDYYISSEMSYEASQKFKRDDHVDVFGLFYILNSHTGEYIYGGITPAQNNKVNHKLLGNLFISGESQQNLNNKIILEKDIVTFQEIDFKIRKYLMDNYKIYDATSPYVSGRIEIGTKDGKHEQIDLFDSPNEGTRSDIFAKYKDNRIINMKNFSHFDIYLEK +P0CAZ2 reviewed defense response to bacterium [GO:0042742]; killing of cells of another organism [GO:0031640] Lachesana tarabaevi (Spider) CTX16_LACTA MKYFVVALALVAAFACIAESKPAESEHELAEVEEENELADLEDAVWLEHLADLSDLEEARGFFGNTWKKIKGKADKIMLKKAVKIMVKKEGIFKEEAQAKVDAMSKKQIRLYLLKYYGKKALQKASEKL +P0CAZ3 reviewed defense response to bacterium [GO:0042742]; killing of cells of another organism [GO:0031640] Lachesana tarabaevi (Spider) CTX19_LACTA MKYFVVALALVAAFACIAESKPAESEHELAEVEEENELADLEDAVWLEHLADLSDLEEARGFFGNTWKKIKGKTDKIMLKKAVKIMVKKEGISKEEAQAKVDAMSKKQIRLYLLKHYGKKALQKASEKL +P0CY83 reviewed Conus bullatus (Bubble cone) CJE28_CONBU MTSVQSATCCCLLWLVLCVQLVTPDSPATAQLSRHLTARVPVGPALAYACSVMCAKGYDTVVCTCTRRRGVVSSSI +P0DJY8 reviewed modulation of host virulence by virus [GO:0098676] Streptococcus pyogenes phage T12 (Bacteriophage T12) SPEA_BPT12 MENNKKVLKKMVFFVLVTFLGLTISQEVFAQQDPDPSQLHRSSLVKNLQNIYFLYEGDPVTHENVKSVDQLLSHDLIYNVSGPNYDKLKTELKNQEMATLFKDKNVDIYGVEYYHLCYLCENAERSACIYGGVTNHEGNHLEIPKKIVVKVSIDGIQSLSFDIETNKKMVTAQELDYKVRKYLTDNKQLYTNGPSKYETGYIKFIPKNKESFWFDFFPEPEFTQSKYLMIYKDNETLDSNTSQIEVYLTTK +P14608 reviewed Pseudomonas aeruginosa CTX_PSEAI MNDIDTITNAWGRWKTAQYGTTCWFTESTQYGRNKDTRSYMQHQTNVSAPKDLVYSNFVQQDGGSTLLGQYDMINEGSQVIELAVNLQQGLVDTFTWSVTEQLKVGVEVKVKANIPLVGGAEITSTVELSLSSTQGASTSKSSNYGASTKVLISPHSHGWGEVALSFTELRTQWVGNVGLQGYVAIWFNNKVALNNDGDYHYLWFIPVEQVFWECVQHNIVNTSGYVVQGNGVLAQATGTFHSSVGLNLKTIAHERPYPETSEAVRTFYNYASLVPDLETRVRSAE +P49122 reviewed Naja atra (Chinese cobra) 3SOF7_NAJAT MKTLLLTLVVVTIVCLDLGYTLKCHNTQLPFIYNTCPEGKNLCFKATLKFPLKFPVKRGCAATCPRSSSLVKVVCCKTDKCN +P60306 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SADB_NAJAT LKCNKLKPLAYKTCPAGKNLCYKMFMMSNKTVPVKRGCIDVCPKNSLLVKYVCCNTDRCN +P60310 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA5B_NAJSP MKTLLLTLLVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMFMVSNLTVPVKRGCIDVCPKNSALVKYVCCNTDRCN +P62561 reviewed Streptococcus pyogenes serotype M18 (strain MGAS8232) SPEA_STRP8 MENNKKVLKKMVFFVLVTFLGLTISQEVFAQQDPDPSQLHRSSLVKNLQNIYFLYEGDPVTHENVKSVDQLLSHDLIYNVSGPNYDKLKTELKNQEMATLFKDKNVDIYGVEYYHLCYLCENAERSACIYGGVTNHEGNHLEIPKKIVVKVSIDGIQSLSFDIETNKKMVTAQELDYKVRKYLTDNKQLYTNGPSKYETGYIKFIPKNKESFWFDFFPEPEFTQSKYLMIYKDNETLDSNTSQIEVYLTTK +P71293 unreviewed negative regulation of translation [GO:0017148] Escherichia coli P71293_ECOLX MECILLKWILCLLLGFSSVSYSQEFTIDFPTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRPIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +P79688 reviewed Bungarus multicinctus (Many-banded krait) 3SO9L_BUNMU MKTLLLTLVVLTIACLDLGYTKTCFNDDLTNPKTTELCRHSVYFCFKNSRIAGGVERMQRGCSLTCPDIKYNGKYIYCCTRDNCNA +Q03037 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q03037_ECOLX MKCILFKWVLCLLLGFSSVSYSREFMIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +Q07871 unreviewed hemolysis by symbiont of host erythrocytes [GO:0019836] Escherichia coli Q07871_ECOLX MKKMFMAVLFALVSVNAMAADCAKGKIEFSKYNENDTFTVKVAGKEYWTSRWNLQPLLQSAQLTGMTVTIKSSTCESGSGFAEVQFNND +Q08JA4 unreviewed negative regulation of translation [GO:0017148] Stx2-converting phage 86 Q08JA4_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q0N4U4 reviewed Conus ferrugineus (Cone snail) CJE1_CONFR MPSVRSVTCCCLLWMMLSVQLVTPGSPGTAQLSGHRTARSPGSTICKMACRTGNGHKYPFCNCRGKRDVVSSSMAV +Q1ELX7 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O111:NM Q1ELX7_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q1ELX9 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O111:NM Q1ELX9_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHRGTSLPQSGARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q1ELY1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O111:NM Q1ELY1_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHRGTSLPQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q25BX4 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q25BX4_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNKLWESNTAAAFLNRKSQPLYTTGE +Q2ACE8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H- Q2ACE8_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSKTAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGR +Q2ACF4 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 Q2ACF4_ECO57 MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGVQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTMGK +Q2ACG0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 Q2ACG0_ECO57 MKCILFKWVLCLLLGFSSVSHSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALVEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q2ACG6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 Q2ACG6_ECO57 MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDAYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALGRSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q2HWU1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O63:HNM Q2HWU1_ECOLX MRYILLKLVLFFCVCLSSASYADEFTVDFSSQKSYVDSLNSIRSAISTPLGNISQGGVSVSVINHVPGGNYISLNVRGLDPYSERFNHLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQDLTEPNQ +Q2VBN4 reviewed Ophiophagus hannah (King cobra) (Naja hannah) 3SDC3_OPHHA MKTLLLTLVVVTIVCLDLGYTRKCLNTPLPLIYKTCPIGQDKCIKMTIKKLPSKYDVIRGCTDICPKSSADVVVVCCDTNKCNK +Q2VBN5 reviewed Ophiophagus hannah (King cobra) (Naja hannah) 3SDC1_OPHHA MKTLLLTLVVVTIVCLDLGYTRKCLNTPLPLIYTTCPIGQDKCVKMTIKKLPSKYDVIRGCTDICPKSSADVVVVCCDTNKCNK +Q2VBN7 reviewed Ophiophagus hannah (King cobra) (Naja hannah) 3SDC4_OPHHA MKTLLLTLVVVTIVCLDLGYTRKCLNTPLPLIYTTCPIGQDKCVKMTIKKLPSKYDVIRGCTDICPKSSADVVVVCCDTNKCDK +Q2VBN8 reviewed Ophiophagus hannah (King cobra) (Naja hannah) 3SDC9_OPHHA MKTLLLTLVVVTIVCLDLGYTRKCLNTPLPLIYKTCPIGQDRCIKMTIKKLPSKYDVIRGCIDICPKSSADVEVLCCDTNKCNK +Q307V8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q307V8_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILSTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q307W0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q307W0_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDGQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q46050 unreviewed negative regulation of translation [GO:0017148] Citrobacter freundii Q46050_CITFR MKCILFKWALCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEVSTPLEHISQGTTSVSVIINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVVVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q47174 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47174_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLHSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERIDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQPLYTTGE +Q47175 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47175_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVVGFVNTTTNTSYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q47177 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47177_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTSYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQPLYTTGE +Q47179 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47179_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVVGFVNTTTNTSYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQPLYTTGE +Q47638 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47638_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFESINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q47639 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47639_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSSDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q47642 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47642_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEVSTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q47643 unreviewed negative regulation of translation [GO:0017148] Enterobacter cloacae Q47643_ENTCL MKCILFKWVLCLLLGFSSVSYSREFTIDLSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTAGDVDLTLNWGRISNVLPEYRGEDGVRVGRISCNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q47644 unreviewed hemolysis by symbiont of host erythrocytes [GO:0019836] Escherichia coli Q47644_ECOLX MKKMFIAVLFALVSVNAMAADCAKGKIEFSKYNEDNTFTVKVSGREYWTNRWNLQPLLQSAQLTGMTVTIISNTCSSGSGFAQVKFN +Q47645 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47645_ECOLX MRHILLKLVLFFCVCLSSASYADEFTVDFSSQKSYVDSLNSIRSAISTPLGNISQGGVSVSVINHVLGGNYISLNVRGLDPYSERFNHLRLIMERNNLYVAGFINTETNIFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQDLTEPNQ +Q47647 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q47647_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTLLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q4PS71 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q4PS71_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFSVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYQGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q53B46 reviewed Ophiophagus hannah (King cobra) (Naja hannah) 3SDC5_OPHHA MKTLLLTLVVVTIVCLDLGYTRKCLNTPLPLIYKTCPIGQDKCIKMTIKKLPSKYDVIRGCIDICPKSSADVEVLCCDTNKCNK +Q53YN2 unreviewed defense response [GO:0006952]; negative regulation of translation [GO:0017148] Phytolacca americana (American pokeweed) (Phytolacca decandra) Q53YN2_PHYAM MKSMLVVTISIWLILAPTSTWAVNTIIYNVGSTTISKYATFLNDLRNEAKDPSLKCYGIPMLPNTNTNPKYVLVELQGSNKKTITLMLRRNNLYVMGYSDPFETNKCRYHIFNDISGTERQDVETTLCPNANSRVSKNINFDSRYPTLESKAGVKSRSQVQLGIQILDSNIGKISGVMSFTEKTEAEFLLVAIQMVSEAARFKYIENQVKTNFNRAFNPNPKVLNLQETWGKISTAIHDAKNGVLPKPLELVDASGAKWIVLRVDEIKPDVALLNYVGGSCQTTYNQNAMFPQLIMSTYYNYMVNLGDLFEGF +Q54A76 unreviewed Staphylococcus aureus Q54A76_STAAU MYKRLFISRVILIFALILVISTPNVLAESQPDPMPDDLHKSSEFTGTMGNMKYLYDDHYVSATKVKSVDKFLAHDLIYNISDKKLKNYDKVKTELLNEDLAKKYKDEVVDVYGSNYYVNCYFSSKDNVGKVTGGKTCMYGGITKHEGNHFDNGNLQNVLVRVYENKRNTISFEVQTDKKSVTAQELDIKARNFLINKKNLYEFNSSPYETGYIKFIENNGNTFWYDMMPAPGDKFDQSKYLMMYNDNKTVDSKSVKIEVHLTTKNG +Q57478 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q57478_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q5MBW7 unreviewed negative regulation of translation [GO:0017148] Stx1-converting phage phi-O153 Q5MBW7_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q5TJL6 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage 2851 Q5TJL6_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q5WPV7 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPV7_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGATSVSVINHTPPGSYISVDIRGLDIYEARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEDSVRVGRISFNNISAILGTVAVILNCHHQGTRSVRYVNEEMQPECQISGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q5WPV9 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPV9_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSNTTLQRVAALEXSGMQISRHSXVSSXLALMEXXGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRLSNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQXLYTTGK +Q5WPW3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPW3_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNNIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSPVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTMGK +Q5WPW5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPW5_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINTTLWESNTAAAFLNRKSQXLYTTGK +Q5WPW7 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPW7_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQXLYTTGK +Q5WPW9 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPW9_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILSTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q5WPX1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPX1_ECOLX MKCILFKWVLCLXLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTXISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q5WPX7 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPX7_ECOLX MKCILFKWVLCLXLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESXPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTG +Q5WPY1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPY1_ECOLX MKCILFKWVLCLLLGFSSVSYFREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGATTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTTAAFLNRKSQSLYTTGE +Q5WPY3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPY3_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNTIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGQISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q5WPY7 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPY7_ECOLX MKCILLKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q5WPY9 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPY9_ECOLX MKCILFKWVLCLXLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWRRISNVLPEYRGEDCVKVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQXTGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q5WPZ1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q5WPZ1_ECOLX MKCILLKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVGVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q5XB82 reviewed Streptococcus pyogenes serotype M6 (strain ATCC BAA-946 / MGAS10394) SPEC_STRP6 MKKINIIKIVFIITVILISTISPIIKSDSKKDISNVKSDLLYAYTITPYDYKDCRVNFSTTHTLNIDTQKYRGKDYYISSEMSYEASQKFKRDDHVDVFGLFYILNSHTGEYIYGGITPAQNNKVNHKLLGNLFISGESQQNLNNKIILEKDIVTFQEIDFKIRKYLMDNYKIYDATSPYVSGRIEIGTKDGKHEQIDLFDSPNEGTRSDIFAKYKDNRIINMKNFSHFDIYLEK +Q6DWK7 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage O157-469 Q6DWK7_9VIRU MKCILFKWVLCLLLGFSSVSSSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWK9 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage A397 Q6DWK9_9VIRU MKCILFKWVLCLLLGFSSVSSSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWL1 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage VTB60 Q6DWL1_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWL3 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage VTB46 Q6DWL3_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWL5 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage VTB323 Q6DWL5_9CAUD MKCILFKWVLCLLLGFFSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWL7 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage O157-379 Q6DWL7_9VIRU MKCILFKWVLCLLLGFFSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWL9 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage O157-310 Q6DWL9_9VIRU MKCILFKWVLCLLLGFFSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWM1 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage O157-330 Q6DWM1_9VIRU MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWM3 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage O157-292 Q6DWM3_9VIRU MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWM5 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage A580 Q6DWM5_9VIRU MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWM7 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage A549 Q6DWM7_9VIRU MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWM9 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage A315 Q6DWM9_9VIRU MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWN1 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage A312 Q6DWN1_9VIRU MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWN3 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage VTB324 Q6DWN3_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q6DWN5 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage VTB178 Q6DWN5_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q6DWN7 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage VTB91 Q6DWN7_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q6DWN9 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage VTB75 Q6DWN9_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q6DWP1 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage O157-572 Q6DWP1_9VIRU MKCILFKWVLCLLLGFFSVSSSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWP3 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage A557 Q6DWP3_9VIRU MKCILFKWVLCLLLGFFSVSSSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWP5 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage A534 Q6DWP5_9VIRU MKCILFKWVLCLLLGFFSVSSSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6DWP7 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage A75 Q6DWP7_9VIRU MKCILFKWVLCLLLGFFSVSSSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6H9W4 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage BP-4795 Q6H9W4_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q6LDT4 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage 933J Q6LDT4_9VIRU MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGSGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q6NK15 unreviewed Corynebacterium diphtheriae (strain ATCC 700971 / NCTC 13129 / Biotype gravis) Q6NK15_CORDI MSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +Q6PWU4 unreviewed defense response [GO:0006952]; negative regulation of translation [GO:0017148] Phytolacca americana (American pokeweed) (Phytolacca decandra) Q6PWU4_PHYAM MKVMLVVTISIWLILAPTSTWAVNTIIYNVGSTTISKYATFLNDLRNEAKDPSLKCYGIPMLPNTNTNPKYVLVELQGSNKKTITLMLRRNNLYVMGYSDPFETNKCRYHIFNDISGTERQDVETTLCPNANSRVSKNINFDSRYPTLESKAGVKSRSQVQLGIQILDSNIGKISGVMSFTEKTEAEFLLVAIQMVSEAARFKYIENQVKTNFNRAFNPNPKVLNLQETWGKISTAIHDAKNGVLPKPLELVDASGAKWIVLRVDEIKPDVALLNYVGGSCQTTYNQNAMFPQLIMSTYYNYMVNLGDLFEGF +Q6YII8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H- Q6YII8_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q776E1 unreviewed negative regulation of translation [GO:0017148] Escherichia phage Stx2 II Q776E1_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q776E8 unreviewed negative regulation of translation [GO:0017148] Escherichia Stx1 converting phage Q776E8_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q776G1 unreviewed negative regulation of translation [GO:0017148] Stx2 converting phage I Q776G1_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q776Q3 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q776Q3_BPVT2 MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q777W4 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage Lahn1 Q777W4_9VIRU MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q779K3 reviewed hemolysis by symbiont of host erythrocytes [GO:0019836]; modulation of host virulence by virus [GO:0098676] Shigella phage 7888 (Shigella sonnei bacteriophage 7888) STXB_BP788 MKKTLLIAASLSFFSASALATPDCVTGKVEYTKYNDDDTFTVKVGDKELFTNRWNLQSLLLSAQITGMTVTIKTNACHNGGGFSEVIFR +Q77CH6 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage SC370 Q77CH6_9VIRU MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q77CH9 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage LC159 Q77CH9_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q77YB9 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage H19B (Bacteriophage H19B) Q77YB9_BPH19 MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGSGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q79EB3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q79EB3_ECOLX MKCILFKWVLCLLLGFSSVSYSREFMIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQGEFRQALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +Q7ABA5 unreviewed DNA-templated transcription initiation [GO:0006352] Escherichia coli O157:H7 Q7ABA5_ECO57 MSQNTLKVHDLNEDAEFDENGVEVFDEKALVEEEPSDNDLAEEELLSQGATQRVLDATQLYLGEIGYSPLLTAEEEVYFARRALRGDVASRRRMIESNLRLVVKIARRYGNRGLALLDLIEEGNLGLIRAVEKFDPERGFRFSTYATWWIRQTIERAIMNQTRTIRLPIHIVKELNVYLRTARELSHKLDHEPSAEEIAEQLDKPVDDVSRMLRLNERITSVDTPLGGDSEKALLDILADEKENGPEDTTQDDDMKQSIVKWLFELNAKQREVLARRFGLLGYEAATLEDVGREIGLTRERVRQIQVEGLRRLREILQTQGLNIEALFRE +Q7AD06 unreviewed Escherichia coli O157:H7 Q7AD06_ECO57 MAQVINTNSLSLITQNNINKNQSALSSSIERLSSGLRINSAKDDAAGQAIANRFTSNIKGLTQAARNANDGISVAQTTEGALSEINNNLQRIRELTVQATTGTNSDSDLDSIQDEIKSRLDEIDRVSGQTQFNGVNVLAKDGSMKIQVGANDGETITIDLKKIDSDTLGLNGFNVNGKGTITNKAATVSDLTSAGAKLNTTTGLYDLKTENTLLTTDAAFDKLGNGDKVTVGGVDYTYNAKSGDFTTTKSTAGTGVDAAAQAADSASKRDALAATLHADVGKSVNGSYTTKDGTVSFETDSAGNITIGGSQAYVDDAGNLTTNNAGSAAKADMKALLKAASEGSDGASLTFNGTEYTIAKATPATTTPVAPLIPGGITYQATVSKDVVLSETKAAAATSSITFNSGVLSKTIGFTAGESSDAAKSYVDDKGGITNVADYTVSYSVNKDNGSVTVAGYASATDTNKDYAPAIGTAVNVNSAGKITTETTSAGSATTNPLAALDDAISSIDKFRSSLGAIQNRLDSAVTNLNNTTTNLSEAQSRIQDADYATEVSNMSKAQIIQQAGNSVLAKANQVPQQVLSLLQG +Q7AEQ9 unreviewed cell wall macromolecule catabolic process [GO:0016998]; defense response to bacterium [GO:0042742]; killing of cells of another organism [GO:0031640]; peptidoglycan catabolic process [GO:0009253] Escherichia coli O157:H7 Q7AEQ9_ECO57 MNAKIRYGLSAAVLALIGAGASAPEILDQFLDEKEGNHTTAYRDGAGIWTICRGATRGDGKPVIPGMKLSKEKCDRVNAIERDKALAWVEKNIRVPLTEPQKAGIASFCPYNIGPGKCFPSTFYRRINAGDRKGACEAIRWWIKDGGRDCRIRSNNCYGQVSRRDQESALACWGIDR +Q7AK23 unreviewed cell wall macromolecule catabolic process [GO:0016998]; defense response to bacterium [GO:0042742]; killing of cells of another organism [GO:0031640]; peptidoglycan catabolic process [GO:0009253] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q7AK23_BPVT2 MSRKLRYGLSAAVLALIAAGASAPEILDQFLDEKEGNHTTAYRDGAGIWTICRGATRVDGKPVIPGMKLSKEKCDRVNAIERDKALAWVEKNIKVPLTEPQKAGIASFCPYNIGPGKCFPSTFYRRINAGDRKGACEAIRWWIKDGGRDCRIRSNNCYGQVSRRDQESALACWGIDR +Q7AK38 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage VT1-Sakai Q7AK38_BPVT1 MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q7AYI8 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage CP-1639 Q7AYI8_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q7B5L0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H- Q7B5L0_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPDVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q7BQ99 unreviewed negative regulation of translation [GO:0017148] Shigella dysenteriae Q7BQ99_SHIDY MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +Q7BUA0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q7BUA0_ECOLX MKCILLKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNCIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRSVNEEIQPECQITGDRPVIRLNNTLWESNTAAAFLNRRAHSLNTSGE +Q7DB57 unreviewed protein secretion [GO:0009306] Escherichia coli O157:H7 Q7DB57_ECO57 MDTGYFVQLCVQTFWIIFILSLPTVIAASVIGIIISLVQAITQLQDQTLPFLLKIIAVFATLALTYHWMGTTIINFSSIIFEMIPKVNG +Q7DB64 unreviewed protein secretion by the type III secretion system [GO:0030254] Escherichia coli O157:H7 Q7DB64_ECO57 MKKISFFIFTALFCCSAQAAPSSLEKRLGKSEYFIITKSSPVRAILNDFAANYSIPVFISSSVNDDFSGEIKNEKPVKVLEKLSKLYHLTWYYDENILYIYKTNEISRSIITPTYLDIDSLLKYLSDTISVNKNSCNVRKITTFNSIEVRGVPECIKYITSLSESLDKEAQSKAKNKDVVKVFKLNYASATDITYKYRDQNVVVPGVVSILKTMASNGSLPSTGKGAVERSGNLFDNSVTISADPRLNAVVVKDREITMDIYQQLISELDIEQRQIEISVSIIDVDANDLQQLGVNWSGTLNAGQGTIAFNSSTAQANISSSVISNASNFMIRVNALQQNSKAKILSQPSIITLNNMQAILDKNVTFYTKVSGEKVASLESITSGTLLRVTPRILDDSSNSLTGKRRERVRLLLDIQDGNQSTNQSNAQDASSTLPEVQNSEMTTEATLSAGESLLLGGFIQDKESSSKDGIPLLSDIPVIGSLFSSTVKQKHSVVRLFLIKATPIKSASSE +Q7DB71 unreviewed protein secretion by the type III secretion system [GO:0030254] Escherichia coli O157:H7 Q7DB71_ECO57 MISEHDSVLEKYPRIQKVLNSTVPALSLNSSTRYEGKIINIGGTIIKARLPKARIGAFYKIEPSQRLAEVIAIDEDEVFLLPFEHVSGMYCGQWLSYQGDEFKIRVGDALLGRLVDGIGRPMESNIAAPYLPFERSLYAEPPDPLLRQVIDQPFILGVRAIDGLLTCGIGQRIGIFAGSGVGKSTLLGMICNGASADIIVLALIGERGREVNEFLALLPQSTLSKCVLVVTTSDRPALERMKAAFTATTIAEYFRDQGKNVLLMMDSVTRYARAARDVGLASGEPDVRGGFPPSVFSSLPKLLERAGPAPKGSITAIYTVLLESDNVNDPIGDEVRSILDGHIVLTRELAEENHFPAIDIGLSASRVMHNVVTSEHLRAAAECKKLIATYKNVELLIRIGEYTMGQDPEADKAIKNRKLIQNFIQQSTKDISSYEKTIESLFKVVA +Q7WUF4 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q7WUF4_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTPQRVAALERSGMQVSRHSLVSSYLALMEFSGNTMTREASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKITNKLWESNTAAAFLNRKSQPLYTTGE +Q7WZI7 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 Q7WZI7_ECO57 MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCQADGRVRGITHNKILWDSSTLGAILMRRTISS +Q7WZI8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 Q7WZI8_ECO57 MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTI +Q7X5J3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q7X5J3_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTSLEHISQGATSVSVINHTPPGSYISVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEDSVRVGRISFNNISAILGTVAVILNCHHQGTRSVRYVNEEMQPECQISGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q8G8K7 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8G8K7_ECOLX MKCILLKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q8GGK8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8GGK8_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q8GGK9 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8GGK9_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q8HA14 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage Nil2 Q8HA14_9CAUD MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q8KU16 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8KU16_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTASMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q8KZM3 unreviewed proteolysis [GO:0006508] Clostridium butyricum Q8KZM3_CLOBU MPTINSFNYNDPVNNRTILYIKPGGCQQFYKSFNIMKNIWIIPERNVIGTIPQDFLPPTSLKNGDSSYYDPNYLQSDQEKDKFLKIVTKIFNRINDNLSGRILLEELSKANPYLGNDNTPDGDFIINDASAVPIQFSNGSQSILLPNVIIMGAEPDLFETNSSNISLRNNYMPSNHGFGSIAIVTFSPEYSFRFKDNSMNEFIQDPALTLMHELIHSLHGLYGAKGITTKYTITQKQNPLITNIRGTNIEEFLTFGGTDLNIITSAQSNDIYTNLLADYKKIASKLSKVQVSNPLLNPYKDVFEAKYGLDKDASGIYSVNINKFNDIFKKLYSFTEFDLATKFQVKCRQTYIGQYKYFKLSNLLNDSIYNISEGYNINNLKVNFRGQNANLNPRIITPITGRGLVKKIIRFCKNIVSVKGIRKSICIEINNGELFFVASENSYNDDNINTPKEIDDTVTSNNNYENDLDQVILNFNSESAPGLSDEKLNLTIQNDAYIPKYDSNGTSDIEQHDVNELNVFFYLDAQKVPEGENNVNLTSSIDTALLEQPKIYTFFSSEFINNVNKPVQAALFVGWIQQVLVDFTTEANQKSTVDKIADISIVVPYIGLALNIGNEAQKGNFKDALELLGAGILLEFEPELLIPTILVFTIKSFLGSSDNKNKVIKAINNALKERDEKWKEVYSFIVSNWMTKINTQFNKRKEQMYQALQNQVNALKAIIESKYNSYTLEEKNELTNKYDIEQIENELNQKVSIAMNNIDRFLTESSISYLMKLINEVKINKLREYDENVKTYLLDYIIKHGSILGESQQELNSMVIDTLNNSIPFKLSSYTDDKILISYFNKFFKRIKSSSVLNMRYKNDKYVDTSGYDSNININGDVYKYPTNKNQFGIYNDKLSEVNISQNDYIIYDNKYKNFSISFWVRIPNYDNKIVNVNNEYTIINCMRDNNSGWKVSLNHNEIIWTLQDNSGINQKLAFNYGNANGISDYINKWIFVTITNDRLGDSKLYINGNLIDKKSILNLGNIHVSDNILFKIVNCSYTRYIGIRYFNIFDKELDETEIQTLYNNEPNANILKDFWGNYLLYDKEYYLLNVLKPNNFINRRTDSTLSINNIRSTILLANRLYSGIKVKIQRVNNSSTNDNLVRKNDQVYINFVASKTHLLPLYADTATTNKEKTIKISSSGNRFNQVVVMNSVGNNCTMNFKNNNGNNIGLLGFKADTVVASTWYYTHMRDNTNSNGFFWNFISEEHGWQEK +Q8L168 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8L168_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAGTLSGDSSYTTLQRVAGISRTGMQINRHSLTTPYLDLMSHSGTSLTQSVARAMLPFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSVNAILGSVALILNCPHHASRVARIVPNEFPSMCPVDGRVRGITHNKILWDSSTLGAILIRRAISS +Q8L170 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8L170_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPQEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSVNAILGSVALILNCHHHASRVARMASDEFPSMCPADGRGRGITHNKILWDSSTLGAILMRRTISS +Q8VLE0 unreviewed hemolysis by symbiont of host erythrocytes [GO:0019836] Escherichia coli Q8VLE0_ECOLX MKKMFMAVLFALVSVNAMAADCAKGKIEFSKYNGDNTFTVKVDGKEYWTNRWNLQPLLQSAQLTGMTVTIKSNTCESGSGFAEVQFNND +Q8VLK6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8VLK6_ECOLX MKCILLKWILCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNIFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +Q8VV62 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8VV62_ECOLX MKCILLKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFGGNAMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +Q8VV64 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8VV64_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSVNAILGSVALILNCHHHASRVARIVPNELPSMCPVDGRVRGITHNKILWDSSTLGAILIRRAISS +Q8VV65 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8VV65_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGATSVSVINHTPPGSYISVDIRGLDIYEARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVARVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEDSVRVGRISFNNISAILGTVAVILNCHHQGTRSVRYVNEEMQPECQISGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q8VV67 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8VV67_ECOLX MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGGNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSVNAILGSVALILNCHHHASRVARIVPNEFPSMCPVDGRVRGITHNKILWDSSTLGAILIRRAISS +Q8VV70 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8VV70_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQTYVSSLNSIRTEISTPLEHISQGATSVSVINHTPPGSYISVDIRGLDIYEARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRYVNEEMQPKCQISGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q8VV71 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8VV71_ECOLX MKCILLKWILCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYRARFDHLRLIIEQNNLYVAGFVNTATNIFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +Q8VV72 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q8VV72_ECOLX MKCILLKWILCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNIFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAALRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +Q8X526 unreviewed cell wall macromolecule catabolic process [GO:0016998]; defense response to bacterium [GO:0042742]; killing of cells of another organism [GO:0031640]; peptidoglycan catabolic process [GO:0009253] Escherichia coli O157:H7 Q8X526_ECO57 MSRKLRYGLSAAVLALIAAGASAPEILDQFLDEKEGNHTTAYRDGAGIWTICRGATRVDGKPVIPGMKLSKEKCDRVNAIERDKALAWVEKNIKVPLTEPQKAGIASFCPYNIGPGKCFPSTFYRRINAGDRKGACEAIRWWIKDGGRDCRIRSNNCYGQVSRRDQESALACWGIDR +Q8X531 unreviewed hemolysis by symbiont of host erythrocytes [GO:0019836] Escherichia coli Q8X531_ECOLX MKKMFMAVLFALASVNAMAADCAKGKIEFSKYNEDDTFTVKVDGKEYWTSRWNLQPLLQSAQLTGMTVTIKSSTCESGSGFAEVQFNND +Q93EY5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q93EY5_ECOLX MKCILLKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWRRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +Q93HT3 unreviewed proteolysis [GO:0006508] Clostridium botulinum Q93HT3_CLOBO MPITINNFNYSDPVDNKNILYLDTHLNTLANEPEKAFRITGNIWVIPDRFSRNSNPNLNKPPRVTSPKSGYYDPNYLSTDSDKDTFLKEIIKLFKRINSREIGEELIYRLSTDIPFPGNNNTPINTFDFDVDFNSVDVKTRQGNNWVKTGSINPSVIITGPRENIIDPETSTFKLTNNTFAAQEGFGALSIISISPRFMLTYSNATNDVGEGRFSKSEFCMDPILILMHELNHAMHNLYGIAIPNDQTISSVTSNIFYSQYNVKLEYAEIYAFGGPTIDLIPKSARKYFEEKALDYYRSIAKRLNSITTANPSSFNKYIGEYKQKLIRKYRFVVESSGEVTVNRNKFVELYNELTQIFTEFNYAKIYNVQNRKIYLSNVYTPVTANILDDNVYDIQNGFNIPKSNLNVLFMGQNLSRNPALRKVNPENMLYLFTKFCHKAIDGRSLYNKTLDCRELLVKNTDLPFIGDISDVKTDIFLRKDINEETEVIYYPDNVSVDQVILSKNTSEHGQLDLLYPSIDSESEILPGENQVFYDNRTQNVDYLNSYYYLESQKLSDNVEDFTFTRSIEEALDNSAKVYTYFPTLANKVNAGVQGGLFLMWANDVVEDFTTNILRKDTLDKISDVSAIIPYIGPALNISNSVRRGNFTEAFAVTGVTILLEAFPEFTIPALGAFVIYSKVQERNEIIKTIDNCLEQRIKRWKDSYEWMMGTWLSRIITQFNNISYQMYDSLNYQAGAIKAKIDLEYKKYSGSDKENIKSQVENLKNSLDVKISEAMNNINKFIRECSVTYLFKNMLPKVIDELNEFDRNTKAKLINLIDSHNIILVGEVDKLKAKVNNSFQNTIPFNIFSYTNNSLLKDIINEYFNNINDSKILSLQNRKNTLVDTSGYNAEVSEEGDVQLNPIFPFDFKLGSSGEDRGKVIVTQNENIVYNSMYESFSISFWIRINKWVSNLPGYTIIDSVKNNSGWSIGIISNFLVFTLKQNEDSEQSINFSYDISNNAPGYNKWFFVTVTNNMMGNMKIYINGKLIDTIKVKELTGINFSKTITFEINKIPDTGLITSDSDNINMWIRDFYIFAKELDGKDINILFNSLQYTNVVKDYWGNDLRYNKEYYMVNIDYLNRYMYANSRQIVFNTRRNNNDFNEGYKIIIKRIRGNTNDTRVRGGDILYFDMTINNKAYNLFMKNETMYADNHSTEDIYAIGLREQTKDINDNIIFQIQPMNNTYYYASQIFKSNFNGENISGICSIGTYRFRLGGDWYRHNYLVPTVKQGNYASLLESTSTHWGFVPVSE +Q93K91 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q93K91_ECOLX MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQPLYTTGE +Q93K92 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q93K92_ECOLX MKCILLKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEMDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +Q94M00 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage 6220 Q94M00_9VIRU MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSVNAILGSVALILNCHHHASRVARIVPNEFPSMCPVDGRVRGITHNKILWDSSTLGAILIRRAISS +Q98965 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SA6_NAJAT MKTLLLTLVVVTIVCLDLGYTLKCNQLIPPFYKTCAAGKNLCYKMFMVAAQRFPVKRGCIDVCPKSSLLVKYVCCNTDRCNN +Q9DGH9 reviewed killing of cells of another organism [GO:0031640] Naja kaouthia (Monocled cobra) (Naja siamensis) 3SA2_NAJKA MKTLLLTLVVVTIVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKIFMVATPKVPVKRGCIDVCPKNSALVKYVCCNTDRCN +Q9F655 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q9F655_ECOLX MKCILLKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRSVNEEIQPECQITGDRPVIRLNNTLWESNTAAAFLNRRAHSLNTSGE +Q9FD43 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q9FD43_ECOLX MKRILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q9K395 unreviewed proteolysis [GO:0006508] Clostridium butyricum Q9K395_CLOBU MPKINSFNYNDPVNDRTILYIKPGGCQEFYKSFNIMKNIWIIPERNVIGTTPQDFHPPTSLKNGDSSYYDPNYLQSDEEKDRFLKIVTKIFNRINNNLSGGILLEELSKANPYLGNDNTPDNQFHIGDASAVEIKFSNGSQDILLPNVIIMGAEPDLFETNSSNISLRNNYMPSNHGFGSIAIVTFSPEYSFRFNDNSMNEFIQDPALTLMHELIHSLHGLYGAKGITTKYTITQKQNPLITNIRGTNIEEFLTFGGTDLNIITSAQSNDIYTNLLADYKKIASKLSKVQVSNPLLNPYKDVFEAKYGLDKDASGIYSVNINKFNDIFKKLYSFTEFDLATKFQVKCRQTYIGQYKYFKLSNLLNDSIYNISEGYNINNLKVNFRGQNANLNPRIITPITGRGLVKKIIRFCKNIVSVKGIRKSICIEINNGELFFVASENSYNDDNINTPKEIDDTVTSNNNYENDLDQVILNFNSESAPGLSDEKLNLTIQNDAYIPKYDSNGTSDIEQHDVNELNVFFYLDAQKVPEGENNVNLTSSIDTALLEQPKIYTFFSSEFINNVNKPVQAALFVSWIQQVLVDFTTEANQKSTVDKIADISIVVPYIGLALNIGNEAQKGNFKDALELLGAGILLEFVPELLIPTILVFTIKSFLGSSDNKNKVIKAINNALKERDEKWKEVYSFIVSNWMTKINTQFNKRKEQMYQALQNQVNALKTIIEFKYNSYTLEEKKELKNNYDIEQIENELNQKVSIAMNNIDRFLTESSISYLMKLINEVKINKLREYDENVKTYLLDYIIQHGSILGESQQELNSMVIDTLNNSIPFKLSSYTDDKILISYFNKFFKRIKSSSVLNMRYKNDKYVDTSGYDSNININGEIFIYPTNKNQFTIFNSKPSEVNISQNDYIIYDNKYKNFSISFWVRIPNYDNKIVNINNEYTIINCMRDNNSGWKVSLNHNEIIWTLQDNARINQKLVFKYGNANGISDYINKWIFVTITNDRLGDSKLYINGHLIDQKSILNLGNIHVSDNILFKIVNCSYTRYIGIRYFNIFDKELDETEIQTLYSNEPNTNILKDFWGNYLLYDKGYYLLNVLKPNNFIDRRKDSTLSINNIRSTILLANRLYSGIKVKIQRVNDSSTNDRFVRKNDQVYINYISNSSSYSLYADTNTTDKEKTIKSSSSGNRFNQVVVMNSVGNNCTMNFKNNNGNNIGLLGFKADTVVASTWYYTHMRDHTNSNGCFWNFISEEHGWQEK +Q9KXD2 unreviewed viral release from host cell by cytolysis [GO:0044659] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXD2_BPVT2 MNRVLCVVIIVLAVGYGALWLATNHYRDNALTYKAQRDKKARELEQANATITDMQVRQRDVAALDAKYSRELADARAENETLRADVAAGRKRLRINATCSGTVREATGTSGVDNATGPRLADTAERDYFILRERLITMQKQLEGTQKYINEQCR +Q9KXF0 unreviewed DNA unwinding involved in DNA replication [GO:0006268] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXF0_BPVT2 MTDNFYAPPHSIEAEQAVIGGLLLDDDSSERVQKVLAMLKPDSFYSRPHKIIFEEITRMHREQKPVDGLTLFDELERKSLTASVGGFAYIAEIAKNTPSAANIVAYAMQVRETAMERYAINRMTEATELLYSRNGMTATQKYEAIQAIFTQLTDHAKTGSRRGLRSFGEVMEDWVSDLEKRFDPSGEQRGMSTGIPSLDRMLSPKGLVKGSLFVIGARPKMGKTTLYSQMAINCAVHEKKPALMFSLEMPGDQILEKLVGQKSGVNPNIFYLPATNDADDGYQGDYDGDFNRAIETANRLSEIDLLYIDDTPGLSLAQIVSESRRIKREKGCVGMILVDYLTLMTAEKADRNDLAYGMITKGLKNLAKELDCVVVLLTQLNRALESRTNKRPLPSDSRDTGQIEQDCDYWVGIHREGAFDDSVPPGETELILRLNRHGNTGTVYCIQANGAIYDTDQQSAEMRRREREEPQSKKKGGF +Q9KXH6 unreviewed DNA integration [GO:0015074]; DNA recombination [GO:0006310] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXH6_BPVT2 MLLDAGGTMANSAYPAGVENHGGKLRITFKYRGKRVRENLRVPDTPKNRKIAGELRASVCFAIRTGTFDYADRFPDSPNLKLFGLVKKDITVGELAQKWLTLKAMEIGSNALNRYQSVMKNMLPRLGPGRLASSITKEDLLFIRKDLLTGEKGSRKTSTSRKGRTVPTVNYYMTTTAGMFSFAAENGYLEKNPFNSITPLRKSKPVPDPLTRDEFSRLIDACHHQQTKNLWTVAVFTGMRHGEIAALAWEDIDLKAGTITVRRNFTKIGDFTLPKTDAGTNRVIHLLAPAIEALKNQAMLTRLSRQHQITVQLREYGRTILHECTFVFCPQIVRKNHKAGINYAVSSIGATWDSAIKRAGIRSRKAYQSRHTYACWALSSGANPTFIASQMGHSSASMVYNVYGAWMPECSVTQVAMLNNVLNARAPDVPQSDQEDEIKLYFSK +Q9MBZ8 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage phiP27 Q9MBZ8_9CAUD MKCILLKWILCLLLGFSSVSYSQEFTIDFSTQQSYVSSLNSIRTAISTPLEHISQGATSVSVINHTPPGSYISVGIRGLDVYQERFDHLRLIIERNNLYVAGFVNTTTNTFYRFSDFAHISLPGVTTISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEAGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNKLWESNTAAAFLNRKSQPLYTTGE +Q9PS34 reviewed killing of cells of another organism [GO:0031640] Naja oxiana (Central Asian cobra) (Oxus cobra) 3SA5_NAJOX LKCKKLVPLFSKTCPAGKNLCYKMFMVAAPHVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +Q9PST3 reviewed killing of cells of another organism [GO:0031640] Naja sputatrix (Malayan spitting cobra) (Naja naja sputatrix) 3SA2B_NAJSP MKTLLLTLVVVTTVCLDLGYTLKCNKLVPLFYKTCPAGKNLCYKMYMVATPKVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +Q9REC3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q9REC3_ECOLX MRHILLKLVLFFCVCLSSASYADEFTVDFSSQKSYVDSLNSIRSAISTPLGNISQGGVSVSVINHVPGGNYISLNVRGLDPYSERFNHLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQDLTEPNQ +Q9S5J2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q9S5J2_ECOLX MKCILLKWILCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIERNNLYVAGFVNTATNIFYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +Q9S5J3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli Q9S5J3_ECOLX MKCILLKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIERNNLYVAGFVNTATNTSYRFSDFAHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEEVELTLNWGRISNVLPEFRGEGGVKMGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +Q9W6W6 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SAA_NAJAT MKTLLLTLVVVVTIVCLDLGYTLKCNQHIPPFYKTCAAGKNLCYKIFMVAAPKVPVKRGCIDVCPKSSDLVKYVCCNTDRCN +Q9W6W9 reviewed killing of cells of another organism [GO:0031640] Naja atra (Chinese cobra) 3SA4N_NAJAT MKTLLLTLVVVTIVCLDLGYTRKCNKLVPLFYKTCPAGKNLCYKMFMVSNLTVPVKRGCIDVCPKSSLLVKYVCCNTDRCN +S6BK96 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 S6BK96_ECO57 FSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +U3SXR2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O91 U3SXR2_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPEEVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITGDRPVIRINNTLWESNTAAAFLNRRAHSLNTSGE +U3SXS2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O91 U3SXS2_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +U3T0F0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O91 U3T0F0_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQVLSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILSTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +U3T2R3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O91 U3T2R3_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILSTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +V5URC7 unreviewed negative regulation of translation [GO:0017148] Shigella phage 75/02 Stx V5URC7_9CAUD MKIIIFRVLTFFFVIFSVNVVAKEFTLDFSTAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVTFPGTTAVTLSGDSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRSYVMTAEDVDLTLNWGRLSSVLPDYHGQDSVRVGRISFGSINAILGSVALILNCHHHASRVARMASDEFPSMCPADGRVRGITHNKILWDSSTLGAILMRRTISS +V9VJB8 unreviewed negative regulation of translation [GO:0017148] Escherichia coli V9VJB8_ECOLX MRHILLKLVLFFCVCLSSVSYADEFTVDFSSQKSYVDSLNSIRSAITTPLGNISQGGVSVSVINHVPGGNYISLNVRGLDPYSERFNHLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRTSIKVNNVLWEANTIAALLNRKPQGLTELNQ +V9VJC5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli V9VJC5_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGATSVSVINHTPPGSYISVDIRGLDVYEARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEDSVRVGRISFNNISAILGTVAVILNCHHQGTRSVRYVNEEMQPECQISGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +V9VLJ5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli V9VLJ5_ECOLX MRHILLKLVLFFCVCLSSVSYADEFTVDFSSQNSYVDSLNSIRSAISTPLGNISQGGVSVSVINHVPGGNYISLNVRGLDPYSERFNYLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRTSSRAMLRFVTVIAEALRFRQIQRGFRPALSETSPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSSILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQGLTEPNQ +W6JGT5 unreviewed negative regulation of translation [GO:0017148] Escherichia coli W6JGT5_ECOLX MRYILLKLVLFFCVCLSSASYADEFTVDFSSQKSYVDSLNSIRSAISTPLGNISQGGVSVSVINHVPGGNYISLNVRGLDPYSERFNHLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSEASPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSAILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQDLTEPNQ +A0A024A2U8 unreviewed Vibrio cholerae A0A024A2U8_VIBCL MIKLKFGVFFTVLLSSAYANGTPQNITDLCAEYHNTQIHTLNDKIFSYTESLAGKREMAIITFKNGATFQVEVPGSQHIDSQKKAIERMKDTLRIAYLTEAKVEKLCVWNNKTPHAIAAISMAN +A0A059U2X2 unreviewed Vibrio cholerae A0A059U2X2_VIBCL MFSSLKNKLNTFKSTLSLGVFLLFSAFANQALAAADAGLVTEVTKTLGTSKDTVIALGPLIMGVVGAIVLIVTVIGLIRKAK +A0A097PAT7 unreviewed modulation of host virulence by virus [GO:0098676] Streptococcus pyogenes phage T12 (Bacteriophage T12) A0A097PAT7_BPT12 MENNKKVLKKMVFFVLVTFLGLTISQEVFAQQDPDPSQLHRSSLVKNLQNIYFLYEGDPVTHENVKSVDQLLSHDLIYNVSGPNYDKLKTELKNQEMATLFKDKNVDIYGVEYYHLCYLCENAERSACIYGGVTNHEGNHLEIPKKIVVKVSIDGIQSLSFDIETNKKMVTAQELDYKVRKYLTDNKQLYTNGPSKYETGYIKFIPKNKESFWFDFFPEPEFTQSKYLMIYKDNETLDSNTSQIEVYLTTK +A0A0A1V4Z3 unreviewed Metarhizium robertsii A0A0A1V4Z3_9HYPO MRQIWLSITIWTAVLFLLTSAEQPLSSEINRGRFKRSIVPTIPRSGSAKHVETSWRLNDRALDDGLPDGASENTAELQKFKIKYEEIPFKLDEAWTKLEHTKGFSLRGEKWFSTGKGIVYPAELMKQNKGLWPRNYDPTTGKPKPFPEAARGSDEFKSTSLWEHVNGQTKSTTRWMSMTKSVHKSIEFSANWENGKPSKVLTQPEGRDGLDLNVIWKIQDAPNVVDTHGSLANKGANGEFVSNEKFIPSDYLDQVEYSAFEGIPTEQIKGYFRTELILRNPGLAEKIANGEEPEGVFVKNIDYNDKFDSLSHAGARPDLAGFPPVGIDQGTMKKEGWERQPFELEPWNKFDQGPQGETEAQKTQRRVQSHKDRARDFEDKSEREILASVCSTNLRKRAGSLCNSDTSAPAPGNDRPEPGKPIEEEVKPTDPNGEELPKAIDDAKGVELTRKTSDDVFSDLFSSQKMGEKAAKLGVKAGEARTRLSGYELITAESPKLKLSGVKLAGEGAGVALWVTGMVQAFTSHTTGLQRAAAVTAIIPFVGCGFSALAEADSEEGVDVIDKMMCIYADILLLTPLAPFSFVIQVFRGIMSLYKPKALPKAEDVQDNRDKRWARFLDDRVFTYFYSDDSVSNNRVFRDKLNGTLFADHLGVVSDAADFIAIANTSARVALQAQNADKPKIQAGALDVANQITAEISPMIVRRQREFLLKLPQLVLRDTKLSLQPIAEQFNKELIEHMTSEAMVRQYTPLVPAIDGQPLGDEENLKDQTRGSLHAIGEHLKKNPPPLPKLFDIAYVLGQSKGMLDINPLSLSPGEFIKSRAPDLSQDDVDFYSLHHALQISLLLRGKLTEDKLSKLWPSDDASTVQQLQLLLALKFGKIYDEQKVLWVKSQSGVTGNFLGEKEVRWSTHPNIPPHGRDEESMAYLGLILDLSEERIKSIPRHKEVIGFKDLGTDAKLITAMLEHAKELYIKKVDEEAVAMQDDKTNDGEKDKGA +A0A0B2WM64 unreviewed Metarhizium album (strain ARSEF 1941) A0A0B2WM64_METAS MKAYKFIAIFVGQAFAASSSDLETPGTLKGTTANAASELQLSGQATQVIRDDAKAAYHDYNPRSVKRQITTTPAIVYRVDTLSPKQVRAQGGFPPRQSSLAEREFLATGGIRWDQVISSTRLRNGRDTPGKDRVVRRNKDYNAIYNELRAGGPQYQLAGFPKGHEAWTKERWSPYLEADISESGLRFADESGKAVGLSRDSPLQFDITYKVGKASAFYRPSPKKNKDLPPSKGKGQENSVLCRRSGQTSQDCDDRREEEVAEEEPPAGAKQIEAARRAESVSDKEFVELATKYELSRVPWEWQAEGIKSLSDVRTKLLAYRPIEPTSPRLRPGNGGAIMKVVGEAGEALWVTSVVHAFVTNSTALGRAAAITAIIPFVGCAVNTAAEVEEREDSAIITIDSALCFIGDALILGGLVPLGFVFHLARIIIQAFTPPPEPPTKEELLKVRDELWNHFLHEHVFRYIYSHSYNDPKQEFAFKLNNTLAITSRSVVSEAAQTIGALNASSEIHESYMDSEPGLDPVKLETGSREAIGKIRDSLAAEILRKQREVILGIPTALHEGEALSLRETGEAFNTKFITRLTSRETVEAYSPLKWKQFFLEGLLMLGPGPGDAFLTPRIEEYPYQHARAHLVRAGEYLKAVPVMMPRILDTAFVIGQSKGLADIHRRLLSGRDYIKQEAKRLGGFDLPDTSVDVFAILQAIDVAKVLQGRLKEENITRVFPVENDEACRGLQLLLAMKCGRLYEDAKLEAAERERRRNPWILSTEPPEGLVNPLIPPIPRDPDGFLVSLMLGIIDTVLDVEYDESANGPLRDELDRIQTRLTKLREMVQDTVALASFGQKESNHRAPQLV +A0A0B2WNV7 unreviewed Metarhizium album (strain ARSEF 1941) A0A0B2WNV7_METAS MEKYNLLSHVRGNIKNTAYVSTTTDIHTAVRYALPDGPGRRGYVYKIEPSTTFVDVNRSLRIPRYWSNMEFAAMKGIPISQVVGWVELTEDVIELLQLGKTDQIKFTENTSWKALPEWLKGGTTRPELASFPKGDKAWKLHPWKELKNARESVEVLFKKIVASEHPSVVLAAKAETELMALAETLSEKHLSAVTRRNGLKRLARGRWKMELSQVRTSVLGRVKLSEAWKVPTKGLAVAGVAAWAYGVVDAFTSESSGWDKAAAVTAIIPLAGCAANLVAGADDPNSEGVLVAVDTGLCLLGDALLLGGASLPLGILVHVSRSLLQFFRAPPKLPSVQEILKMRDDPWQSFVDQHLTNFLASKAWRDKLEASVAVEALAIWSMAADYMGRLEAVGRTVSNASHDVQEAIHQIEERAQGEVVKRQRAVLLQLPQRVRADLTVLMERALSEYNEDFIKKLNSEEVVGRYPGYSKAVGWFIPDTWVYKDSRERMRDASSVLRQNPPQLPSLLTLAYFVGVAAGVDDPPPPRVDPNSNKPGFAKFIPAEPEDWKNAVNQTVMDPSMYYAEKTGNSSSQELIRHTMHVVDCLLGKSQEQELPTTVLGLSESSHVREFQLLIAMHMGSTFADWKEARGSKVGYMHEDYQKDIAGLVERLYDIPRDSASRSISQIQRTRLRPGAVSDYYRYHVS +A0A0B2X5F7 unreviewed Metarhizium album (strain ARSEF 1941) A0A0B2X5F7_METAS MKTLWVSPSVWIAALVVSASGHTLPSADAASYNDKRSTESQQVSVFRGEYGRTPQQVADVGGLMSKGYARNFDVPLTPAERGWGSSLTEHISGRNKAVSLFVSTTYDPVIAMKFASGGETAEFYIYEAALDDKAIDVEKSIRKFERSYAATQKEVAFVKGIPNEQIRGHYVMRPGDYDPTTHLGIDEFKHRFADRWVPNPQFNQRYLSTKSARTQSQFDKLYKGPTTAQQEAFAKSYAAQLGKKDVQTPQSAAKALESFFKKASPSEKLLESSRPNKSRLPKMKPVDGSKVRTKPGKGPAEVGPRPGPHVPGTPEVRPKPKPVDLAPAKPKDVALAKNAEMVAAAERLSEKEFASASRRYGLKAIVEGRWNTDLSVVRSKALGRGKLSLSSPKLLPKGSKLKAGMGALGAAGIGLWIHGMVEAFTTETSDWDKAAAVTAIIPFVGCGTNLIAQAEKADANTALVAVDTGLCVLGDALLLGGVTAPLGVVVHLSRFLVQFFEPPPDLPTYQEIHAMRDKPWQAFLEEHLNKHLVSEEWRKKLEAAVAIESLGIWSKAADRVGLLEAAKATVLQTPDLSESRGEAQKAIEQVRNGAKAEILRRQRRYLLSLPQTLHEGFAKWLAATADTYNADFVRKIKSDDMVERYPSYSKVVAWMFSRQRVYAASHERMATAAWLLEGDPPPMPSLFTLAYFVGVSAGVDDPPPPRINPKGGPGFAKFTPAEPGDWKCAVDPDVIDPMLYLEEKTGRKRHLVLVRHTVAVVRLLLGHIKEFQLPAEGEGLSNVQEFQMLIAMYIGNTFADWKEIHRHKVGYIHKDFEKDMIGLMQRLYNIPRAEAGQLVRTAGEHA +A0A0B4F8L3 unreviewed Metarhizium anisopliae (strain ARSEF 549) A0A0B4F8L3_METAF MRQIWLSITIWTAVLFLLTSAEQPLSSAINRGRFKRSIVPSIPHSGSAKHVETSWRLNDRALDDGLPDGASENTAELQKFKIKYEEIPFKLDEAWTKLEHTKGFSLRGEKWFSTGKGIVYPAELMKQNQGLWPRNYDPTTRKPKPFPEAARGSDEFKSTSLWEHVNGQTKSTTRWMSMTKSVHKSIEFSANWENGKPSKVLTQPEGRDGLDLNVIWKIQDAPNVVDTHGSLANKGANGEFVSNEKFIPSDYLDQVEYSAFEGIPTEQIKGYFRTELILRNPGLAEKIANGEEPEGVFVKNIDYNDKFDSLSHAGPRPDLAGFPPVGIDQGTMKKEGWERQPFELEPWNKFDQGPQGETEVQKTKRRVQSHKDRARDFEDKSEREILASVCSTNLRKRAGSLCNSDTSAPAPGNDRPEPGKPIEDEVKPTNPNEEELPKAIDDAKGVELTRKTSDDVFLDLFSSQKMGEKAAKLGVKAGEARTRLSGYELITAESPKLKLSGVKLAGEGAGVALWVTGMVQAFTSHTTGLQRAAAVTAIIPFVGCGFSALAEADSEEGVDVIDKMMCIYADILLLTPLAPFSFVIQVFRGIMSLYKPKALPKAEDVQDNRDKRWARFLDDRVFTYFYSDDSVSNNRVFRDKLNGTLFADHLGVVSDAADFIAIANTSARVALQAQNADKPTIQAGALDVANQITAEISPMIVRRQREFLLKLPQLVLRDTKLSLQPIAEQFNKELIEHMTSESMVTQYTPLVPPIDGQPLGDEENLKDQTRGSLHAIGEHLKKNPPPLPKLFDIAYVLGQSKGMLDINPLSLSPGEFIKSRAPDLAQDDVDFYALHHALQISLLLRGKLTEDKLSKLWPSDDANTVQQLQLLLALKFGKIYDEQKVLWVKSQSGVTGNFLGEKEVRWSTHPNIPPHGRDEESMAYLGLILDLSEERIKNIPRHKEVIGFNDLGTDAKLITAMLEHAKELYIKKVDEEAVAMQDDKANDGEKDKGA +A0A0B4GRD1 unreviewed Metarhizium guizhouense (strain ARSEF 977) A0A0B4GRD1_METGA MRQIWLSITIWTAVLFLLTSAEQPLSPEINRGRFKRSIVPSIPRSDSAKHVETSWRLNDRALDDGLPDGASENTAELQKFKIKYEEIPFKLDEAWTKLEHTKGFSLRGEKWFSTGKGIVYPAELMKQNKGLWPRNYDPTTGKPKPFPEAGRGSDEFKSTSLWEHVNGQTKYTTRWMSMTKSVHKSIEFSANWENGKPSKVLTQPEGRDGLDLSVIWKIQDAPNVVDTHGSLANKGANGEFVSNEKFIPSDYLDQVEYSAFEGIPTEQIKGYFRTELILRNPGLAEKIARGEEPEGVFVKNIDYNDKFDSLSHAGARPDLAGFPPGGIGQGTMKKEGWERQPFELEPWSKFDQGPQGEAEGQKIQRRVQSHKDRAREFEDKSEREILASVCSTNLRKRAGSLCNSDTSAPVPGNNEPEPGKPTEEEIKPTNPNEEELPKAIDDAKGVELTRKTSDDVFSDLFSSQKMGEKVAKLGVKAGEARTRLSGYELLTAESPKLKLSGVKLAGEGAGVALWVTGMVQAFTSHTTGLERAAAVTAIIPFVGCGFSALAEADSQEGVDVIDKMMCIYADILLLTPLAPFSFVIQVFRGIMSLYKPKALPKAEDVQDNRDKRWARFLDDRVFTYFYSDDSVSNNRVFRDKLNGTLFADHLGVVSDAADFIAIANTSARVALQAQNADKPKIQAGALDVANQITAEISPMIVRRQREFLLKLPQLVLRDTKLSLQPIAEQFNKELIEHMTSESMVRQYTPLVPAIDGQPLGDEEHLKDQTRGSLRAIGDHLQKNPPPLPKLFDIAYVLGQSKGMLDINPLSLSPGEFIKSRSPDLSQDDVDFYALHHALQISLLLRGKLTEDKLSKLWPSDDANTVQQLQLLLALKFGKIYDEQKVLWVKSQSGVTGNFLGEKEVRWSTHPNIPPHGRDEESMAYLGLILDLSEERIKSIPRHKEVIGFKDLGTDAKLITAMLEHAKELYIKKVDEETVAMQDDKANEGEKAKSV +A0A0B4GTV8 unreviewed Metarhizium guizhouense (strain ARSEF 977) A0A0B4GTV8_METGA MLAQGPLTLLLCMLIACLEWSIGAHGLSIRQESLPTLLYRFDTRKPDVIKKAGGFSPRSMDIQNEEGASVYLHMRGDPAGGAPKDSIYVSTTIDAQIAAKEVAAGREGYIYEIHATENMFSVSDTLKNHYIYLSQKEWVAVLGIRSDQIKSVAKVKARKATGAKPELEFKPFKGYNEAKYNGHTASPPEPQLAGFPPNHPALEEPQWKALSGKSLKDAASEFLKRAEAKGWELPEFREGGGCTISKRGLCDEAGNGAEDQGLKNPEKEGDEQVPGTGEEPPRPPELNKEGEVPSTGELVKSVEDVSSTEFRALAGRYKVLSFVENKLKLKLSLFRSDTLKYKPLGSALNHVKAPPSFGKLALKSLNKSFKALGLGLYAHGVIDAFANNVSAIDRAAALTAIIPVVGCATQIAADIAGGQANALDAIDDAMCIVGDVLLFNPATAPFGVAVHIARFIIGLIRAALPPPGPDPEVLREIKKIRDEAWTEFLQQHVYKELSSREFGDNMRSALATETLSLLSEGAQTIGVLVKSQELALNKTDDKTELEQLKSFFQNGTENVRKRLDSTILDAQRRYMINLVANVRDQDKNASIQAAADAYNEFFVTKNLTDAGADESIKQSIRSEKLPMPDSFTLAYLIGQGTAQDIQVPTQPAPEIEKALTSPEPKDEKGLALRQAAANDGTNWEMHPLALSLPDYLQLKVQGMSKQDADTVSIQQTAALIKFFDGNVAEEDLEKASPKLEGVDIKEFQILAALNMGRKLKLWKLTEDRKLHFVPESAASIPAGTIASVLGLSEQDVQVVLGK +A0A0D9NW94 unreviewed Metarhizium anisopliae BRIP 53293 A0A0D9NW94_METAN MRQIWLSITIWTAVLFLLTSAEQPLSSEINRHRFKRSIVPTIPRSGSAKHVETSWRLNDRALDDGLPDGASENTAELQKFKIKYEEIPFKLDEAWTKLEHTKGFSLRGEKWFSTGKGIVYPAELMKQNKGLWPRNYDPTTGKPKPFPEAARGSDEFKSTSLWEHVNGQTKSTTRWMSMTKSVHKSIEFSANWENGKPSKVLTQPEGRDGLDLNVIWKIQDAPNVVDTHGSLANKGANGEFVSNEKFMPSDYLDQVEYSAFEGIPTEQIKGYFRTELILRNPGLAEKIANGEEPEGVFVKNIDYNDKFDSLSHAGARPDLAGFPPVGIDQGTMKKEGWERQPFELEPWNKFDQGPQGEAEGQKIQRRVQSHKDRARDFEDKSEREILASVCSTNLRKRAGSLCNSDTSAPAPGNDRPEPGKPTEEEVKPTDPNGEELPKAIDDAKGVELTRKTSDDVFLDLFSSQKMGEKAAKLGVKAGEARTRLSGYELITAESPKLKLSGVKLAGEGAGVALWVTGMVQAFTSHTTGLQRAAAVTAIIPFVGCGFSALAEADSEEGVDVIDKMMCIYADILLLTPLAPFSFVIQVFRGIMSLYKPKALPKSEDVQDNRDKRWARFLDDRVFTYFYSDDSVSNNRVFRDKLNGTLFADHLGVVSDAADFIAIANTSARVALQAQNADKPKIQAGALDVANQITAEISPMIVRRQREFLLKLPQLLLRDTKLSLQPIAEQFNKELIDHMTSESMVTQYTPLVPAIDGQPLGDEENLKDQTRGSLHAIGEHLKKNPPPLPRLFDIAYVLGQSKGMLDINPLSLSPGEFIKSRAPDLSQEDVDFYALHHALQISLLLRGKLTEDKLSKLWPSDDASTVQQLQLLLALKFGKIYDEQKVLWVKSQSGVTGNFLGEKEVRWSTHPNIPPHGRDEESMAYLGLILDLSEERIKSIPRHKEVIGFKDLGTDAKLITAMLEHAKELYIKKVDEEAVAMQDDKANDGEKDKGA +A0A0F8A0C8 unreviewed Hirsutella minnesotensis 3608 A0A0F8A0C8_9HYPO MEANSGFSVALHVKGVPPSGRSPENSVYVSTTDDPLVAAKFLRGEKGYVYKIRAVPRMFSVAGTLQKYYDYPFPVSNQREWVAMGGVSWDQIESAAFIKPTDSGVVAPDTKFNWKANRAFARGEYSKLTPDITAGAGRPELAGFDEIPKHPGSTDPLFRDLQSKRTLKEYGVDYLKDSVKAEKGLPWDGQGPLLKPVEPKSGSRSRGRVCVISKRDGSGCQLRARSSAAKRYDAYARKHGLAQLATRKNRPLSFATLSETVSSKFGGKLGYRAKLAAAKGIGGAVGAVSLGIWAKSVSDIFSTESSALERVAVTTSLIPVVGCVTQGASDSTGENARNSVVNGIDTAVCIVADVLAFIPGGGTVSFALHLARSLFRILESLAFPKEPPPIEKTYFQYRWGWDQYFYHMDKMLSSSEFKGNLTSQLIAAKADVLLMAAEAAGTIDATLSIGAANETEGVNQGDSEAEDDIDIDESDAKPDAAPGQQSPPEPKDGLGKRDGEGEPIAPNPSDGDNQVPDLEPLICKSYNERKKQIFQNLTAKLEGHMEEAYTKYDLEFFNNFHPWLYRFLRQHQPFPSVEELERKRDTHIEQVMKTYARDDSKPGLSPGPGANRLGELLQIVFDDVTRNDICKEPKKPPMIDGAEVLPGVEIGGDLPEAQRKKEEQEGKWRDELQLVSLAEKENKLKDAIWAQVRQEIWQMRVQSGAWPPREGSDAVDPARDFDKWARFDQLMKEAEEREQRAQGVKPSTPGNPGQNEDTSKDNAGSEESQANPPPGPTGSNVLLCASGGHNRPCLEAKTTPGHCVPMPPDWNDHTSSIRLNQEAGACNFYTDYHCAGAQLNPSAPGVDLLEEQNRNFDNQISSYRCAAETGTAEPSQSHTLFVAKTYAYKSSAQECIFYFETSTPDDGNWIGIFKTGQTPGTDQAWSWSWAKSGSGSANVNCAGLGPGYQAYLVGKDGNARLQGPMDL +A0A0H3JCG7 unreviewed Escherichia coli O157:H7 A0A0H3JCG7_ECO57 MKDLTPGEMTAESYDDNYLDDEDADWTATGQGQKSAGDTSFTLAWKPGEEGQKGLIGWFESGDVRAYKIRFPNGTVDVFRGWVSSIGKAVTAKEVITRTVKVTNVGKPSVAEERSKITPVSAIKVTPTSGTVAKGKTTTLTVSFEPESATDKTFRAVSADPSKATISVKDMTITVNGVATGKVQIPVVSGNGQFAAVAEVTVTEAGAAG +A0A0H3JCK6 unreviewed Escherichia coli O157:H7 A0A0H3JCK6_ECO57 MTFTVKTIPDMLVEAYENQTEVARILNCSRNTVRKYTGDKEGKRHAIVNGVLMVHRGWGKDTDA +A0A0H3JD17 unreviewed Escherichia coli O157:H7 A0A0H3JD17_ECO57 MKKKYELVVKGINNYPDKITVTVALEIGGYPSLLLPDVAISLDRTEGATLEFYEAEAKKQAKQFFMDVAAGLCEGDGPLPEKRPVILEAQDVLITYRGKLPGIITGSLKTPPLA +A0A0H3JDC3 unreviewed Escherichia coli O157:H7 A0A0H3JDC3_ECO57 MSDLSLTQPKLKECPFCGGNARLWVEAGINIDVWGYAECDLCEARGAWAPSVAAAAEKWNRRAGDEANLSASQRSNQK +A0A0H3JDS3 unreviewed Escherichia coli O157:H7 A0A0H3JDS3_ECO57 MLDTCRLASYAPKGKEKQAMKQQKAMLIALIVICITVIVTALVTRKDLCEVRIRTGQTEVAVFTAYEPEE +A0A0H3JDV0 unreviewed Escherichia coli O157:H7 A0A0H3JDV0_ECO57 MAKDLKTLALARLSGFRHKTVKVPEWRNVSVVLREPSAEAWYLWQEVLNGDGEDDDTLSVVAKTRRNLEADVTLFCDVLCDTDLQRVFTPDDREQVLAVYGPVHARLLRQALELIADAESARKK +A0A0H3JE68 unreviewed Escherichia coli O157:H7 A0A0H3JE68_ECO57 MTSKETFTHYQPLGNSDPAHTATAPGGLSAKAPAMTPLMLDTSSRKLVAWDGTTDGAAVGILAVAADQTSTTLTFYKSGTFRYEDVLWPEAASDETKKRTAFAGTAISIV +A0A0H3JE69 unreviewed Escherichia coli O157:H7 A0A0H3JE69_ECO57 MAEIKTLHLVPREGMQVSEKPSVVRVRFGDGYEQRRPTGLNPQLKTFQAVFRVTDESTRRWLDEFLSWHGGYRAFLWRPPKHNRTVRVVCREWSVTDNARYSDFSCTIEQVVN +A0A0H3JEE6 unreviewed symbiont-mediated killing of host cell [GO:0001907] Escherichia coli O157:H7 A0A0H3JEE6_ECO57 MYQMEKITTGVSYTTSAVGTGYWLLQLLDKVSPSQWVAIGVLGSLLFGLLTYLTNLYFKIKEDRRKAARGE +A0A0H3JEE9 unreviewed viral release from host cell by cytolysis [GO:0044659] Escherichia coli O157:H7 A0A0H3JEE9_ECO57 MRELKMKLCVLMLPLVVSACGSTPPAPVPCVKPPAPPAWIMQPAPDWQTPLNGIISSSENG +A0A0H3JEF1 unreviewed Escherichia coli O157:H7 A0A0H3JEF1_ECO57 MNTAFALVLTVFLVSGEPVDIAVSVHRTMQECVTAATEQKIPGNCYPVDKVIHQDNNEIPAGL +A0A0H3JES0 unreviewed Escherichia coli O157:H7 A0A0H3JES0_ECO57 MIPVWSTACPDWAERLKKGLSIIPAPIYPEQAAHALAIFKQLRIVDAPGSPTFGESCAQWVFDLVAALFGSYDAQTGVRHIKEVFILIPKKNSKSTLAAGIMMTALLLNWRQAAGYTILAPTVEVAANAFNPARDMVRRDDDLDDLCQVQTHIRTITHRVTDTTLKVVAADPNTVSGIKSVGTLIDELWLFGKQYKAEDMLREAIGGLASRPEGFVVYTTTQSNEPPAGVFRQKLQYARDVRDGKIHDPHFLPVIFEHPPEMVESGAHLLMENLAMVNPNLGYSVDEAFLYREYRKAREAGEEAFRGFMSKHANVEIGLALRSDRWAGADFWEQQGRRVSLDDILQRADVVTVGIDGGGLDDLLGMYVIGRDRETREWLGWGHAWAHETAVVRRKSEASRFQDLVACGDMTIVRRVGDDTAEVAEYVRRIHEAELLDHIGIDPSGVGQILDSLAEAGIPDGIVVGISQGWKLGGAIKTTERKLAEGVLVHGDQPLMAWCVGNARVEPKGNAILITKQASGRGKIDPLMALFNAVSLMSLNPEPKKKEYAVFFI +A0A0H3JF29 unreviewed Escherichia coli O157:H7 A0A0H3JF29_ECO57 MNILKKIMQRLCGCGKHDDRENGELLTAQLRLGPADILESDENGIIPEQDRVITQVVILDADKKQIQCVVRPLQILRADGTWENIGGMK +A0A0H3JF63 unreviewed Escherichia coli O157:H7 A0A0H3JF63_ECO57 MKKKYELVVKGINNYPDKITVTVAPEIGGYPSLLLPDVAISLDRTEGATLEFYEAEAKKQAKQFFMDVAAGLCEGDGPLPEKRPVILEAQDVLITYRGKLPGIITGSLKTPPLA +A0A0H3JF71 unreviewed cell division [GO:0051301] Escherichia coli O157:H7 A0A0H3JF71_ECO57 METLLPNVNTSEGCFEIGVTISNPVFTEDAINKRKHERELLNKICILSMLARLRPIQKGCWQ +A0A0H3JFE8 unreviewed DNA transposition [GO:0006313] Escherichia coli O157:H7 A0A0H3JFE8_ECO57 MTKNTRFSPEVRQRAIRMVLESQDEYDSQWAAICSIAPKIGCTSETLRVWVRQHERDTGGGDGGLTSAERQRLKELERENRELRRSNDILRQASAYFAKAEFDRLWKK +A0A0H3JFF7 unreviewed Escherichia coli O157:H7 A0A0H3JFF7_ECO57 MNTAAEAIRALSLQMPGFRRQMNEGWYQIRIRGEDTAPEAVYARLHEPLGEGAVIHIVPRLAGAGKGGLQIVLGAAAIVGSFFTAGATMALWGAALSAGGLTATTMLFSLGASMILGGVAQMLAPKAKTPEYRATDNGKQNTYFSSLDNMIAQGNPMPVPYGEMLVGSRRISQDISTRDEGGDGKVVVIGRG +A0A0H3JGA4 unreviewed Escherichia coli O157:H7 A0A0H3JGA4_ECO57 MGKGGGKGHTPVEAKDNLKSTQMMSVIDAIGEGPIEGPVKGLQSILVNKTPLTDTDGNPVIHGVTAVWRAGEQEQTPPEGFESSGSETALGVEVTKAKPVTRTITSANIDRLRVTFGVQSLVQTTSQGDRNPASVRLLIQLQRNGNWVTEKDVTINGKTTSQFLASVILDNLPPRPFNIRMVRETADSTTDQLQNRTLWSSYTEIIDVKQCYPNTAIVGLQVDAEQFGGQQMTVNYHIRGRIIQVPSNYDPEKRTYSGIWDGSLKPAYSNNPAWCLWDMLTHPRYGMGKRLGAADVDKWALYAIAQYCDQTVPDGFGGTEPRMTFNAYLSQQRKAWDVLSDFCSAMRCMPVWNGQTLTFVQDRPSDVVWPTPAVMWWWMITAWGFATASAP +A0A0H3JGQ7 unreviewed proteolysis [GO:0006508] Escherichia coli O157:H7 A0A0H3JGQ7_ECO57 MTQTESAILAHARRCAPAESCGFVVSTPEGERYFPCVNISGEPEAYFRMSPEDWLQAEMQGEIVALVHSHPGGLPWLSEADRRLQVQSDLPWWLVCRGTIHKFRCVPHLTGRRFEHGVTDCYTLFRDAYHLAGIEMPDFHREDDWWRHGQNLYLDNLEATGLYQVPLSSAQPGDVLLCCFGSSVPNHAAIYCGDGELLHHIPEQLSKRERYTDKWQRRTHSLWRHRAWRASAFTGICNDLAAASTFV +A0A0H3JGX0 unreviewed Escherichia coli O157:H7 A0A0H3JGX0_ECO57 MQYAIAGWPVAGCPSESLLERITRKLRDGWKRLIDILNQPGVPKNGSNTYGYPD +A0A0H3JHD2 unreviewed Escherichia coli O157:H7 A0A0H3JHD2_ECO57 MAISAGRLTQMISVLNPVLTRNAAGEMTEEWVSCGKIHADIRGRSSRERMQSGAEMAQAEIRIWVRGQSGREITAASRLHVLSGPWRDRILNVVGLPVPDATGGRLEILCRLGGEK +A0A0H3JI47 unreviewed viral life cycle [GO:0019058] Escherichia coli O157:H7 A0A0H3JI47_ECO57 MVTVAELQALRQARLDLLTGKRVVSVQKDGRRIEYTAASLDELNRAINDAESVLGTTRRRRRPLGVRL +A0A0H3JIP3 unreviewed Escherichia coli O157:H7 A0A0H3JIP3_ECO57 MTFLNQLMLYFCTVVCVLYLLSGGYRAMRDFWRRQIDKRAAEKISASQSAGSKPEEPLI +A0A0H3JJL1 unreviewed proteolysis [GO:0006508] Escherichia coli O157:H7 A0A0H3JJL1_ECO57 MTQTESAILVHARRCAPAESCGFVIGTPEGERYQPCVNISAEPEAYFRIAPEDWLQAEMQGEIVALVHSHPGGLPWLSEADRRLQIKSALPWWLVCRGEIHKFRCVPHLTGRRFEHGVTDCYTLFRDAYHLAGITLPDFEREDDWWRNGQNLYLDNMAATGFYRVPLSSAQAGDILLCCFGASVANHAAIYCGNGELLHHLPEQLSKRERYSEKWQRRTHSVWRHRHWHASAFTGICNDLAAASACT +A0A0H3JJM1 unreviewed Escherichia coli O157:H7 A0A0H3JJM1_ECO57 MTKDELIARLRSLGEQLNRDVSLTGTKEELALRVAELEEELDDTDETAGQDTPLSRENVLTGHENEVGSAQPDTVILDTSELVTVVALVKLHTDALHVTRDEPVAFVLPGTAFRVSAGVAAEMTERGLARMQ +A0A0H3JJM5 unreviewed viral life cycle [GO:0019058] Escherichia coli O157:H7 A0A0H3JJM5_ECO57 MTRQEELAAARAALHDLMTGKRVATVQKDGRRVEFTTTSVSDLKKYIAELEVQTGMTQRRRGPAGFYV +A0A0H3JJS0 unreviewed Escherichia coli O157:H7 A0A0H3JJS0_ECO57 MLNTCRLASYVPKGKEKQAMKQQKAMLIALIVICLTVIVTALVTRKDLCEVRIRTGQTEVAVFVDYESEK +A0A0H3JN46 unreviewed Escherichia coli O157:H7 A0A0H3JN46_ECO57 MLSSCIGVVMKDGALLRSSSLFIAYMGCLGWGSAYFYGWGTSFYYGFPWWIVGAGVDDVARSLFFAVIVIAIFLIGWGIGVVFFFAVKRKHSMQELNVFRLYFAVELLFVPAIIEFSILRQKIQVPLLLLSAAIALAVTISIRSYGRFLSVSCFYDKPFIKKHFFEIVMIAFVAYFWLFSFLTGYYKPQFKKEYEMINYNDGWYYVLARYDNCLVLSTSFNAGSKRFVIYQSAQDKNLQVDIVRTRI +A0A0H3WE84 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A0H3WE84_ECOLX INRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTAEALRFRQIQRGFRTTLDDLSGRS +A0A141AZM3 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A141AZM3_ECOLX ALSETAPVYTMTPGDVDLTLNWGRISNVLPEFRGEGGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEEIQPECQITG +A0A141AZM6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A141AZM6_ECOLX ALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITG +A0A142BWN1 unreviewed Corynebacterium diphtheriae A0A142BWN1_CORDP MSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIPGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +A0A142BWN2 unreviewed Corynebacterium diphtheriae A0A142BWN2_CORDP MSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNIEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +A0A142I6Y0 unreviewed Vibrio phage pre-CTX A0A142I6Y0_9VIRU MVKIIFVFFIFLSSFSYANDDKLYRADSRPPDEIKQSGGLMPRGQSEYFDRGTQMNINLYDHARGTQTGFVRHDDGYVSTSISLRSAHLVGQTILSGHSTYYIYVIATAPNMFNVNDVLGAYSPHPDEQEVSALGGIPYSQIYGWYRVHFGVLDEQLHRNRGYRDRYYSNLDIAPAADGYGLAGFPPEHRAWREEPWIHHAPPGCGNAPRSSMSNTCDEKTQSLGVKFLDEYQSKVKRQIFSGYQSDIDTHNRIKDEL +A0A142I6Y1 unreviewed modulation of host virulence by virus [GO:0098676] Vibrio phage pre-CTX A0A142I6Y1_9VIRU MIKLKFGVFFTVLLSSAYAHGTPQNITDLCAEYHNTQIHTLNDKIFSYTESLAGKREMAIITFKNGATFQVEVPGSQHIDSQKKAIERMKDTLRIAYLTEAKVEKLCVWNNKTPHAIAAISMAN +A0A167GAX3 unreviewed Metarhizium rileyi (strain RCEF 4871) (Nomuraea rileyi) A0A167GAX3_METRR MRSRWTLLVLSWIPLLLWANAAAAGEPFDVFRGDSRPPDVLEAEGGFLPSINPAPTDLYSLAKHLDYYKGPYGRVVTPYISTTKDLTTALQYSQKRAASTTGFTYVYRISTTPHIFDAAQSLSLFRHQEMAKEVSEYDAVGGILWQQVRGYYKIPLGASEEAINNILADPEKIFNTNAQYDIRFDKYYASSGQPQLDGSHGENTYENAREFMSRPGVGDVVGWQGDFPLFKDPLYDQRAIKEKESAEAAKDATTQASAASKEAKEALEAVKKTKDAITALAEADKAVKAAKLSVQSAKKVATIAQEHITIKTDTYNEAKQSAMQAREAAAEAIKEANYKAAQSYVREATNLAKTKTPESARQAIQKVRQIADLKTTIKARIIALSLTMDEVRQALDQEREKWAMVTSDNVPRQIPSEDSILASNREYYDMALDSEFKVLQHEHDKLRVAMEDLLRLEAKAEAATKTSRQILEKEKPATAHGDMDTAGKAVGGVAAGIGAVAGSAGLTKAAGSVGADIADRVPLVGAGAGERTPIAPAEVDSVLADIKNQIAVEAAPAPPSEPPSRSVACATLGKREICLDVEDESSLRPGEEGGKGKTKSPVEKAKPIEMTPARQSELMALAETISEKEFNSIAKKNGLQAVVERRWKTTLPEARVKVLGYQKMSLHVSPVTAQRLGVSVKVGTGALAAVGVGLYVKGVVDAFTTETTKWEKTAAVTAIVPFVGCVTNLVAQTEKENAEANAALIGVDTSLCLLGDMLLLGGWTAPLGIIVHLIRYLIQFFEAPPSLPSYKEVTEMRDKPWRSFLDEHLTRNIASKSWRDKLEGALTVHALEIWSQACDRVGILKAGSQLISDAAEGMSQTHHVRRDGDLETNADTDLSQIQAEIEPAIAKIWQEADDSIVSLQRQYLLSLPKSLRDDLEVSITATAKDYNEKFIVQLNSEDTIRKYPGYSKAIAWLYTEKTVYSDSRSQMEKFGNQLRDDLPPLPSYFTLAYFVGVAAGVEDPPPPKIDPHGGPGFAKFMPAEPEEWKCVVNSTTIDPARYYREKTGGDNQRVLVQQTVDVVYHLLGKLEESRLSTEIPGLTDAREFQMLLAMYIGNTFADWKETQGNRVGYIHDDYVHDMPAFINRVYDIPVSVAAEHILNL +A0A167KUT2 unreviewed Beauveria brongniartii RCEF 3172 A0A167KUT2_9HYPO MRVFTARQSRISWLSLTVLAAFFLFIGLSHGHPSSGNNLHQLSYRGAGDSKPKPPKDPKPPKGGDGPSVPPASGPSKGKKVGFKKNVDRLPKFELPKEPLVGYRGETRDPAAIEAAKGFFPRDPGRTLLKEETEAGSSIYSHVAEKIVKDYTKFVSTSMDPAAASDFGKSTGYVYVIAPDAKMVNVPETLGEFMPAQFAKESELVAVGQIPVDQVRGWIKKSTTIRPGDWLALREHGHKLSGEPLSEELKKQLGRNIADAYTPNPKYNAAKYDKTRASGGRPELAGFPKDSPAWKKAPWKPYQNKKVSESLNEFLNEVVCAKAEACVKAQLTSKAGEAGVPNPYVDVDPPCKETDLKKRQEGACRQLTEEDETETGDQEPEEGTEPSEGAEPQEPGNAEEGLGAEEKLDLARKTSEEVFEDLFTSRKLSQKATEWGLKVQDARTRVLGYKPLTETSPHFSLSKAGLALEGAGLVAWGVGMVQAFTSNSTGWERAAAVTALVPFVGCGFNAIAESQSKEGIDVIDQMLCITADILLFTPLAPFAFVVQVFRVINGLFKTKSVPKKEEVQETRDKAWSKILDDKVYTYYYSDDSLSRNRKFRDKLNGTLFADSMAIVSDAADLIAAANATMQASAKSPNVDKDQINAVVLNVTDQIKAQIPSKVAVRQREFLLQLPQLIKQGTDLSVHKMADQFNKETITHLTSNAMVKQYTPTVFVPPGDPAGPDLKEGVRRELGAVAAHLEKTPLPLPKLFDVAYVLGQSKGMQDIDPLTLHPGEFIKSRAPDMSQDDVDSYALFHALQISMLLRGSVSEEKLSKLWPTDDKKTVQELQLLLALKFGRVFDSQKMAAAAQLAGQHATPQEIKFMTHPSIPPLKIAPESMAYIGLILELSEERIKAVPHLKEQIGFKELGTDEKLIMAMLEKAKKLYVERDKEMTLPEEGQAAPAA +A0A167PB70 unreviewed Cordyceps fumosorosea (strain ARSEF 2679) (Isaria fumosorosea) A0A167PB70_CORFA MRVFTTRQSRISWLSLTVLAAFFLFLGLGHGHPASGGHLNQLAERGGSSSKPGKAPKPGKAPKPGKGPSGGESSTAPPPASYPKGKKVGFKKTVDRLPPFKQEGEPLVAYRGETREPEAIEVAEGFFPRAPTRAINPEEAELGSSIYGHVAVKVAKEYTGYVSTSMDAGAASDFGRSSGYVYVIAPDPKLINVPETLGKFMPPEFAKEAELAAAGSIPFEQIRGWIKKDATLRPGDWLNLRRHGHKLNGEPVSEALRQELGRGMSDAYTPNPKYEAAKYQNTKASGGRPELAGFPKDSPAWNEAPWKAHKNTKTSESLKKFVSEMVCAKAEACVKLQTTTKAGADGVPNPYEDVDPPCNPKETDLKKRAEGEGEVCILGADEDEEEFNGRQTEEEGNGSSEEGTGSSEEGAGLEEGTGLEESIGGKAGEKVEVARKASEEVFEDLFTSRKLSEKAAEWGLTKVKDARTRALGYKPLTEQSPHFSISKVGLALEGAGLFAWGVGIADAFTSNSTGWQKAAALSALIPFLGCGFNAIAESKTKQGIDIIDQMMCITADILMVTPLAPFAFVVQAFRMIAGLFKTDAVPEPTEVQETRDAAWRKLLDDRVYTYYYSESSLAPRNRRFRDKLNGTMLQDSMAVLSDAADLIAAAKATVQDAAKTNTKADNKQIDAVLLNVTDQIKAEITPKIVSRQREFLLQLPQRIKQGTDLSVQKMAEEFNKETIAHFQSSETIYHYTPTPPTGNDLRPATIRDLTKITDHLQKNPLPLPQLFDIAYVLGQSQGMLDIEKLTLHPGEFIKSRVPDMKQDDVDFYALHHTLQMSMLLRGSTTEEKMSKLWPTSDDKAVKELQVLLALKFGRVFDSQKHIRAAQLEGDVPTKAEEKFMTHPSIPPLKIAPESMAYIGLILELSEERIKAIPRHNEVVGFKELGTDEKLIMAMLRKAKQLFVEKQEKFDKIPDEGQAAKAA +A0A168C6B0 unreviewed Akanthomyces lecanii RCEF 1005 A0A168C6B0_CORDF MRIFTARQSRITWLSLTVLAAFFLFIGLSHGHPSPGGNLHQLSRRGNSDSKPKGPKPPKGPKGGEPSGPPPATAPSKGKKVGFKKNIDRLPKFELPKEPLVTYRGETRDPGAIEAAEGFFPREPTRALTEAETEAGSSIFSHVAVKVAKEYTKYVSTSMDPGAASDFGKSTGYVYVVAPDQKMVNVPETLGEFLPSEFAKESELAAVGKIPFEQVRGWIKKNTALRPGDWLKLRDLGPKLSGEPLTEELRQQLGKTLADAYTPNTKYDAAKYDKTRASGGRPELAGFPKDSPAWQDERWKPFQNKKVSESLKEFLSETVCAKEEACVAGQLTSKAGEAGVPDPYKDVDPPCKETDLKKRDGEGACVLPDDAPDEEEGTEPSNDTEPEGTENTEPAEEGLGGETPETGLSESEKVDLAQKTSNEVFEDLFTSRKLSEKASEWGLKVQDARTQLVGYKPLTEQSPHFSMSKAGLALEGAALVAWGVGMAQAFTSNSTGWEKAAAVTALIPFVGCGFNAIAESETKEGIDVIDQMLCITADILLFTPLAPFAFAIQVFRVIAGMFKTPSMPKAEEVQETRDKAWSTILDDKVYTYYYSDDSLSRNRKFRDKLNGTMYADTMAIVSDAADLIAAANATMQASAKGPNVDKEQINAVVLNVTDQIKAGISPKIVARQREFLLQLPQRIKEGTDLSVQKMAEQFNKETMTQLTSNAMVKKYTPFVPVVEGDPGVDLEPQVRGELGALAAHLEKTPLPLPKLFDVAYVLGQSKGMLDIDPQTLNPSAFIKSRAPEMSQDDVDFYALHHALQISMLLRGTTTEDKLSKLWPSDGDDKTVQELQLLLALKFGRTFDSQKMAAAAQLAGQFATPQEIQFMTHPSVPPLKIESESMAYMGLILDLSEERITGLPRFKEVVGFKDLGTDEKLIMAMLEKAKELFVERQENNNSTLPEEGQAAAAA +A0A172PZA6 unreviewed Corynebacterium diphtheriae bv. mitis A0A172PZA6_CORDP MSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYATWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +A0A172PZD5 unreviewed Corynebacterium diphtheriae bv. gravis A0A172PZD5_CORDP MSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +A0A172PZE5 unreviewed Corynebacterium diphtheriae bv. mitis A0A172PZE5_CORDP MSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +A0A1C9ZWR6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A1C9ZWR6_ECOLX MRHILLKLVLFFCVCLSSVSYADEFTVDFSSQNSYVDSLNSIRSAISTPLGNISQGGVSVSVINHVPGGNYISLNVRGLDPYSERFNYLRLIMERNNLYVAGFINTETNTFYRFSDFSHISVPDVITVSMTTDSSYSSLQRIADLERTGMQIGRHSLVGSYLDLMEFRGRSMTRASSRAMLRFVTVIAEALRFRQIQRGFRPALSETSPLYTMTAQDVDLTLNWGRISNVLPEYRGEEGVRIGRISFNSLSSILGSVAVILNCHSTGSYSVRSVSQKQKTECQIVGDRAAIKVNNVLWEANTIAALLNRKPQGLTEPNQ +A0A1J4M3Z6 unreviewed Acinetobacter baumannii A0A1J4M3Z6_ACIBA MNKNSYRIIYSKARQMFVAVAENVRSQTKTSGQSEASTQNNIDNTESQAFHQLWQVKALVASISLWMPLAPVYAGIVADSAANAANRAVIGAGKNSAGTVVPVVNIQTPKNGISHNIYKQFDVLAEGAVLNNSRQGATTKTVGNVAANPFLATGEARVILNEVNSSAASRFEGNLEVAGQMADVIIANPSGINIKGGGFINANKAIFTTGKPQLNADGSIKQFTVDQGKITVSANPNSKFGLGGNNNDANYVDLYARALELSAELRAKNDIQVIAGANNVSADLQDVTPKTGTGTAPTLAVDVKALGGMYANNIYLMGTEKGLGVTNAGTIQAVNNLVITSAGKIEHSGTISSTSKTQGLVNIQTTGTGTAADINSSGSINSNSALNIDSGNNLNVNAKEIIINNGSLASSPLIVNAKGNINLAADTRIMDDSQGGDVYIDAANINLAAGSELKSNRGTATIQVQKDLVAAKGAKLIAAKDLNVLSNGKLSLTENHIQASLGSINLQADSANTQNIIDLQGGTIYAGKDLNLYSSGDLNLKNLGFSLENAATRVKNIKAYSGRDLVWNNADKALPLITGMVQLDAANNLTVTAKEISNKDSIQLHANQIALNSALTSQKNIDVSSEIADLVLSQALKAQGDINLTALAGGVTANSLKATSSAGKISILANKNINLNSTQTSKAMPSADKDELTTDQTVISGLKGVTLGSIGDGIVNLQSVQVNASQGDILVSSNNGINLKANNDVVVTGDTGRFKTVNNVLKGQTVSIENSKSDIKIQNTDLSSTVGKLAINSRAGMSTIIDSVLTSKGNTELYAKDLLTLQGVNATSDQHLAVSSGRTVYSNAEYTPATKWIADKVTNLTSKGVTSVTATGNQVLQNTNLTGGAVLLEAGGFILGQTGLNLNAVGSDLLKNDTKLNSLNGDLTIQTNSNLTIDPKAYSLKAVGDIELVSKTGTLTLKGYGGTAGNGSEQVVNLDTANGSINLEGAKVDIQGSQLTAQKDIKIVSSKDDVLIDGVKNKFSGLQKLNLAQDYSLELDRLNKERLKLQSQEYFNDFSNMQKKYQYYNQVLNKYWDRSTLDQSKNENAVLWVNAYNDYHKNYNEFKNKYMVKINYPILGEPPILINLMDGSPFSADYVFSFKYDDNYLNDVKRNELFYKSNLNGSEHSETKLNSKNGNINVTSAKGLSISGGNISAQLGQVNLEASGILAEQYKSTTITESNPQPRILNASIIIDGHTDFYDKGNENDDNYSMRTLVSPTIINGDKGVNIRTVGKTKDDNLVLQATGITSKNGDVKIESNKSILFDAAVEQSYDRTTTTEKKKSWGGLKKKYITTVAENNDTNAASVDISGKNIFIETKKLDPSVDVKNPDNNIDIYSGRFTADGGKITINSGGNLNFYTVEESSDSNVDITKKSTFAGIKYNTSKTNATRTQVTELPATLKADYIGTKSGFDTRLVGTEFEYLKDATIEAGGKLDFIAAKTSITDLVKKEKNSVVWQSMQDKGSITETAQLPSFNGPVLPTFKAAGGLSVQVPISEKDANKVELRDEILKLANQPGNAYLKELVNRKDVDWQTVLLTQKDWDYKSQGLTAAGAALIVIIVTIATMGSGTAAAAGAAGGTAASGTTVGLGASMIGSAGITTTAAGAIVPSTLGAMANAAITSLATQASVGLINNGGDIGKTLKDLGSKDSIKNLAASVVTAGLMSQVGSALNLKPDSTLLPDRLANNFASSVGSTLVQTAIKGGDLGDNLQVALLAGLAGALQGELAQNLKGLEDVNYILHKIAHAAVGCAAAAATKQSCEAGAIGAGVGEIIAQEMLHGRNPTFVSAEEKAKIEAYSKLIAGTISAYAGYDVNTAANSATIAIQNNAFKDKALLKLEATRKYLDDKSKAALDGLINAYKKGDIEAAKKFKNQLDDSVTTWANSSSSYLNIETKSALGEIVIVVSDLVIPTNYVEILPVGKLSKVAKILKIGEDGTKSAGRLAEELAELQKVDIKFGKTLPGAKAPITVTAESNIGGKHMFDTNQTARPEVNRTNTPTLAAGNAKIDPSNPNLTMKNAHAEIALIQRAYDAGLTKGETMQVLVRGKEVCDHCGQVMKTMYERSGLSKLIIHDTTSGTTTTYYKVIDAKTKIATTKIEVAPIFGH +A0A1L5YR45 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A1L5YR45_ECOLX RISMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPV +A0A1U9WQX1 unreviewed Corynebacterium phage LGCM-VI A0A1U9WQX1_9CAUD MNRKLFALILIGALLGIGAPLSAHASVDDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGSEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFENRGKRGQDAMYEYMAQSCAGNRIRRSVSNSSSCLNLDWDAIRDKTKAKIESLKENGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVAETNSVFAGANYAAWAVNVAQVIDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMIAQAIPLVGELVDIGFAAYNFVESIINLFQVIHNSYNRPTYSPGHKTQPFLHDGYAVSWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTITGKLDVNKSKTHISVNGRKIRMQCRAIDGNVTFCRPKSPVYVGKGVHANLHVAFHRSNSEKIQSNEILSDSIGILGYQKIVDHTKVNSKLSLFFEIKS +A0A1W6JQQ9 unreviewed Corynebacterium phage LGCM-V2 A0A1W6JQQ9_9CAUD MNRKLFALILIGALLGIGAPLSAHASVDDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGSEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFENRGKRGQDAMYEYMAQSCAGNRIRRSVSNSSSCLNLDWDAIRDKTKAKIESLKENGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVAETNSVFAGANYAAWAVNVAQVIDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMIAQAIPLVGELVDIGFAAYNFVESIINLFQVIHNSYNRPTYSPGHKTQPFLHDGYAVSWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTITGKLDVNKSKTHISVNGRKIRMQCRAIDGNVTFCRPKSPVYVGKGVHANLHVAFHRSNSEKIQSNEILSDSIGILGYQKIVDHTKVNSKLSLFFEIKS +A0A1W6JQW4 unreviewed Corynebacterium phage LGCM-V3 A0A1W6JQW4_9CAUD MNRKLFALILIGALLGIGAPLSAHASVDDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGSEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFENRGKRGQDAMYEYMAQSCAGNRIRRSVSNSSSCLNLDWDAIRDKTKAKIESLKENGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVAETNSVFAGANYAAWAVNVAQVIDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMIAQAIPLVGELVDIGFAAYNFVESIINLFQVIHNSYNRPTYSPGHKTQPFLHDGYAVSWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTITGKLDVNKSKTHISVNGRKIRMQCRAIDGNVTFCRPKSPVYVGKGVHANLHVAFHRSNSEKIQSNEILSDSIGILGYQKIVDHTKVNSKLSLFFEIKS +A0A1W6JR77 unreviewed Corynebacterium phage LGCM-V4 A0A1W6JR77_9CAUD MNRKLFALILIGALLGIGAPLSAHASVDDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGSEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFENRGKRGQDAMYEYMAQSCAGNRIRRSVSNSSSCLNLDWDAIRDKTKAKIESLKENGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVAETNSVFAGANYAAWAVNVAQVIDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMIAQAIPLVGELVDIGFAAYNFVESIINLFQVIHNSYNRPTYSPGHKTQPFLHDGYAVSWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTITGKLDVNKSKTHISVNGRKIRMQCRAIDGNVTFCRPKSPVYVGKGVHANLHVAFHRSNSEKIQSNEILSDSIGILGYQKIVDHTKVNSKLSLFFEIKS +A0A1W6JRA5 unreviewed Corynebacterium phage LGCM-V5 A0A1W6JRA5_9CAUD MNRKLFALILIGALLGIGAPLSAHASVDDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGSEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFENRGKRGQDAMYEYMAQSCAGNRIRRSVSNSSSCLNLDWDAIRDKTKAKIESLKENGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVAETNSVFAGANYAAWAVNVAQVIDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMIAQAIPLVGELVDIGFAAYNFVESIINLFQVIHNSYNRPTYSPGHKTQPFLHDGYAVSWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTITGKLDVNKSKTHISVNGRKIRMQCRAIDGNVTFCRPKSPVYVGKGVHANLHVAFHRSNSEKIQSNEILSDSIGILGYQKIVDHTKVNSKLSLFFEIKS +A0A1W6JRA7 unreviewed Corynebacterium phage LGCM-V6 A0A1W6JRA7_9CAUD MNRKLFALILIGALLGIGAPLSAHASVDDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGSEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFENRGKRGQDAMYEYMAQSCAGNRIRRSVSNSSSCLNLDWDAIRDKTKAKIESLKENGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVAETNSVFAGANYAAWAVNVAQVIDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMIAQAIPLVGELVDIGFAAYNFVESIINLFQVIHNSYNRPTYSPGHKTQPFLHDGYAVSWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTITGKLDVNKSKTHISVNGRKIRMQCRAIDGNVTFCRPKSPVYVGKGVHANLHVAFHRSNSEKIQSNEILSDSIGILGYQKIVDHTKVNSKLSLFFEIKS +A0A1W6JRG3 unreviewed Corynebacterium phage LGCM-V7 A0A1W6JRG3_9CAUD MNRKLFALILIGALLGIGAPLSAHASVDDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGSEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFENRGKRGQDAMYEYMAQSCAGNRIRRSVSNSSSCLNLDWDAIRDKTKAKIESLKENGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVAETNSVFAGANYAAWAVNVAQVIDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMIAQAIPLVGELVDIGFAAYNFVESIINLFQVIHNSYNRPTYSPGHKTQPFLHDGYAVSWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTITGKLDVNKSKTHISVNGRKIRMQCRAIDGNVTFCRPKSPVYVGKGVHANLHVAFHRSNSEKIQSNEILSDSIGILGYQKIVDHTKVNSKLSLFFEIKS +A0A1W6JRL2 unreviewed Corynebacterium phage LGCM-V8 A0A1W6JRL2_9CAUD MNRKLFALILIGALLGIGAPLSAHASVDDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGSEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFENRGKRGQDAMYEYMAQSCAGNRIRRSVSNSSSCLNLDWDAIRDKTKAKIESLKENGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVAETNSVFAGANYAAWAVNVAQVIDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMIAQAIPLVGELVDIGFAAYNFVESIINLFQVIHNSYNRPTYSPGHKTQPFLHDGYAVSWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTITGKLDVNKSKTHISVNGRKIRMQCRAIDGNVTFCRPKSPVYVGKGVHANLHVAFHRSNSEKIQSNEILSDSIGILGYQKIVDHTKVNSKLSLFFEIKS +A0A1W6JRV1 unreviewed Corynebacterium phage LGCM-V9 A0A1W6JRV1_9CAUD MNRKLFALILIGALLGIGAPLSAHASVDDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGSEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFENRGKRGQDAMYEYMAQSCAGNRIRRSVSNSSSCLNLDWDAIRDKTKAKIESLKENGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVAETNSVFAGANYAAWAVNVAQVIDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMIAQAIPLVGELVDIGFAAYNFVESIINLFQVIHNSYNRPTYSPGHKTQPFLHDGYAVSWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTITGKLDVNKSKTHISVNGRKIRMQCRAIDGNVTFCRPKSPVYVGKGVHANLHVAFHRSNSEKIQSNEILSDSIGILGYQKIVDHTKVNSKLSLFFEIKS +A0A2A2D7J4 unreviewed Streptomyces albireticuli A0A2A2D7J4_9ACTN MWEGSTTLRDQSSRDAALSSASRARVQPLPDAAPSAGTPTGAAVRHVQDPAELPGRQGTAWKRRRHLRYGSSDSQRSRRSTATPSHKEQRSTDYPSTCASSKKVIAMTTAVENIQVAEITPSTRIVYRGVSPAEFIYLEGNKFSRAQSPTQGNDDPQWKALYTGSDANVSSRNITDNPGGVVKIEYPSDWKVLEITSTTPSQKWHNDMGEAWPVWRAVKKWAASNQVDLPDVTASNIDDYLLLDELGKKKIILKKPIGEDDVSSHEFIIPWKMAETVAQNKIDSTSDPAAKFFTPDDLDSTTKQPKDQAAVRRILKKWDAYSCKGGASATFGVASLCGINVAAYKADIEKLIKDVYEDPNFSDLKNRTGGPQKDKDTLKGYYERLKPKVETLRPLKAGVSSAVGAAGAISWAIGVADAFTSENVSSFDKAAAVTAIVPGLGECVGIANAIDKRDPEGLIINTISMAALMASAAVPVLAPIGVALDAGLAAAQGVATVLEYLEIGQPARTPLPVSSPKTHKGVTAAWVGSERIIAHRPRPGMRQHIFSVSIDSSKPEYTAPLIEVAGVRADGKLDPSPEWIRIRQNHYPIPFRFEKLSGDSPYAFRCVLLRPTTITRTEPVYVTFAYMTSDMTCRTGESDPNKACSPNNPAIAVRFGSLVKNEDERSVLAVTWPGPSIRPETNWIKLPYSIHPY +A0A2A9P4J3 unreviewed Ophiocordyceps unilateralis (Zombie-ant fungus) (Torrubia unilateralis) A0A2A9P4J3_OPHUN MKALGPCPLLLSIAISLLLFFSIGAANPTPEKTEVAAERPEVVWRGEQEKRGPADIKRDGGFRALGLHRDLIGGMPTEVEREAASSLYIHAAGFDLGATKYISTTTDPMVAASYAHIRGVKPGYVYLIHTDPKFVDVYATLGEAVPFPFDKEHVAVGLIPWHQVVGWFDLSEPHFKRHLLSVHGSLGKLKGRAGEEEEEVEGFYRNEDFDSRYLPKRGSGGQPQLAGFAPSSAYWERPPWNKFVRQSSADNLEQFVVDHVHAPRGEDESVWSWALTRPSEEVTDSVKTVEKAAEEKSSSSNMDNCCRLHRRGLVVVVQKRGCIPCKSTTTAGESRGKMKRVEPMAMAQKSSKEAFDALLHQFGHGNLATHQDQLHAQLDRRLQEMRELSRLDKINHLAHQVGADASRAAGLAIYGYAVADVFSHEAGVLDKAAVLTSVLPVIGCSVQTANHVARGTLDPTRLALCFAQDALVISGLWEFALSWQTITGVWETLTMMDEQDQLYDSDVFKEKRIRGWQSKIQELEKYIDSNDFLQNVTHRFSAFQVGLLFQASQLVGDLHASHHIGLGAAETYATEKLADKAQKMISLAMEPEMERQICIETARNKIRVLESLKGSLLDLTRKLAKQYDDQFFDQYWQAATGEVDFLGFIPIRPPPVNMKELREAMMYQKRNGPLPLYQERIHQALRRAVERIETPASCLCRQEEKQKCEFADCSSPKAAVGKMDAGGRVFFSTIMSMDDDSISSDCKLRFLPCWAGASSSSEHAHRLEALEIGPRQMWCKAAGAALESNSTQGMNLPLAE +A0A2C5ZN79 unreviewed Cordyceps sp. RAO-2017 A0A2C5ZN79_9HYPO MSPEPSMHGVSPQPETEPEQPSTGAKPKQFKPKKEGKKVRTRPKFKPKEGVNVLYRGDSRPASAIEGVGGFYTRPESSFPSSSEATPRDPEKAWSLFQHVTNKKDGLTKFISTSLDPYAAWEFGKPSGNLYLIAPDKQMVSVRDTLDGTLDDRFMEEWEYAAVGQIPWEQVKGHWKKADLRPSDLIALREGKEIDKPFVANPEFNAQKFQSTESSGARPDLAGFPKGDPNWKDGRFSKYEKDKPSANLKKFVLEVVCGKNAQCAENYLAPRCVETDLRKRAEGICSSGEEEEEAEEEAAEEEEGSGGEKEEAAGEDGPGKEAQGGDGSGKEAESGAESGAESGAARGGKAMKSEPASVDVAEKESASEFEQLARNYGLESVAKRKWKRPLSEVRTRNLGYQPLSGSGGKGIGRPTFRPRGRLLSAGGKALGAAGYAFWVEGVVDAFVNDASTSDKVAAVTSIMPLVGCTAQLAANIDNDRLGGFEVLDTALCYIGDGLVIGGLWPLGLIVHLSRFIISLVRVWLEPEEPSEEHLKQIQAARDGPWSRFMEEYMGGYLASDGLGDQLGSAIAIDSLAILSDGAWAIGVLTAAQDKAVHMTDNQTEQAQMREFFPARIAHVRQQLATAIPHRQRAFLLRLVGRLQNDSKLSIKTVADQYNKDFLDGLSKNGTVEVHEKIRQHLAREPLPPLGAVKTAYLIGQAMGPYAKMPDDEVEKLLTLPDEPTSAPDAKAVAQDGAATDGWEINPLALSLPVYLGEKGVSDEDGRAASIRLALAVVRHLKGEAGEDETAGLDAALASDLRLLIAMKLGREFDSGAAEGGQTKTKKTTALAAEAAADPAAAVAKVLGLEKKDVEAVAGNK +A0A2H4S865 unreviewed Cordyceps militaris (Caterpillar fungus) (Clavaria militaris) A0A2H4S865_CORMI SFPASPSKQAMRIFTTRQSRVSWLSLTVLAAFFLFIGLSHGHPSSGNNLRQLSYRGNSDSKPGKPGKTPKPPKGSKPPVGGGEPSAPPPANAPSSSKGKKVGFKKNIDRLPKFELPEEPLVTYRGETRDAAAIEAAEGFFPRAPTRAITAAETEAGSSIYSHVAVKVAKEYTKYVSTSMDPAAASDFGRSTGFVYVVAPDEKMINVPKTLGEYLSVDFAKESELAAAGQIPFDQIRGWIKKSTDLRPGDWRTLREHGHKLNGKPLTEELKKDLGRALSEAYVPNPKYNAAKYDKTRASGGRPELAGFPQDSPAWQKAPWKQYKGKKTSESLQAFVSEGVCAKAEACVKAQLTSKAGEAGVPNPYEDLDPLCKETDLRKRDVACRLEADEEDPAEAGQTEGETVATEPEGIKEPGSAEEGLGGGSTETAVEKVAQKTSDEVFEDLFTSRKLSQKAAEWGLKIQDARTRVLGYKPLTPQSPHFSISKVGLALEGAGLVAWGAGVVTAFATNTTGWERAAAVTGIVPFVGCGFNIIAESQSKAGIDPIDQMMCITADILLLTPLAPFAFAVQVFRVAAGLLKADAVPKPEEAQATRDAAWSKVLDDTVYTYYYSADKLASNRQFRDKLNGTLYAESMAVVSDAADLIAAANATVQASAAKGGDQAQIRAVVLNVTDQIKAQISPKIVAKQREFLLQLPELIKKGTDLSVNKMAEQYNSETVTNLLSPEVVHHYTPLVPAVPGDAASDLEPSVRRELGVVVEHLKKTPLPLPKLFDVAYVLGQSKGLLDIDPLTLNPGAFIKSRVPTMSQDDVDFHALHHALQISSLLRGATTEEKLSKLWPAAPAGGADGAKTVQELQLLLALKFGRVFDAQKVAASAQLAGKEQDDRFMTHPSIPPLKIAPESMAYIGLILELDEARINAIPRHKEIVGFNELGTDKKLILAMLEKAQGIYAARHNETTAAPVKPEGQAAAAA +A0A2H4THL6 unreviewed polysaccharide biosynthetic process [GO:0000271] Escherichia coli O157 A0A2H4THL6_ECOLX ENLFVIEDCAEAFGSKYKGKYVGTFGDISTFSFFGNKTITTGEGGMVVTNDKTLYDRCLHFKGQGLAVHRQ +A0A2K8LST6 unreviewed Amycolatopsis sp. AA4 A0A2K8LST6_9PSEU MKSFRAFSGVLRGSLVTTVLAALVASLTPAVAAGSPAASPVRQAAAAESSGGGLATSGARMAGPASGGVGVRQAAPPQVPEDVEVTLPGTSSRMRGVRIPVPGVEKSGIVVVKDLYGPAIEDAKTPKVVKFEDAPGGAAEAERFVKRMQDAVAEIAKSPDGARLLRGISEMRPLDQVGMDLATGQKDVTVVLHRSAWGERDAKYSNTAFTMAYDDTRAANGVGSPAEIVVPNESYAIFGVNLENSRRYMIREQDALAHEFIHASHYLEGSRYPHSADGSPVPITYTYTTYLGEKKHREVDIPLEEIRTLGGPDALLAAEQQKVHGLQRLVDKAIANSRLVMDTYIKLAPDRATKKLLKKIRDARNAAPDVTETSIAQQLGITPRRHYANISGTADAVTAAANEISQDKGIHWKIVEGEVKIVRAGTTITVEDTRDPAAVEQRLGGQPQALPLCPSSGARAACIPKQEPKDVPEATRSRVNENLQFVKSNPDAVLYRGEPIGHPGLREQGSVAKPDSVYLGVEDERGPAKVFSAGISAPGTEYSTLTDQWLQNSHLDVLKDSGWVTTTESADRAKAEAVPLSRAQFQPGTYRSPPRLVAWGWVYEIEPTEFFVSAQASAARIKNGYPDLPADKKAVYKDFETATPLSAATQATREWSAIRRISPENIIGAKHYRATYQATSDGRVADPNPKVEELPAGDAAANRAYKPAAAGYDPYADRGSGWSDTATVFKPLDRDPWRGAVEAQKILRGIPVTAQTGGTRTGVQPDAVNRRASDIVRDLLQDKQFMAEIPRPVQKPGSKELVWSKENAKAALDKVQGRLKNQFNLTEGPLNKAFLALWAYDMYELAKTHENVDGLEIAARVTALVPGLGDALGLADGINRGDAEAIAVNSVAMAAFMGALVVPALGQLLGGAVLTYTLGKLASEAVRYVVNAVAQWFTPPPEFSPDALRPGEIPTMPAVDVQNDVVPCSTERVITWKPDPRLKKDVDLVIRVTAADGGTYEMGADPQAGRRVFLDSSQTLTADLFYRAKRPWGTFVSHQITRQDITYRIGAAGPFPSPRNRCIADADRRVSWPVQDVGWIAARFPALFETRIPGRVSAAFAYPNSWDQRDGRYTAFGFFKGDTFVSYDDREDRLRDNTPKKIGDAWPGLGGSAYASGVDAAVDVRDDYGGAVLLLFKGNKMGRYKYAGSSSRLEWDDTIANRLHGLPPEFADGVDAVLPDSPRSGGLPGTDRIVLLKGDRYALYDLREQRRIPTGSDLIAARWRVPAGVDLRRGVDSALAISPDQAMLFAGQRAILARDSGEAASVPTGLPVSVQSAFEGRPDRFWSYEDGPGGSPRGRIRTAAVTDASSPDEKKKATFALMPSDIPRCYRLRTSAYPDPPPGGFADWEQRAASADDIKYSEAGRYVCPSRAPGGGFVLHPADLKKKTADNGRVLYAQAGLGQTGPYFANPADLSLVDQREFAARTTWHILAPHWSSEVDLPTENTASLVDVATRQAVLKQRDETNLELAADNGDLAWEPGWRSKFRLYTADADQSCYTFHDPWSRKDLYWDSETNKVSLRDPRTANDRGFAFCTAAAGDDGKGGKAFSLRPLGFDDRVLGWDPAAGAFRMTARAGVQSLRLSLGTPHRGTILPLMDAVDTTQPSSVHRPPAVSFDPVLGLSPLAPGKTGEAKVVFRNTQWYTFDVSGTGAAVTLLAPAHTRFVPQNQLQWEVSADGGKSWNKLGIASADCKALDEGQRLECPKSSAGSIAPGALIRLSPVVSVAADAPADRIPLPGRALIAMRLSLAQRVGTATAGIHVYIQDPNRSVRVRRSVVAASAGEQARLPFVLENAGHHVEADFGPVTFTAPEHSRFENQAEVSGHTVGNGIPDTPFTVSECAVRSLEAGKHDNRILACAHNSLGRTTWRGALGDADGKDKADERIVLGPLLLRVDQDAAQVVLTGGTVAMTLATAVVKRPELDGGAVPLGTYPAGGAALASIVVVGSVGFAGAARVPWSDEGMRPYAMLAPGHEGPVPWMMRNTGYELKSDMTGTVTFRAPAHARFVAPKDGKIPMEYSFGTSPASWRNMEPSGGRDPANTLHDCVATVDGKTMTCQNTGRKQMRWQGGQGAEGGKDKNAAVVRFRPTIRVDDDAPVDARAPGECGSASGDGTLELANATQYTDGGKWVPRGDTGRLSVRSDLALCIVDKTDRMSQPDSPPAKVFPGGETTVTWEIANNGYQLNADYTGDVVFTAPDNMVFLAPPDQKIPLQYSEDGGRTWRTDPRNPALFGCYFANTAKIMTCRNIGDNKAYPYNDNAWAGLWGNDLGGKKFALRFRFQVQARMNDDAPVGTCMNGGTATMNLANARRANGQGGEDKAARWNQLGSIALTGKLAVCTESGFPDAKDRSDTGKPALSGRLSPDGTGPVPWLVSNAGPNVSMDYTGTMVFHAPDHTTFVPQPRDYIRLQYSYDDGRSWVDADGNAGLYRCRTSSDKKTMTCNNTGWKGQQWYGSSGDGNGKRKFQMLRFLPILHADADSPVGNCSSGRQPSWLTLDNVEQDNPQGGKNKYGTQYAFGTLRTCFVNGFEGSKDRAPITSELAQWGTAEWQVSNSGYEVVGDFAGKVVFHFGVPGYAGFVKPQEGKLPLRYSLDDGKTWKSDRALTGCAISNDKLTMTCDNTQNSGTRWYGALGNAAGPDAEKDKREGIYQILLPIWVSPTLRGRCLPDGTGVLSIKNAVQWDGLAARPIPRGEMKVTGKLKVCVAKPEK +A0A2T2XWR4 unreviewed Kluyvera genomosp. 2 A0A2T2XWR4_9ENTR MNQPPVRFTYRLLSYLVSGLLATQPLLPAVAATLTPTGNTATDNAANGVPIVNIATPNEAGISHNQFTDYNVGKEGLILNNAADKLAQTQLGGLINGNANLKAGKEAKGIINEVIGGNRSQLQGYTEVAGKAANVMVANPYGITCNGCGFINTPQATLTTGKPVFDASGNLQTLDVKKGSITIEGQGIDASGSDALSIISRATEVNAAIHAKDLKVIAGANRVGSDGSVQAQQGEGPAPTVAVDTGALGGMYANRIHLVSSEKGVGVNLGDLNARQGDITLDANGKLTVHNSLASGTLSAKGEELALTGAHKANGNLSLSSQGDIALSNGTLASDGELVLNASGTISHVNEKLTVGGGATLTAKNISQDAASEINAARDIAVNAHDTLKTQGQMTAGQNLTVSSNTLTQDGSLLAHNRAQLSADTLNNNGSVQGAALTVDSSALRNAGSLLSGGGLTVNAQDFTQSGSIGAKGYADITTLGTLINTGSVVSDDALVLNAQAVTQNGVLSGGNGLTVNAQTLSSGENSLTHSHAAMTLSATTTTLDGEISADGDLQLQSDRLTTAAGAQLQSGNNLSLSAREVTLAGTQAAQNAMTANTREQLTHTGKSSAAALSLSAPALSNSGVLVASSLNTQSQRLTNSGLMQGDNALLIDAQTLDNQQNGTLYSAANLTLVIPDIRNGGLITSDSALTLRSGSLRYPGKIIAETLDVNATTLSGDGLLQGTGALTLTGDTLSQGGSGRWLTAGDLALNGNTLDTAGTVQGQNLNVQAESWVNRGSALATGRLNATLGESLINDGDLMSQGHFGLTAPALTNHGSILAAGDMSLAGDVFSSDGTLQSDTLSLGHNRIDNSGTMTGLHGLTLNSSGALNNRGDLLSQKSLTLSAGEVNNSGRLQGQTIALDGSGLTNSGAIQSALDLALTLSGDLNAATGSKITALGEARLAGKALSNQGLISASTLAVNGDSLSNDGEISGVNGLNVMLSGTLQQQGKMLAGGALTVNAGDVSNSGQVQGAESQISASSLTNSGRVQGDSGLTLTLLNALTNQAGGVLLSQNALNLTAPELTNDGTIQGNGQTTLSAVTQARNGGNILSGGDLTFTTPDYSGSGWLQATNLMLNVAKLASNGTVMAANQATLTGNSLSNGGTFQAAQLNVNMQTVTNSGTLLGNQGLTLKGNSLNNAGGKVFSGGDMLAEMVSLSGAGQLVALGNLTLKLTNSFTAQGVIASNKQLAVSSQGDMTNAGTLQGDGITLNAVGRLTNSGQLTAGTGTTVLSGSQIAMNSSGSLQTGGDVNLTSRGNITLDGFTGTAGNMVLAATGSLINTALLYAGNNLSLFANSIQNLRGDMLVGNNLVMQRDASGTANAEVVNSSGNIETIHGDITIKTGHLLNERTGLTSTIQTTNLTTTYPWLGQATANIPVAVLGADTVTQKKTSSYSYEGSCGGAYCFAYEDTRYYYAPIAGYATRQFALNQTENQVSAQGGSARIASGRDMTIVAQQLNNQASAILADQNLILTGNTLTNQSYQSGTYTEYAVYQYERVITGGSGLTPGYSPTHRNTTYENTVPYVLKGNVTESINGELFRAVIQAGAGATTNFTDFVSNENITAQGDRVSNTIVAPALNALSQQTVGTPEQGQTLADTATLPIDSLEWRDQISGALLNMSGGQTLDAVDTSAWPLPSGDNGYFVTATDPDSPYLITVNPKLDGLGQLDPSLFGDLYKLLGMTPGAAPRETGSQYTDVSQFLGSSYMLDRLGLDPDNDYRFLGDAVFDTRYVSNYVLNQTGSRYINGLGSDLDQMRYLMDSAAAAQTALGLTFGVALTAEQIATLDHSILWWESTTINGQIVMVPKVYLSPKDVSVQNGSVISGNNVQLASGNVTNGGSSLIAQNGLSIDSRNSINNLNAGLISAGGGLELSALGDINNISSAISGKTVQLESTGGSINNVTRTEQWSVGGNSRHGNVSVSGTDVGQTASITASDGLYMAAANDINITGANVTAGGDLAMGAGNNINIAANQMTDSSSQSGFLGKQDNLSTSTSNLGSSIIADGNAAMQAGNDLNVTASAIDAGQTAQLVAGNDLNLNAAGNGQTSRTGSSENHQSSADRTTISAGDNVTLVAGQDVTSQAAGIAAEGNVGIQAGRDVNLLAEATVNGSSTRERKKTVIDESVRQQGTEIASGGNTTIIAGRDVNSEAAQVAAQGDIGVAADRDVNLTTATESDYHYFEQTKTKKGFLSKKTTHTIEEDRATREAGTLLSGDNVTVQAGNNLQVKGSSVVGDNAVALGAGNNVDIVAATNTDTSWRFKETKKSGLMGTGGIGLTIGSSKSTHDLREKGTTQSQSFSTVGSTGGSVAISAGNQLHVGGADLIAGQDLSLSGNSVIIEPGHDKRTRDETFEQKKSGLTLALSGTVGSAINNAVSNAQETKETSDSRLKALQATKTALSGVQAGQAAAVASATGDPNAMGVSLSLTTQKSKSQQHTESDDVSGSTLNAGNNLSVIATGKNKGDNSGNIVIAGSQLKAGGDTLLSAQNDILLSGAANTQQSSGKNSSSGGGVGVSIGAGKGAGISVFANVNAASGKDKGNGTDWTETTIDSGKTVTLTSGRDTVLNGAQVSGNTLIADIGRDLLLSSQQNSNQYDSKQSSVAAGGSFTFGTMTGSGYISASQDKMKSRFDSVAEQTGLYAGQGGFDITVGNHTQLDGAVIASTASADKNSLDTGTLGFSDIYNEADFKTQHSGISLSGAGDFGGDQFKGNMPGGMISAAGSSGHAEGTTQAAVANGTITIRNTENQQQDVANLSRDTEHANDSISPIFDKEKEQNRLKAIGLISDIGNQAADIARTQGELNAIKAAKDRMSRAEPEELDAAKAQWQKENPDKTPKPEDISNQLYQNYYNEAFAATGMGTGGAVQRGIQAATAALTALAGGGNLAGVLASASAPELARLVKETTEDNTAVNTVAHAILGGAVAALQGNSAVSGAAGAATGELVARAIADIYYPGVKMSDLTESQKQTISSLASVSAGIAGGLAGDSTSAAAAGASAGKNAVENNALSDIAENQHSGMSQSEKYQKAQDALVKATEEFKAKNCAGLSTEACGAKMDAHRDELLAGFAEAGSEYLPVYGDYKSFAEADSALGYLAAVIGIFPGLGDTAGKFIKGAEKALKAGDLETASKLINKASDEIQAVKALDVGSYNNLKKGEVVGDGLEHDHIPSFAALRTAKENELGRKLTPTEEKALYQNATAVEVPKNVHQAGPTYGGKNTVAQVQQDALDLCGAVCRDTNALRTNMIERGYDPVLVDDAIKQIIDRNRQTGVIK +A0A2V4P3B9 unreviewed Vibrio cholerae A0A2V4P3B9_VIBCL MMKLWVINMKSRFVVFGASHSEGVSSKTGAPYLIPVLFVGKPIRQWKNDKGQCLTFGLQHQEVKFVSSDAMTRKLEQTAFPVLVTFDNEPDPEDPS +A0A2Z2GEX0 unreviewed Vibrio cholerae A0A2Z2GEX0_VIBCL MVKIIFVFFIFLSSFSYANDDKLYRADSRPPDEIKQSGGLMPRGQSEYFDRGTQMNINLYDHARGTQTGFVRHDDGYVSTSISLRSAHLVGQTILSGHSTYYIYVIATAPNMFNVNDVLGAYSPHPDEQEVSALGGIPYSQIYGWYRVHFGVLDEQLHRNRGYRDRYYSNLDIAPAADGYGLAGFPPEHRAWREEPWIHHAPPGCGNTPRSSMSNTCDEKTQSLGVKFLDEYQSKVKRQIFSGYQSDIDTHNRIKDEL +A0A367L003 unreviewed Ophiocordyceps polyrhachis-furcata BCC 54312 A0A367L003_9HYPO MARPFKSSPFSGGLCASLLCLCVVHLLGLDKALFLGNNRDPFLKARAVEQNETLEEARTPIRRAVMSPNYIDMDEAGLYRFQQVTQFLEPSDQLYRSSAPFYTGEDTSHQAKEITRDALKQRNIKTVISLNSDAGNPKFRTVFGPDIAYKAVPVQDYTAPTPKELQRITDIFMGNRKRGGVLVWCGYGHGRTGTAISAIQIRVQAELPGLKHAKLARADFEANHVETDKQLQVLEDYQREIPAYRGFNPFKSALVHDNGVTVPLLLHFAESMPTSAVANVDHHGLEAVELHTTTPKVVLRGEELRRPDDILAAGGFRSRGYLALESGHFLTDDQMERSSSLYFHAEEKTLHSRYIATTTDARTALVSSDTLRSLDEVGYIYRIRADEKMIHLSGSIGNYSSRTNRFEHPAVWFIPADQIEGWYNVTTDKVGLHKDDPTRRSFIDRVQAGSAEGFEANPQFKREKYTSQRGFGPLPRLAGFPKRRPHEVGEQGKDGPIARHLSEGEEKAVSAWDEAEWQDFRHESVRDALEGLIEATGGMRSVSLVKRGKPCKTGQGPDGERSSTDGKSRKDLLGFSALAEKTKVSLPEMHRRLRQTTLAIKRVKSPLAANVVTFGPWVAGIVRVFEEESSVLQRAAAITSIVPVAGCFVQYAADSQVGQGDKLDTALCAIGDALMLNPILLPIGVVVHAARIAVQSVPDFWNTLKATYAPSVLYGRRLEGWKSILKEIAKNVTSPEFASDLKTSFHFDVAAVASTLSEAKANLILGRIDASRKASKEEKRKITKAYAKTLKELNRKACGELSKRIERFQKDTRHTMEEILRNSSQIYDDEFFTDFRRRADQNSLLMIMFSWSVNDKLLRLIEKVRKISLHPNGNFLQGLEETLQTEHRQKLNDVVKKSASDTFPHDPAKCVALFEADDEKDVESGLEFIDEPPRCVKKSPRKKGSKRDCIHWREVISTLKKEDVTDCNDEGLWTYRESMTKLINGQLNCGTADDHWSWDQGTRLKHCRIRHGFQYCDFYKHKTMDHKTKMQAYNTGLILEQPPKIVWNGQQ +A0A3B0B7P5 unreviewed Streptomyces klenkii A0A3B0B7P5_9ACTN MRISTQRVFATTAFAGMMAAASAELAHAAPAPAPGGDRITQATQTGLEAFHGYKPGHLDSILEGLRPVGSAGNDDPNWKGLYLAETTGHAAGYSTNEAGTAAGGVVRVTLPDEVNVATVHLSHRADETGEAFLDRQLRFVKDEFGVPVGKPLMDALGEKNTVLKIADDGTGQSEFIVPWKMAERAKAEKAVEFRGKNSAMDAAIYAAAPANCLAPTGRVKRSADRCLPVDWDKVEEKAKATAKKVAQDAEHVEKLPKRSPKGPTWGEAHSTAELSHAKVREVSGAHVAASAAGVGTWVYGMAKTFSDKDATTLDKVAVTGAVVPGLGQALGIADGIQHGDPEAIAVNAVALAALAAAQVVPVVGEVVDAVLLTEQLVEVLVDVFRTATADPPPAPRLDAGALPVLPVPRASLPCSVWWTYMDVLWETAGKVPANEKVPSKTQVVVRERGGSEYTYPVADGKSPGWLVPHGLGSSRTFDVFYRLKTDQGRVLESKQRAVVQAKAEVAQCNTLIWNEDWDR +A0A419HD92 unreviewed Pseudonocardiaceae bacterium YIM PH 21723 A0A419HD92_9PSEU MLAMLAVETSDAIASTPQDRAVSVKITEQSKLDKLVVYKGAKPSEYADLMKAPDVVQGDPPPGEYRGLLATETPAQASGASISMVKYENGVSQDGEDVDGPGQSQIGPDGRPLLPPNHYICEYQPKHCLPARPTVTYLHLAEAGGIVKYTIPGPIDLVTVDYDAGSDPTEVASAVKKKLGIDQDRYLVPALGTDGRFKALRLQAKNTNGSVRQEILVPWAQIEGKGEHKPKIESAYKFDGVANYENGKPGDSPSWTQARMLQRDEECKNSTSQFCRIGKAALAKATAEAQADAQKILKDPSFKPPHPAPHQKLTDAELTQVANDLAKRVSALHPGARDLTGLGKGLAKALKGVISGVTAGIQAAEIAESAAGSNSGDLEKVCSALGPIPIVGDIAGIVNAAAEGDAVEIVKNVLTLGLSMVSVAFPPLAPVADAVAFVLEVAVFLGKYLWGLLFPAPPDPREEAYKANQALHDEAGVRLQWEYAQYGQIKASPRQRQVLQSLVVTAKSDLGFSPQGFTEVGYSDFYLRTVAVRVYQDGIRVADLTCRFMPATGNRYPSDDCESPVPLLATAAHPLRIEFIYDTDSRCGNGTVCKNRDVDMQLTRPGEKDLPLKYAIYG +A0A4U0NVY7 unreviewed Streptomyces piniterrae A0A4U0NVY7_9ACTN MSPARRDIGLTRSLSSVGIRRQLRRCCASSPGLFRSNTRNPFRPRPIATFTSSVHEYLNRRRNMRHARKRISTKKAISAVALAGVVSAAFAQLGHAGTTDTEVAIKREKVTDLTAYHGVKPGDVEKVLKKIQRPDNGTAGNDNADWKAFYLAETPQHAAEYVNGEPDPKTYKVKAGEVVQVKLNGEVELATVSGSADSPEVVQQLKKEFHIPEGRPLMDALGEQNVVLKIKDGTTEKGVERFEIIVPWSIAEKHATATRHAHFPDPRGSDKAEEFSRMSPEDAAKYVGECLSGGSRTKRAAAASVCELAKDKARDILSEAGNEVSLPQRDSDGLSKQEITATAEATRSKLGTGVHGAVSAAMVADWAHDVARTFADPKATKLDKAAAVTAIAPVIGQAVNIADGIQHHDKKTIVVNSLVLAAVVAAQAVPAVGEVVDAAIVADFVVEKLVGWFTPTAKPGPEHVAPPSDLVETPQLDKDPWMSDPADTSYSKGATAAPQPSASSGSAGAGQESKKKDTGKTDDEAAIPDVQETPIPNF +A0A4Y6ECH0 unreviewed negative regulation of translation [GO:0017148] Escherichia phage Lyz12581Vzw A0A4Y6ECH0_9CAUD MSVINHTPPGSYFAVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A5C6GP95 unreviewed Metarhizium rileyi A0A5C6GP95_9HYPO MRSRWTLLVLSWIPLLLWANAAAAGEPFDVFRGDSRPPDVLEAEGGFLPSINPAPTDLYSLAKHLDYYKEPYGRVVTPYISTTKDLTTALQYSRKRAASTTGFTYVYRISTTPHIFDAAQSLSLFRHQEMAKEVSEYDALGGILWQQVRGYYKIPLGASEEAINNILADPEKIFNTNAQYDIRFDKYYASSGQPQLDGSHGENTYENAREFMSRPGVGDVVGWQGDFPLFKDPLFDQRAIKEKVSAEAAKDATTQASAASKEAKKALEAVKKTKDATTALAEADMAVKAAKLSVQSAKKVATIAQEHITIKTDTYNEAKQSAMQAREAAAEAIKEANYKAAQSYVREATNLAKTKTPESARQAIQKVRQIADLKTTIKARIIALSLTMDEVRQALDQERGKWAMVTSDNVPRQIPSEDSILASNREYYDMALNSEFKFLQHEHDKLRVAMEDLLRLEAKAEAATKTSRQILEKEKPATAHGDMDTAGKAVGGVAAGIGAVAGSAGLTKAAGSVGADIADRVPLVGAGAGERTPVAPAEVDSVLADIKNQVAVEAAPAPPSEPPSRSVACATLGKREIFCLDVEDESSLRPGEEGGKGKTKSPVEKAKPIEMTPARQSELMALAETISEKEFNSIAKKNGLQAVVERRWKTTLPEARVKVLGYQKMSLHVSPVTAQRLGVSVKVGTGALAAVGVGLYVKGVVDAFTTETTKWEKTAAVTAIVPFVGCVTNLVAQTEKENPEANAALIGVDTSLCLLGDMLLLGGWTAPLGIIVHLVRYLIQFFEAPPSLPSYKEVTEMRDKPWRSFLDEHLTRNIASKSWRDKLEGALTVHALEIWSQACDRVGILKAGSQLISDAAEGMSQTHHVRRDGDLETNADTDLSQIQAEIEPAIAKIWQEADDSIVSLQRQYLLSLPNSLRDDLEVSITATAKDYNEKFIVQLNSEDTIRKYPGYSKAIAWLYTEKTVYSDSRSQMEKFGNQLRDDLPPLPSYFTLAYFVGVAAGVEDPPPPKIDPHGGPGFATFMPAEPEDWKCVVNSTTIDPARYYREKTGGDNQRVLVQQTVDVVYHLLGKLEESRLSTEIPGLTDAREFQMLLAMYIGNTFADWKEAQGNRVGYIHDDYVHDMPAFINRVYDIPVSVAAEHILNL +A0A655UAG6 unreviewed Vibrio cholerae A0A655UAG6_VIBCL MKVVYMGISHRKGISNKGLGKPYEMHKIHFATPIETIDTPNMSLSGRGLQEQTLDIDPLCLPQFDKVSPLSEVNVSVEPKPSNFTQTWVVGLTQ +A0A6F8PZG8 unreviewed Staphylococcus aureus A0A6F8PZG8_STAAU MPNMKKRLLFVIVITLFIFSSNHTVLSNGDVGPGNLRNFYTKYEYVNLKNVKDKNSPESHRLEYSYKNDTLYAEFDNEYITSDLKGKNVDIFGISYKYGSNSRTIYGGVTKAENNKLDSPRIIPINLIINGKHQTVTTKSVSTDKKMVTAQEIDVKLRKYLQDEFNIYGHNDTGKGKEYGTSSKFYSGFDKGSVVFHMNDGSNFSYDLFYTGYGLPESFLKIYKDNKTVDSTQFHLDVEISKR +A0A7D5V514 unreviewed Metarhizium brunneum A0A7D5V514_9HYPO MRQIWLSITIWTAVLFLLISAEQPLSPEITRGRFKRSIVPSIPRSDSAKHVETSWQLNDRALDDGLPDGASENTAELQKFKIKYEEIPFKLDEAWTKLEHTKGFSLRGEKWFSTGKGIVYPAELMKQNKGLWPRNYDPTTGKPKPFPEAGRGSDEFKSTSLWEHVNGQTKYTTRWMSMTKSVHKSIEFSANWENGKPSKVLTQPEGRDGLDLNVIWKIQDAPNVVDTHGSLANKGANGEFVSNEKFIPSDYLDQVEYSAFEGIPTEQIKGYFRTELILRNPGLAEKIANGDEPEGVFVKNIDYNDKFDSLSHAGVRPDLAGFPPGGINQGTMKNEGWERQPFELEPWNKFDQGPQGEAEGQKIQRRVQSHKDRARDFEDKSEREILASVCSTNLRKRAGSLCNSDTSAPAPGNDRPEPGKPIEEEVKPTDPNGEELPKAIDDAKGVELTRKTSDDVFLDLFSSQKMGEKAAKLGVKAGEARTRLSGYEPITAESPKLKLSGLKLAGEGAGVALWVTGMVQAFTSHTTGLQRAAAVTAIIPFVGCGFSALAEADSQEGVDVVDKMMCIYADILLLTPLAPFSFVIQVFRGIMSLYKPKDLPKAEDVQDNRDKRWARFLDDRVFTYFYSDDSVSKNRVFRDKLNGTLFADHLGVVSDAADFIAISNTSARVALQAQNADKPKIQAGALDVANQITAEISPMIVRRQREFLLKLPQLVLRDTKLSLQPIAEQFNKELIEHMTSESMVRQYTPLVPAIDGQPLGDEENLKDQTRGSLHAIGEHLKKNPPPLPKLFDIAYVLGQSKGMLDINPLSLSPGEFIKSRAPDLSQDDVDFYSLHHALQISLLLRGKLTEDKLSKLWPSDDASTVQQLQLLLAVKFGKIYDEQKVLWVKSQSGVTGNFLGEKEVRWSTHPNIPPHGRDEESMAYLGLILDLSEERIKNIPRHKEVIGFKDLGTDAKLITAMLEHAKELYIKKLDEEAVAMQDDKANDGEKPKSA +A0A7G4Y1Y0 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A7G4Y1Y0_ECOLX ATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSG +A0A7G4Y1Y1 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A7G4Y1Y1_ECOLX TAKTYVDSLNVIRSAIGTPLQTISSGGTSLLMIDSGTGDNLFAVDVRGIDPEEGRFNNLRLIVERNNLYVTGFVNRTNNVFYRFADFSHVT +A0A7L9DEN2 unreviewed negative regulation of translation [GO:0017148] Escherichia coli A0A7L9DEN2_ECOLX SMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +A0A7X3JPW1 unreviewed Vibrio cholerae A0A7X3JPW1_VIBCL QTAFPVLVTFDNEPDPEDPSRNLVIDYQVVCSLFDNVPGGKPLDKPQPIKS +A0A7Y4UPV1 unreviewed Corynebacterium ulcerans A0A7Y4UPV1_CORUL MNRKLFASILIGALLGIGTPLSAHASADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGSEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGNSSSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKAVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNSVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIQTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKIVDHTKVNSKLSLFFEIKS +A0A877P5Q5 unreviewed hemolysis by symbiont of host erythrocytes [GO:0019836] Escherichia coli A0A877P5Q5_ECOLX MKSMFIAVLFALVSVNAMAADCAKGKIEFSKYNEDNTFTVKVSGRVYWTNRWNLQPLLQSA +A0A877P659 unreviewed Escherichia coli A0A877P659_ECOLX TGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +A0A8H4Q2I9 unreviewed Ophiocordyceps camponoti-floridani A0A8H4Q2I9_9HYPO MKPLQLPLFTAVLSLLTQTQAISSPDNPHPPETPSDKPEFVWRGEKEKRSPEDVKRAGGFRAIGLNSMLRGGVPTEMEREMGSSLYAHTAGFDLGATLYVSTTTDPLVAAGFAHIRSDTPGYVYLIHTDSRFIDVEASLKESVLFPYDKEHVAVGLISWDQVVGWFDLKDSEFMSFLISLRNPDDHLAAPEMEGFHRNPDFNGKYLSEMGTGGQPQLAGFSRSAVYWEREPWLSFRGQSAADNLEAYVRKHIHVPVGDEGIWQWALKTLEEDYIPFIDEAEAGGLSAAEERALSCCEIARRSLVDKRDCVPCRSNQDGSNMDENEILSLARENSKDKFTILLHQFGYEAVATHQPQLHDQLTMLFERVRQSPRLNRIKNFSAHYGGQVVKAGGLLVYGTAVADSFTHEAGVLDRAAVVTSILPFIGCTVRVADDASKGRVDVASASFCFVEDGLILAGLWEFGLAWQTLLGVAETLQGVEEQDRLYDPDVFRTKRLQGWESKIREVEAFIGSDEFMSNVTSRYDAHQIGLLFQASQLAGDLHAVHHVIQTPRSYPSEKLAVRAHRMVRIAMDHDMEKQICIEAARNKIKVREALHTSMLNATRKLAMDYDDDFFRRYWLQATSPVPFISLFSISPPATNVEELRNELDFQRRNVPLPSFEHRIVAAVDRVIERLDTPATCLCRQGNSSCF +A0A8J3FUK7 unreviewed Longimycelium tulufanense A0A8J3FUK7_9PSEU MSHRWKAARGAALLLASSVLVGTVSGQALAAPTGPSEACKRDVGCYATEQQGKDVAKESFTQAEHDRIWELQQELEVMRVPDGYVRAFRVEAKEGANQILTYSPDRGVRLSQERDIFLNFGDTYRAASFLMHRNKDERFANTVLRQFVVPKAEYDRIRGNAVAERDRVPDRAYRVDTKNLEQYGLPSQVTERLNNAVANSGNRQFMEFAPKYQGSTFDYGVRLFSQRGEDISHRVQKPLIRPCRSAGCERKTLDKLDRKKIETRAKQVISKLAQDGDLRRELPRRTTTGHAHEEVRGVLAKSRGALHEIHGAATTVAMPLATVAWVEDMARVFREKNATTLDKAATVSEIVPVAGQVLGMADGIAHRDAETVAVNAVVLAAIAVSQAVPVVGELVDLGLTAYAVVDVVVRLFGPAREVPITQESYWPELTYSPSKRSDNPHPPEVWRMTILKARVVKGGDSDGSGQLYGGITLNGKPIWHVSEANPVRVGAGQELPGLAEDRSVTSYFFPGSKYPFQLKARIWDYDPTIFDVDDVVAGSDVEVQEFRGKSFKDGGVGEIGFETPDGEIRITFRLDREYKAA +A0A9P8S3Q1 unreviewed Metarhizium humberi A0A9P8S3Q1_9HYPO MRQIWLSITIWTAVLFLLTSAEQPLSSEINRGRFKRSIVPTIPRSGSAKHVETSWRLNDRALDDGLPDGASENTAELQKFKIKYEEIPFKLDEAWTKLEHTKGFSLRGEKWFSTGKGIVYPAELMKQNKGLWPRNYDPTTGKPKPFPEAARGSDEFKSTSLWEHVNGQTKSTTRWVSMTKSVHKSIEFSANWENGKPSKVLTQPEGRDGLDLNVIWKIQDAPNVVDTHGSLANKGANGEFVSNEKFIPSDYLDQVEYSAFEGIPTEQIKGYFRTELILRNPGLAEKIAKGEEPEGVFVKNIDYNDKFDSLSHAGPRPDLAGFPPVGIDQGTMKKEGWESQPFELEPWNKFDQGPQGEAEVQKIQRRVQSHKDRARDFEDKSEREILASVCSTNLRKRAGSLCNSDTSAPAPGNDRPEQGKPIEEEVKPTNPNEEELPKAIDDAKGVELTRKTSDDVFSDLFSSQKMGEKAAKLGVKAGEARTRLSGYELLTAESPKLKLSGVKLAGEGAGVALWVTGMVQAFTSHTTGLQRAAAVTAIIPFVGCGFSALAEADSEEGVDVIDKMMCIYADILLLTPLAPFSFVIQVFRGIMSLYKPKALPKAEDVQDNRDKRWARFLDDRVFTYFYSDDSVSNNRVFRDKLNGTLFADHLGVVSDAADFIAIANTSARVALQAQNADKPKIQAGALDVANQITAEISPMIVRRQREFLLKLPQLVLRDTKLSLQPIAEQFNKELIEHMTSEAMVTQYTPLVPAIDGQPLGDEENLKDQTRGSLHAIGEHLKSNPPPLPKLFDIAYVLGQSKGMLDINPLSLSPGEFIKSRAPDLSQDDVDFYSLHHALQISLLLRGKLTEDKLSKLWPSDDASTVQQLQLLLALKFGKIYDEQKVLWVKSQSGVTGNFLGEKEVRWSTHPNIPPHGRDEESMAYLGLILDLSEERIKNIPRHKEVIGFKDLGTDAKLITAMLEHAKELYIKKVDEEAVAMQDDKANDGEKDKGA +A0A9W8QGA9 unreviewed Akanthomyces muscarius (Entomopathogenic fungus) (Lecanicillium muscarium) A0A9W8QGA9_AKAMU MRIFTTRQSRITWLSLTVLAAFFLFIGLSHGHPSPGGNLHQLSRRGNSDSKPKTPKPPKGPKGGEPSGPPPATAPSKGKKVGFKKNIDRLPKFELPKEPLVTYRGETRDPGAIEAAEGFFPREPTRALTEAETEAGSSIFSHVAVKVAKEYTKYVSTSMDPGAASDFGKSTGYVYVVAPDQKMVNVPETLGEFLPSEFAKESELAAVGKIPFEQVRGWIKKNTALRPGDWLKLRDLGPKLSGEPLTEELRQQLGKTLADAYTPNTKYDAAKYDKTRASGGRPELAGFPKDSPAWQDERWKPFQNKKVSESLKEFLSETVCAKEEACVAGQLTSKAGEAGVPDPYKDVDPPCKETDLKKRDGEGACVLPDDAPDEEEGTEPSNDTEPEGTENTEPAEEGLGGETPETGLSESEKVDLAQKTSNEVFEDLFTSRKLSEKASEWGLKVQDARTQLVGYKPLTEQSPHFSMSKAGLALEGAALVAWGVGMAQAFTSNSTGWEKAAAVTALIPFVGCGFNAIAESETKGGIDVIDQMLCITADILLFTPLAPFAFAIQVFRVIAGMFKTPSMPKAEEVQETRDKAWSTILDDKVYTYYYSDDSLSRNRKFRDKLNGTMYADTMAIVSDAADLIAAANATMQASAKGPNVDKEQINAVVLNVTDQIKTGISPKIVSRQREFLLQLPQRIKEGTDLSVQKMAEQFNKETMTQLTSNAMVKKYTPFVPVVEGDPGVDLEPQVRGELGALAAHLEKTPLPLPKLFDVAYVLGQSKGMLDVDPQTLNPSAFIKSRAPDMSQDDVDFYALHHALQISMLLRGTTTEEKLSKLWPSDGDDKTVQELQLLLALKFGRTFDSQKMAAAAQLAGQFATPQEIRFMTHPSVPPLKIESESMAYMGLILDLSEERITGLPRFKEVVGFKDLGTDEKLIMAMLEKAKELFVERQENNNSTLPEEGQAAAAA +A0A9X2RR79 unreviewed Streptomyces telluris A0A9X2RR79_9ACTN MNQANRTGQVKPARQRPAFQRAFATTAFAGMMAAASAELAHAAPVPAPGGDRISQAKQTGLEGFHGYKPGHLDSILEGLRPVGTAGNHVPDWKGLYLAETTGHAAGYSTNEEGTAAGGVVRVTLPDEVNVATVHLAHQADETDDAFLKRQLDFVKNEFGVPDGKPLMDALGEKNTVLKIADDGTGRSELIVPWKMAERGKAEKAVEFRGKNSALDAAIYAAAPANCLAPTGRAKRSADRCLPVDWDKVEEKAKATAKKVAQDAEHVERLPKRNPKGPTWGEAHSTAELTHAKVRAVSGAHVAATAAGVGTWVYGMAKTFSDKDATTLDKVAVTGAVVPGLGQALGIADGIQHGDPEAVAVNAVALAALAAAQVVPVVGEVVDAALLTEQLVEVLVDVFRTATADPPPAPRLDAGALPILPVPRADLPCSVWWTYMDVLWETAGKVPPDERVPAKTQVVVRERGGSEYTYPVADGKSPGWLVPHGLGSSRTFDVFYRLKTDQGRVLESKQRAVVQAKAEVAQCNTLVWNEDWQR +A0A9X7JSF9 unreviewed Streptosporangium nondiastaticum A0A9X7JSF9_9ACTN MRSSMRISTQRVFATTAFAGMMAAASAELAHAAPTPAPGGDRITQAAQTGLEAFHGYKPGHLDSILEGLRPVGSAGNHDPNWKGLYLAETTGHAAGYSTNEAGTAAGGVVRVTLPDEVNVATVHLAHRADETDDAFLDRQLKFVKDEFGVPAGKPLMDALGEKNTVLKIADDGTGQSEFIVPWKMAERAKAEKAVEFRGKNSAMDAAIYAAAPANCLAPTGRVKRSADRCLPVDWDKVEEKAKATAKKVAQDAEHVERLPKRSPKGPTWGEAHSTAELTHAKVREVSGAHVAASAAGVGTWVYGMAKTFSDKNATTLDKVAVTGAVVPGLGQALGIADGIQHGDPEAIAVNAVALAALAAAQVVPVVGEVVDAVLLTEQLVEVLVDVFRTATADPPPAPRLDAGALPILPVPRASLPCSVWWTYMDVLWETAGKVPANEKVPSKTQVVVRERGGSEYTYPVADGKSPGWLVPKGYGSARTFDVFYRLKTDQGRVLESKQRAVVQAKAEIAQCNTLIWNEDWDR +A0JC53 unreviewed negative regulation of translation [GO:0017148] uncultured bacterium A0JC53_9BACT DSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNAMTRDASRAVLRFVTVTA +A0JC54 unreviewed negative regulation of translation [GO:0017148] uncultured bacterium A0JC54_9BACT DSSYTTLQRVAGISRTGMQINRHSLTTSYLDLMSHSGTSLTQSVARAMLRFVTVTA +A1Z1P1 unreviewed Vibrio cholerae A1Z1P1_VIBCL QTGFVRHDDGYVSTSISLRSAHLVGQTILSGHSTYYIYVIATAPNMFNVNDVLGAYSPHPDEQEVSALGGIPYSQIYGWYRVHFGVLDEQLHRNRGYRDRYYSNLDIAPAADGYGLAGFPPEHRAWREEPWIHHAPPGCGNAPRSSMSNTCDEKTQSLGVKFLDEYQSKVKRQIFSGYQSDIDTHNRIKDEL +A4F270 unreviewed Campylobacter jejuni A4F270_CAMJU MQKIIVFILCCFMTFFLYACSSKFENVNPLGRSFGEFEDTDPLKLGLEPTFPTNQEIPSLISGADLVPITPITPPLTRTSNSANNNAANGINPRFKDEAFNDVLIFENRPAVSDFLTILGPSGAALTVWALAQGNWIWGYTLIDSKGFGDARVWQLLLYPNDFAMIKNAKTNTCLNAYGNGIVHYPCDASNHAQMWKLIPMSNTAVQIKNLGNGKCIQAPITNLYGDFHKVFKIFTVECAKKDNFDQQWFLTTPPFTAKPLYRQGEVR +A7UH54 unreviewed Corynephage beta A7UH54_CORBE DPVVSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDTIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKKLGLSLTGPLMEQVGTEKFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNSVFAGANYAAWAVNVGPVIESETADNLEKTTGALSILPGIGSVMGIADGAVHPNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +A7UQX2 unreviewed Escherichia coli O157:H7 A7UQX2_ECO57 MNMMAVPFHGNSLYVVNHNGEPYVPMKPVVAGMGLAWQSQLAKLRQRFASTITEIVMVAEDGKQRNMVSMPLRKLAGWLQTINPNKVKPEIRDKVIRYQEECDDVLYEYWTKGFVVNPRKMSVMEELNQACADMKRDKNIASVFATGLNEWKQVKAAHVSKIRTLVNEANMLIDFVLADTGKGKITKAD +A7UQX3 unreviewed hemolysis by symbiont of host erythrocytes [GO:0019836] Escherichia coli O157:H7 A7UQX3_ECO57 MKKMFMAVLFALASVNAMAADCAKGKIEFSKYNEDDTFTVKVDGKEYWTSRWNLQPLLQSAQLTGMTVTIKSSTCESGSGFAEVQFNND +A7UQX5 unreviewed Escherichia coli O157:H7 A7UQX5_ECO57 MTPQQENALRSIARQANSEIKKARQQFPDKNVDDICRSVLKKHRETVTLMGFTPTHLSLAIGMLNGVFKER +A7UQY3 unreviewed symbiont-mediated killing of host cell [GO:0001907] Escherichia coli O157:H7 A7UQY3_ECO57 MYQMEKITTGVSYTTSAVGTGYWFLQLLDRVSPSQWAAIGVLGSLLFGLLTYLTNLYFKIKEDRRKAARGE +A8B1I4 unreviewed Escherichia coli O157:H7 A8B1I4_ECO57 MGWLMISDKLITLVKSLCVLVGISFLVMLVAIFFSTAWRVLTLSGLVG +A8B1I9 unreviewed Escherichia coli O157:H7 A8B1I9_ECO57 MATPNPLEPVKGAGTTLWVYNGKGDAYANPLSDDDWQRLAKVKDLTPGEMTAEPYDDNYLDDEDADWTATGQGQKSAGDTSFTLAWKPGEEGQKGLIGWFESGDVRAYKIRFPNGTVDVFRGWVSSIGKAVTAKEVITRTVKVTNVGKPSVAEERSEITPATAIKVTPTSGTVAKGKTTTLTVSFEPESATDKTFRAVSADPSKATISVKDMTITVNGVATGKVQIPVVSGNGQFAAVAEVTVTEAGAAG +A8B1J2 unreviewed symbiont entry into host cell [GO:0046718] Escherichia coli O157:H7 A8B1J2_ECO57 MQDIHGESLIESVKSEQSPRVVLWEIDLTVQGGERYFFCNELNEKGEAVTWQGREYQAYPIDGSGFEMNGKGSSARPSLTVSNLFGLVTGMAEDLQSLVGATVVRRRVYARFLDAVNFVAGNPEADPEQELSDRWVVEQMSELTAMTASFVLATPTETDGALFPGRIMLANTCMWDYRGDECGYNGPAVADEFDNPTTDIRKDRCSKCMRGCEMRGMVANFGGFLSINKLSQ +A8B1J3 unreviewed Escherichia coli O157:H7 A8B1J3_ECO57 MKTFRWKVKPDMEVNSQPSVREVRFGDGYSQRMAAGLNADLKTYRVTLSVTREEARHLEAFLAEHGGWKAFLWKPPYAYRQIKVTCAGWSARVGMLRVEFSAEFKQVVN +A9LN29 unreviewed Streptococcus pyogenes A9LN29_STRPY GKNSHDQDSSLFGIYSIVKNFQKIGPFDEGDLVTHENVKSVDQLLSQDPIYNVSGSKYDKLKTERKNLEMATFFKDKNVDIYDVEYYHLCYLCENAERSACIYGGVTNHEGNHLVIPKKIVVKVSIDGIQSLSFDTETNKKMVTAQELDYKVRKYLTDNKQLYTNGPSKYETGYIKFIP +B0I1U1 unreviewed Corynebacterium ulcerans B0I1U1_CORUL MNRKLFASILIGVLLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQSCAGSRVRRSVGSSLSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGVNSVFAGANYASWAVNVAQVVDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDLGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFVHGGYAASWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDDATIFCRPKTPVYVGNGVHANLHVAFHRSSSEKIHSDETPLSSIDVLGYQKTVDHTKVNSKLSLFAEVKS +B0I550 unreviewed Affertcholeramvirus CTXphi B0I550_9VIRU QTGFVRHDDGYVSTSISLRSAHLVGQTILSGHSTYYIYVIATAPNMFNVNDVLGAYSPHPDEQEVSALGGIPYSQIYGWYRVHFGVLDEQLHRNRGYRDRYYSNLDIAPAADGYGLAGFPPEHRAWREEPWIHHAPPGCGNAPRSSMSNTCDEKTQSLGVKFLDEYQSKVKRQIFSGYQSDIDTHNRIKDEL +B1B6U4 unreviewed Affertcholeramvirus CTXphi B1B6U4_9VIRU MKLWVINMKSRFVVFGASHSEGVSSKTGAPYLIPVLFVGKPIRQWKNDKGQCLTFGLQHQEVKFVSSDAMTRKLEQTAFPVLVTFDNEPDPEDPSRNLVIDYQVVCSLFDNVPGGKPLDKPQPIKS +B5LMU4 unreviewed Vibrio cholerae B5LMU4_VIBCL MKIKERLANQRKAINKTQAQMADEIGISLTSYKKYESGEGLPTMENLVKIADALEISIDELCGRWATDENQELMLRLKKIQQLDEEEQKAISMVLESMLIRHSTKSILNHGA +B5LMU6 unreviewed Vibrio cholerae B5LMU6_VIBCL MMKLWVINMKSRFVVFGASHSEGVSKTGAPYLIPVLFVGKPIRQWKNDKGQCLTFGLQHQEVKFVSSDAMTRKLEQTAFPVLVTFDNEPDPEDPSRNLVIDYQVVCSLFDNVPGGKPLDKPNKI +B9V3G5 unreviewed Affertcholeramvirus CTXphi B9V3G5_9VIRU MMKLWVINMKSRFVVFGASHSEGVSKTGAPYLIPVLFVGKPIRQWKNDKGQCLTFGLQHQEVKFVSSDAMTRKLEQTAFPVLVTFDNEPDPEDPSRNLVIDYQVVCSLFDNVPGGKPLDKPNKI +B9V3G6 unreviewed Affertcholeramvirus CTXphi B9V3G6_9VIRU MSLKPYTLMDVYDSLEDLNNMALYLRSGAYTDEIAHQVQNLICDKIIDLQGIVNFIRLSPSLNPQLKALTEPSL +C5IC92 unreviewed Corynebacterium ulcerans C5IC92_CORUL MNRKLFASILIGAILGIGAPPLAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGAQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKELSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGSSLSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGVNSVFAGANYASWAVNVAQVVDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDLGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFVHGGYAASWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDDATTFCRPKTPVYVGNGVHANLHVAFHRSSSEKIHSDETPLSSIDVLGYQKTVDHTKVNSKLSLFAEVKS +C9E7D1 unreviewed Affertcholeramvirus CTXphi C9E7D1_9VIRU MSIFIHHGAPGSYKTSGALWLRLLPAIKSGRHIITNVRGLNLERMAKYLKMDVSDISIEFIDTDHPDGRLTMARFWHWARKDAFLFIDECGRIWPPRLTVTNLKALDTPPDLVAEDRPESFEVAFDMHRHHGWDICLTTPNIAKVHNMIREAAEIGYRHFNRATVGLGAKFTLTTHDAANSGQMDSHALTRQVKKIPSPIFKMYASTTTGKARDTMAGTALWKDRKILFLFGMVFLMFSYSFYGLHDNPIFTGGNDATIESEQSEPQSKATVGNAVGSKAVAPASFGFCIGRLCVQDGFVTVGDERYRLVDNLDIPYRGLWATGHHIYKDTLTVFFETESGSVPTELFASSYRYKVLPLPDFNHFVVFDTFAAQALWVEVKRGLPIKTENDKKGLNSIF +C9EBS1 unreviewed Affertcholeramvirus CTXphi C9EBS1_9VIRU MRYFLLFLTLLFLSPSVTASAINCDPNTTTSHQLLFGFGSPIVQSVLFDGCMLDIEKDDYGFVWSCLSNENGDYCKGLYKPRFSQGVSPNWPMCDLSGASAERCIYPYCPEGEECVPLPPSPPSDSPVDGLSSSFKSAFNQVYKNQSEMASTLNHVSGQVSHSQDMVQLNTKFHADRVLESVTAVNNRLGGQMEYLEEIRIDVWDTQREVRKAKDELYSRVAAVSYDVLYSELNVLRAIDELKDSLGGTVVPPNPDQPNPTPPDSSSPNYTGALNTISKKLNTLETISQQLDTMNTALSGRCSNPERCQFPIREAETELETAQQNLKQMINEKITQSALHQFKGSAAVPSFCSYVEAFGYNLCFDFSLFSENLHIIRMIVLAMAYILAAMLILFR +C9EC27 unreviewed Affertcholeramvirus CTXphi C9EC27_9VIRU MKLWVINMKSRFVVFGASHSEGVSKTGAPYLIPVLFVGKPIRQWKNDKGQCLTFGLQHQEVKFVSSDAMTRKLEQTAFPVLVTFDNEPDPEDPSRNLVIDYQVVCSLFDNVPGGKPLDKPNKI +C9EH18 unreviewed Affertcholeramvirus CTXphi C9EH18_9VIRU MSIFIHHGAPGSYKTSGALWLRLLPAIKSGRHIITNVRGLNLERMAKYLKMDVSDISIEFIDTDHPDGRLTMARFWHWARKDAFLFIDECGRIWPPRLTATNLKALDTPPDLVAEDRPESFEVAFDMHRHHGWDICLTTPNIAKVHNMIREAAEIGYRHFNRATVGLGAKFTLTTHDAANSGQMDSHALTRQVKKIPSPIFKMYASTTTGKARDTMAGTALWKDRKILFLFGMVFLMFSYSFYGLHDNPIFTGGNDATIESEQSEPQSKATVGNAVGSKAVAPASFGFCIGRLCVQDGFVTVGDERYRLVDNLDIPYRGLWATGHHIYKDTLTVFFETESGSVPTELFASSYRYKVLPLPDFNHFVVFDTFTAQALWVEVKRGLPIKTENDKKGLNSIF +D0EZH7 unreviewed Streptococcus dysgalactiae subsp. equisimilis (Streptococcus equisimilis) D0EZH7_STREQ VNFSTTHTLNIDTQKYRGKDYYISSEMSYEASQKFKRDDHVDVFGLFYILNSHTGEYIYGGITPAQNNKVNHKLLGNLFISGESQ +D1GID0 unreviewed modulation of host virulence by virus [GO:0098676] Affertcholeramvirus CTXphi D1GID0_9VIRU MIKLKFGVFFTVLLSSAYAHGTPQNITALCAEYHNTQIHTLNDKIFSYTESLAGKREMAIITFKNGATFQVEVPGSQHIDSQKKAIERMKDTLRIAYLTEAKVEKLCVWNNKTPHAIAAISMAN +D1GID8 unreviewed modulation of host virulence by virus [GO:0098676] Affertcholeramvirus CTXphi D1GID8_9VIRU MIKLKFGVFFTVLLSSAYAHGTPQNITDLCAEYHNTQIYTLNDKIFSYTESLAGKREMAIITFKNGATFQVEVPGSQHIDSQKKAIERMKDTLRIAYLTEAKVEKLCVWNNKTPHAIAAISMAN +D6R7L2 unreviewed Vibrio cholerae O1 D6R7L2_VIBCL MIKLKFGVFFTVLLSSAYAHGTPQNITDLCAEYHNTQIHTLNDKIFSYTESLAGKREMAIITFKNGATFQVEVPGSQHIDSQKKAIERMKDTLRIAYLTEAKVEKLCVWNNKTPHAIAAISMAN +E2DHC6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli E2DHC6_ECOLX MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQTYVSSLNSIRTEISTPLEHISQGATSVSVINHTPPGSYISVDIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSMTTDSSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRLALSETAPVYTMTPEDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRYVNEEMQPKCQISGDRPVIKINNTLWESNTAAAFLNRKSQSLYTTGE +E3PYL7 unreviewed Escherichia coli E3PYL7_ECOLX MQSNNVAAGAAGAATGELAARAIGMLYPGVKLSDLSEEQKQTISTLATVSAGLTGGLTGNSTASAAVGAHRAGRMLLRITT +E3UUX3 unreviewed Vibrio cholerae serotype O1 biovar El Tor E3UUX3_VIBCE MIKLKFGVFFTVLLSSAYAHGTPQNITDLCAEYHNTQIHTLNDKIFSYTESLAGKREMAIITFKNGATFQVEVPGSQHIDSQKKAIERMKDTLRIAYLTEAKVEKLCVWNNKTPHAIAAISMAN +E9DRF6 unreviewed Metarhizium acridum (strain CQMa 102) E9DRF6_METAQ MHIRWAVVITALRLLQLSGHALATPSPPGPPVPDRPPAPPPRPPVPGRPPLPPPRPKGPPALPRPQPPAEPPKPPRVKFDTDPNPEFVFRGESVSWDEAKKRGGFFPRTSYESPGAYELWPVQLQRERGATIDTAYVSTSYYFEVARSYPSRNNRFVYRIRGTPNFVDMPRSLGQHIVVDEVEFAALGGISWKQVHGWIDSADIRDDTIVDELSNTRFDRLQKEGKYVLNPDYDPAFDAFRAGGGAPQLVGFPEGHPARNQEPWKAFQDKSAKEYAIEFMDENGKGLGWRGQFPLLKPAEGTSEIDKPTGEKPGKPSDETLGLPPCPRTKRFLSQRQSNCIPQPHEASHNQDKAPTAVETAGDGELMAMANQRSKESFDSLLLEFGYGSVVKQGQLYNELNARLPEFSTPRPEKIAGLTTKIGEGALAVAGLALYGKAVADVFSSDASVLDKAAVVTSILPGIGCAVQLADDAERGHGHVDVAQTALCSTEDALSVSGFWEIALAMQVGQELSNWIKAENERDKLWDMELLAKKTAEGWLDNAKRLINHLRSDEFIANATTKLSTYQILTLFQASQLTGDLHAAHKVLSENATMPNAQQGQSVNDDISALVQPELKRQICSTMAESKYQLHMKLEAVALNHMAKLEREFRNHFLDEWLKAATTPAPILGITLPDWSSNTKLIHEEIDRARNTPLPLYEDEVKRAIKEAIERIKTPAPCQCVQGKKGGKCEFGDCQSPSPQTRMDAGGRIYATRVRSIEMAKRMRLTEGCQALFTTCQVPGSTSEIGRPLWCTPGK +E9F3K2 unreviewed Metarhizium robertsii (strain ARSEF 23 / ATCC MYA-3075) (Metarhizium anisopliae (strain ARSEF 23)) E9F3K2_METRA MRQIWLSITIWTAVLFLLTSAEQPLSSEINRGRFKRSIVPTIPRSGSAKHVETSWRLNDRALDDGLPDGASENTAELQKFKIKYEEIPFKLDEAWTKLEHTKGFSLRGEKWFSTGKGIVYPAELMKQNKGLWPRNYDPTTGKPKPFPEAARGSDEFKSTSLWEHVNGQTKSTTRWMSMTKSVHKSIEFSANWENGKPSKVLTQPEGRDGLDLNVIWKIQDAPNVVDTHGSLANKGANGEFVSNEKFIPSDYLDQVEYSAFEGIPTEQIKGYFRTELILRNPGLAEKIANGEEPEGVFVKNIDYNDKFDSLSHAGARPDLAGFPPVGIDQGTMKKEGWERQPFELEPWNKFDQGPQGETEAQKTQRRVQSHKDRARDFEDKSEREILASVCSTNLRKRAGSLCNSDTSAPAPGNDRPEPGKPIEEEVKPTDPNGEELPKAIDDAKGVELTRKTSDDVFSDLFSSQKMGEKAAKLGVKAGEARTRLSGYELITAESPKLKLSGVKLAGEGAGVALWVTGMVQAFTSHTTGLQRAAAVTAIIPFVGCGFSALAEADSEEGVDVIDKMMCIYADILLLTPLAPFSFVIQVFRGIMSLYKPKALPKAEDVQDNRDKRWARFLDDRVFTYFYSDDSVSNNRVFRDKLNGTLFADHLGVVSDAADFIAIANTSARVALQAQNADKPKIQAGALDVANQITAEISPMIVRRQREFLLKLPQLVLRDTKLSLQPIAEQFNKELIEHMTSEAMVRQYTPLVPAIDGQPLGDEENLKDQTRGSLHAIGEHLKKNPPPLPKLFDIAYVLGQSKGMLDINPLSLSPGEFIKSRAPDLSQDDVDFYSLHHALQISLLLRGKLTEDKLSKLWPSDDASTVQQLQLLLALKFGKIYDEQKVLWVKSQSGVTGNFLGEKEVRWSTHPNIPPHGRDEESMAYLGLILDLSEERIKSIPRHKEVIGFKDLGTDAKLITAMLEHAKELYIKKVDEEAVAMQDDKTNDGEKDKGA +E9NQD7 unreviewed Streptococcus dysgalactiae subsp. dysgalactiae E9NQD7_STRDY CRVNFSTTHTLNIDTQKYRGKDYYINSEMSYEASQKFKRDDHVDVFGLFYILNSHTGEYIYGGITPAQNNKVNHKLLGNLFISGESQQNLNNKIILEKDIVTFQEIDFKIRKYLMDNYKIYDATSPYVSGRIEIGT +F1ALY2 unreviewed Corynebacterium silvaticum F1ALY2_9CORY VVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGNSSSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKAVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNSVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIQTGFQGESGHDIKITAEN +F1ALY3 unreviewed Corynebacterium ulcerans F1ALY3_CORUL VVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGNSSSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKAVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNSVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIQTGFQGESGHDIKITAEN +F1ALY4 unreviewed Corynebacterium ulcerans F1ALY4_CORUL VVLSLPFAEGSSSVEYINNWEQAKELSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGSSLSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGVNSVFAGANYASWAVNVAQVVDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDLGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFVHGGYAASWNTVEDSIIKTGFQGESGHDIKITAEN +F1ALY5 unreviewed Corynebacterium ulcerans F1ALY5_CORUL VVLSLPFAEGSSSVEYINNWEQAKELSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGSSLSCINLDWDAIRDKTKTKIESLKEHGSIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGVNSVFAGANYASWAVNVAQVVDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDLGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFVHGGYAASWNTVEDSIIKTGFQGESGHDIKITAEN +F1T1U0 unreviewed Corynebacterium ulcerans F1T1U0_CORUL MNRKLFASILIGAILGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGAQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKELSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGSSLSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGVNSVFAGANYASWAVNVAQVVDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDLGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFVHGGYAASWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDDATTFCRPKTPVYVGNGVHANLHVAFHRSSSEKIHSDETPLSSIDVLGYQKTVDHTKVNSKLSLFAEVKS +G1BEM3 unreviewed Escherichia phage fi125 G1BEM3_9VIRU MANKYTPIFIAGILLPILLNGCSSGKNKAHLDPKVFPPQVEGGPTIPSPDEPGLPLPGAGPALPTNAPIPIPVPGTAPAVSLMNMDGSVLTMWSRGAGSSLWAYYISDSNSFGELRNWQIMPGTRPNTIQFRNVDVGTCMTSFPGFKGGVQLSTAPCQFGPDRFDFQPMVTRNGNYQLKSLSTGLCIRANFLERTPSSPYATTLTMERCPSSGERNFEFMWSISEPLRPALATIVKPEIRPFPPLPIEPDRHSAGGEQ +G1BEM5 unreviewed Escherichia phage fi125 G1BEM5_9VIRU MKRLIIIVTMLLISGCSSSQEAVNNQIDELGKENNSLFTFRNLQSGLMLHNRLDLHGREITGWEVVPVKTPAEALVTDQSGWIMIRTPNTDQCLGTPDGKNLLKMTCNPTDKKTLFSLIPSTTGAVQIKSVLSGLCFLDSKNSGLSFETGKCIADFKKPFEVVPQSHLWMLNPLNTESPII +G3JHE5 unreviewed Cordyceps militaris (strain CM01) (Caterpillar fungus) G3JHE5_CORMM MPPLHLGIPERASLIRGREGSSGFVGEEELVMITSELVKVGLGWNPGKTGQGKEKLDATSTISAARDRASQVGRRRPASRLVFRFGPHMKVCNLHRLFMEAKVARAKWPSSFAYNAAETTDLNNKRALSAVGSIPVLMPESSAAIKAPIVDMGDGSRELVFSGSFSEPQGRAYDELLFSASSSGSIRLASILGLTFKHKLLSRPRLQAWASILGLTFRRKLLFGITFKHKLLSRPRLQAHGHPSSGNNLRQLSYRGNSDSKPGKPGKTPKPPKGSKPPGKKVGYKKNIDRLPKFELPEEPLVTYRGETRDAAAIEAAEGFFPRAPTRAITAAETEAGSSIYSHVAVKVAKEYTKYVSTSMDPAAASDFGRSTGFVYVVAPDEKMINVPKTLGEYLSVDFAKESELAAAGQIPFDQIRGWIKKSTDLRPGDWLTLREHGHKLNGKPLTEELKKDLGRAMSEAYVPNPKYNAAKYDKTRASGGRPELAGFPQDSPAWQKAPWKQYKGKKTSESLQAFVSEGVCAKAEACVKAQLTSKAGEAGVPNAYEDLDPLCKETDLRKRDVACRLEADEEDPAEAGQIEGETVATEPEGIKEPGSAEEGLGGGSTETAVEKVAQKTSDEVFEDLFTSRKLSQKAAEWGLKIQDARTRVLGYKPLTPQSPHFSISKVGLALEGAGLVAWGAGVVTAFATNTTGWERAAAVTGIVPFVGCGFNIIAESQSKAGIDPIDQMMCITADILLLTPLAPFAFAVQVFRVAAGLLKADAVPKPEEAQATRDAAWSKVLDDTVYTYYYSADKLASNRKFRDKLNGTLYAESMAVVSDAADLIAAANATVQASAAKGGDQAQIRAVMLNVTDQIKAQISPKIVAKQREFLLQLPELIKKGTDLSVNKMAEQYNSETVTNLLSPEVVHHYTPLVPAVPGDAASDLEPSVRRELGVVVEHLKKTPLPLPKLFDVAYVLGQSKGLLDIDPLTLNPGAFIKSRVPTMSQDDVDFHALHHALQISSLLRGATTEEKLSKLWPAAAAGGADGAKTVQELQLLLALKFGRVFDAQKVAASAQLAGKEQDDRFMTHPSIPPLKIAPESMAYIGLILELDEARINAIPRHKEIVGFNELGTDKKLILAMLEKAQGIYAARHNETTAAPVKPEGQAAAAAPVKPEGQAAAAA +G5EKG4 unreviewed Corynebacterium diphtheriae G5EKG4_CORDP MSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +G9IBF4 unreviewed proteolysis [GO:0006508] Escherichia coli G9IBF4_ECOLX MLKNLRLHILFLLFISVSVRAEKPWYFDAIGLTETTMSLTDKNTPVVVSVVDSGVAFVGGLSDSEFAKFSFTQDGSPFPVKEPEALYIHGTAMASLIASRHEVYGVYPHALISSRRVIPDGVQDSWIRATESIMSNVFLAPGEEKIINISGGQKGISSASVWSELLSRMGRNNERLIVAAVGNDGADIRKLSAQQRIWPAAYHPVSSVNKKQDPVIRVAALAQYRKGETPVLHGGGVTGSRFGNGWVDIAAPGQNITFLKPDGKTGIGSGTSEATAIVSGVLAAMVSCNPRATATELKRTLLESADKYPSLADKVTEGRVLNAEKAISMFCKKNYIPVRQGRMSEEL +G9IBF5 unreviewed proteolysis [GO:0006508] Escherichia coli G9IBF5_ECOLX LWPHILFLLFISVSVRAEKPWYFDAIGLTETTMSLTDKNTPVVVSVVDSGVAFVGGLSDSEFAKFSFTQDGSPFPVKESEALYIHGTAMASLIASRHGVYGVYPHALISSRRVIPDGVQDSWIRATESIMSNVFLAPGEEKIINISGGQKGISSASVWTELLSRMGRNNERLIVAAVGNDGADIRKLSAQQRIWPAAYHPVSSMNKKRDPVIRVAALAQYRKGETPVLHGGGITGSRFGNGWVDIAAPGQNITFLRPDGKTGTGSGTSEATAIVSGVLAAMVSCNPRATATELKRTLLESADKYPSLADKVTEGRVLNAEKAISMFCKKNYIPVRQGRMSEEL +G9IBF6 unreviewed proteolysis [GO:0006508] Escherichia coli G9IBF6_ECOLX RLHILFLLFISVSVRAEKPWYFDAIGLTETTMSLTDKNTPVVVSVVDSGVAFVGGLSDSEFAKFSFTQDGSPFPVKEPEALYIHGTAMASLIASRHGVYGVYPHALISSRRVIPDGVQDSWLRATESIMSNVFLAPGEEKIINISGGQKGISSASVWSELLSRMGRNNERLIVAAVGNDGADIRKLSAQQRIWPAAYHPVSSVNKKQDPVIRVAALAQYRKGETPVLHGGGVTGSRFGNGWVDIAAPGQNITFLRPDGKTGIGSGTSEATAIVSGVLAAMVSCNPRATATELKRTLLESADKYPSLADKVTEGRVLNAEKAISMFCKKNYIPVRQGRMSEEL +G9IBF7 unreviewed proteolysis [GO:0006508] Escherichia coli G9IBF7_ECOLX FISVSVRAEKPWYFDAIGLTETTMSLTDKNTPVVVSVVDSGVAFVGGLSDSEFAKFSFTQDGSPFPVKEPEALYIHGTAMASLIASRHGVYGVYPHALISSRRVIPDGVQDSWLRATESIMSNVFLAPGEEKIINISGGQKGISSASVWSELLSRMGRNNERLIVAAVGNDGADIRKLSAQQRIWPAAYHPVSSVNKKQDPVIRVAALAQYRKGETPVLHGGGVTGSRFGNGWVDIAAPGQNITFLRPDGKTGIGSGTSEATAIVSGVLAAMVSCNPRATATELKRTLLESADKYPSLADKVTEGRVLNAEKAISMFCKKNYIPVRQGRMSEEL +G9IBF8 unreviewed proteolysis [GO:0006508] Escherichia coli G9IBF8_ECOLX LRLHILFLLFISVSVRAEKPWYFDAIGLTETTMSLTDKNTPVVVSVVDSGVAFVGGLSDSEFAKFSFTQDGSPFPVKEPEALYIHGTAMASLIASRHGVYGVYPHALISSRRVIPDGVQDSWLRATESIMSNVFLAPGEEKIINISGGQKGISSASVWSELLSRMGRNNERLIVAAVGNDGADIRKLSAQQRIWPAAYHPVSSVNKKQDPVIRVAALAQYRKGETPVLHGGGVTGSRFGNGWVDIAAPGQNITFLRPDGKTGIGSGTSEATAIVSGVLAAMVSCNPRATATELKRTLLESADKYPSLADKVTEGRVLNAEKAISMFCKKNYIPVRQGRMSEEL +G9IBF9 unreviewed proteolysis [GO:0006508] Escherichia coli G9IBF9_ECOLX RLHILFLLFISVSVRAEKPWYFDAIGLTETTMSLTDKNTPVVVSVVDSGVAFVGGLSDSEFAKFSFTQDGSPFPVKEPEALYIHGTAMASLIASRHGVYGVYPHALISSRRVIPDGVQDSWLRATESIMSNVFLAPGEEKIINISGGQKGISSASVWSELLSRMGRNNERLIVAAVGNDGADIRKLSAQQRIWPAAYHPVSSVNKKQDPVIRVAALAQYRKGETPVLHGGGVTGSRFGNGWVDIAAPGQNITFLRPDGKTGIGSGTSEATAIVSGVLAAMVSCNPRATATELKRTLLESADKYPSLADKVTEGRDLNAEKAISMFCKKNYIPVRQGRMSEEL +G9IBG0 unreviewed proteolysis [GO:0006508] Escherichia coli G9IBG0_ECOLX MFLLFISVSVRAEKPWYFDAIGLTETTMSLTDKNTPVVVSVVDSGVAFVGGLSDSEFAKFSFTQDGSPFPVKESEALYIHGTAMASLIASRHGVYGVYPHALISSRRVIPDGVQDSWIRATESIMSNVFLAPGEEKIINISGGQKGISSASVWTELLSRMGRNNERLIVAAVGNDGADIRKLSAQQRIWPAAYHPVSSMNKKRDPVIRVAALAQYRKGETPVLHGGGITGSRFGNGWVDIAAPGQNITFLRPDGKTGTGSGTSEATAIVSGVLAAMVSCNPRATATELKRTLLESADKYPSLADKVTEGRVLNAEKAISMFCKKNYIPVRQGRMSEEL +G9IBG1 unreviewed proteolysis [GO:0006508] Escherichia coli G9IBG1_ECOLX RLHILFLLFISVSVRAEKPWYFDAIGLTETTMSLTDKNTPVVVSVVDSGVAFVGGLSDSEFAKFSFTQDGSPFPVKEPEALYIHGTAMASLIASRHEVYGVYPHALISSRRVIPDGVQDSWIRATESIMSNVFLAPGEEKIINISGGQKGISSASVWSELLSRMGRNNERLIVAAVGNDGADIRKLSAQQRIWPAAYHPVSSVNKKQDPVIRVAALAQYRKGETPVLHGGGVTGSRFGNGWVDIAAPGQNITFLKPDGKTGIGSGTSEATAIVSGVLAAMVSCNPRATATELKRTLLESADKYPSLADKVTEGRVLNAEKAISMFCKKNYI +G9IBG2 unreviewed proteolysis [GO:0006508] Escherichia coli G9IBG2_ECOLX LTETTMSLTDKNTPVVVSVVDSGVAFVGGLSDSEFAKFSFTQDGSPFPVKEPEALYIHGTAMASLIASRHGVYGVYPHALISSRRVIPDGVQDSWLRATESIMSNVFLAPGEEKIINISGGQKGISSASVWSELLSRMGRNNERLIVAAVGNDGADIRKLSAQQRIWPAAYHPVSSVNKKQDPVIRVAALAQYRKGETPVLHGGGVTGSRFGNGWVDIAAPGQNITFLRPDGKTGIGSGTSEATAIVSGVLAAMVSCNPRATATELKRTLLESADKYPSLADKVTEGRDLNAEKAISMFCKKNYIPVRQGRMSEEL +G9IBG3 unreviewed proteolysis [GO:0006508] Escherichia coli G9IBG3_ECOLX RLHILFLLFISVSVRAEKPWYFDAIGLTETTMSLTDKNTPVVVSVVDSGVAFVGGLSDSEFAKFSFTQDGSPFPVKEPEALYIHGTAMASLIASRHEVYGVYPHALISSRRVIPDGVQDSWIRATESIMSNVFLAPGEEKIINISGGQKGISSASVWSELLSRMGRNNERLIVAAVGNDGADIRKLSAQQRIWPAAYHPVSSVNKKQDPVIRVAALAQYRKGETPVLHGGGVTGSRFGNGWVDIAAPGQNITFLKPDGKTGIGSGTSEATAIVSGVLAAMVSCNPRATATELKRTLLESADKYPSLADKVTEGRVLNAEKAISMFCKKNYIPVRQGRMSEEL +I3PB63 unreviewed Corynebacterium pseudotuberculosis I3PB63_CORPS ENFSSYHGTKPGYVDSIQKGIQKPKSGAQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKELSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGSSLSCINLDWDAIRDKTKTKIES +I6Y2S6 unreviewed negative regulation of translation [GO:0017148] Escherichia coli O157:H7 I6Y2S6_ECO57 SSYTTLQRVAALERSGMQISRHSLVSSYLALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEESQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +J4UHQ5 unreviewed Beauveria bassiana (strain ARSEF 2860) (White muscardine disease fungus) (Tritirachium shiotae) J4UHQ5_BEAB2 MRVFTARQSRLSWLSLTVLAAFFMCIGLIHGHPSSGSNLHQLSYRGAGDSKPKPPKGPKPPKGGDGPSVPPPTSGPSKGKKVGFKKNVDRLPKFELPKEPLVAYRGETRDPTVIEAAKGFFPRDPERALTAEEAEAGSGIYSHVAEKIVKDYTKYVSTSMDPGAASDFGKSTGYVYVVAPDPKMINVPETLGEFMPAQFAKENELIAVGQIPFEQVRGWIKKSTTIRAGDWLALREHGHKLSGGPLSDDLKKKIGNIANAYTPNKKYDAAKYDKTRASGGRPELAGFPKDSPAWKKEPWKQYQNKKVSESLTEFLNEVVCDKVEACVEAQLASKPGEAGVPNPYLDVDPPCKETDLKKRQEGEACRQLTEEDETETDDQVPEEDETETDGQVPEEGTEPSEGTEPEAPGNAEEGLGAEEKLDLARKTSEEVFEDLFTSRKLSEKAAEWGLKVQDARTRVLGYKPLTEASPHFSLSKAGLALEGAGLVAWGAGMVQAFTSNSTGWERAAAVTALVPFVGCGFNAIAESQTKEGIDVIDQMMCITADILLFTPLAPFAFVVQVFRVINGLFKTTSVPKAEEVQATRDKAWSKILDDKVYTYYYSDDSLSRNRKFRDKLNGTLWADSMAIVSDAADLIAAANATMQASAKSPNVDKDQINAVVLNVTDQIKAQIPSKVAVRQREFLLQLPQLIKQGTDLSVHKMADQFNNETITHLTSDAMVKQYTPTVFVPPGDPAGPDLKEGVRGELGAVAAHLEKTPLPLPKLFDVAYVLGQSKGMQDIDSLTLNPGEFIKSRAPEMSPDDVDSYALFHALQISMLLRGSVSEDKLSKLWPTDDKKTVQELQLLLALKFGRVFDSQKMAAAAQLAGQHPTPQEEKFMTHPSIPPLKIAPESMAYIGLILELSEERIKTVPHLKEEVGFKELGTDEKLIMAMLEKAKELYVERDKEMTLPEEGQAAPEGQAAPEGQAAPAA +K6UNZ7 unreviewed Austwickia chelonae NBRC 105200 K6UNZ7_9MICO MQQPRPQHGGVAVAQAFTDPKADALTKTAATLSVVPGLGQALGIADGIKHENTEEIVVQSISLAGLLAAQAIPVVGEAVDFGLLVYQLVETIVDLATHLSSAAANPPTEATDSVRPAVSLGLRAGWKTEEDAKLHIGSPYGMKFQRIVLSAEEGKEIPFVRAAVAVDSKFLKINGPRSFVVQNGIKTPMACFETEGNLAFCRPSRPIFLSSSSPATLHLSYVTNEHENGTIKNPTVDILGQRIVENKVITANKVSLVYKVDSSNTL +O34630 unreviewed Vibrio cholerae O34630_VIBCL MKKQIFTLDELQLDTNASPFVFVDYLAWSVPYASFRHAHKSDLSSLIWAPLPKPDYRMARTPEQKEKLIELYKQKWNVAMMERLEVFCLHVLGLRMSPWRDKGLYGYENSCHLMSKYSNKHVGFVALGGNRNTCYFQIEGVGCRTVLEHTSLFRLHWWLDLLGCSRLSRIDLAVDDFHGLFGREYAKKAYSDDAFRTARAGRAPNGGERLVSEPNGKIINESFEVGSRESRIYWRIYNKAAQLGLDMHWFRNEVELKDMPIDVLLNIEGYFAGLCAYSASIINSLPVKVVTKKRQVALDIHSRIKWARRQVGKTLFDISKHFGGDLERVFGALISKEIHDDSLNLPDSYMKLIDEIMGD +P97163 unreviewed Streptococcus pyogenes P97163_STRPY KKMVFFVLVTFLGLTISQEVFAQQDPDPSQLHRSSLVKNLQNIYFLYEGDPVTHENVKSVDQLLSHDLIYNVSGPNYDKLKTELKNQEMATLFKDKNVDIYGVEYYHLCYLCENAERSACIYGGVTNHEGNHLEIPKKIVVKVSIDGIQSLSFDIETNKKMVTAQELDYKVRKYLTDNKQLYTNGPSKYETGYIKFIPKNKESFWFDFFPEPEFTQSKYLMIYKDNETLDSNTSQI +Q332E0 unreviewed proteolysis [GO:0006508] Clostridium botulinum C phage (Clostridium botulinum C bacteriophage) Q332E0_CBCP MPITINNFNYSDPVDNKNILYLDTHLNTLANEPEKAFRITGNIWVIPDRFSRNSNPNLNKPPRVTSPKSGYYDPNYLSTDSDKDTFLKEIIKLFKRINSREIGEELIYRLSTDIPFPGNNNTPINTFDFDVDFNSVDVKTRQGNNWVKTGSINPSVIITGPRENIIDPETSTFKLTNNTFAAQEGFGALSIISISPRFMLTYSNATNDVGEGRFSKSEFCMDPILILMHELNHAMHNLYGIAIPNDQTISSVTSNIFYSQYNVKLEYAEIYAFGGPTIDLIPKSARKYFEEKALDYYRSIAKRLNSITTANPSSFNKYIGEYKQKLIRKYRFVVESSGEVTVNRNKFVELYNELTQIFTEFNYAKIYNVQNRKIYLSNVYTPVTANILDDNVYDIQNGFNIPKSNLNVLFMGQNLSRNPALRKVNPENMLYLFTKFCHKAIDGRSLYNKTLDCRELLVKNTDLPFIGDISDVKTDIFLRKDINEETEVIYYPDNVSVDQVILSKNTSEHGQLDLLYPSIDSESEILPGENQVFYDNRTQNVDYLNSYYYLESQKLSDNVEDFTFTRSIEEALDNSAKVYTYFPTLANKVNAGVQGGLFLMWANDVVEDFTTNILRKDTLDKISDVSAIIPYIGPALNISNSVRRGNFTEAFAVTGVTILLEAFPEFTIPALGAFVIYSKVQERNEIIKTIDNCLEQRIKRWKDSYEWMMGTWLSRIITQFNNISYQMYDSLNYQAGAIKAKIDLEYKKYSGSDKENIKSQVENLKNSLDVKISEAMNNINKFIRECSVTYLFKNMLPKVIDELNEFDRNTKAKLINLIDSHNIILVGEVDKLKAKVNNSFQNTIPFNIFSYTNNSLLKDIINEYFNNINDSKILSLQNRKNTLVDTSGYNAEVSEEGDVQLNPIFPFDFKLGSSGEDRGKVIVTQNENIVYNSMYESFSISFWIRINKWVSNLPGYTIIDSVKNNSGWSIGIISNFLVFTLKQNEDSEQSINFSYDISNNAPGYNKWFFVTVTNNMMGNMKIYINGKLIDTIKVKELTGINFSKTITFEINKIPDTGLITSDSDNINMWIRDFYIFAKELDGKDINILFNSLQYTNVVKDYWGNDLRYNKEYYMVNIDYLNRYMYANSRQIVFNTRRNNNDFNEGYKIIIKRIRGNTNDTRVRGGDILYFDMTINNKAYNLFMKNETMYADNHSTEDIYAIGLREQTKDINDNIIFQIQPMNNTYYYASQIFKSNFNGENISGICSIGTYRFRLGGDWYRHNYLVPTVKQGNYASLLESTSTHWGFVPVSE +Q46102 unreviewed Campylobacter jejuni Q46102_CAMJU MKKIITLFFMFITLAFATPTGDLKDFTEMVSIRSLETGIFLSAFRDTSKDPIDQNWNIKEIVLSDELKQKDKLADELPFGYVQFTNPKESDLCLAILEDGTFGAKSCQDDLKDGKLETVFSIMPTTTSAVQIRSLVLESDECIVTFFNPNIPIQKRFGIAPCTLDPIFFAEVNELMIITPPLTAATPLE +Q47169 unreviewed Escherichia coli Q47169_ECOLX VKVMKEGQPVQGHSVVFTTNFGMFNGKSQTQNATTGSDGRATITLTSSSAGKATVSATVSGGNDVKAPEVTFFDGLKIDNKVDILGKNVTGDLPNIWLQYGQFKLKVSGGNGTYSWHSENTNIATVDESGKVTLKGKGTAVINVTSGDKQTVSYTIKAPNYMIRVGNKASYANAMSFCGNLLPSSQTVLSNVYNSRGPANGYDHYRSMQSITARITQTEADKISGVSTTYDLITQNPHKDVQVNAPNVYAVCVE +Q4U4K4 unreviewed Affertcholeramvirus CTXphi Q4U4K4_9VIRU MKLWVINMKSRFVVFGASHSEGVSSKTGAPYLIPVLFVGKPIRQWKNDKGQCLTFGLQHQEVKFVSSDAMTRKLEQTAFPVLVTFDNEPDPEDPSRNLVIDYQVVCSLFDNVPVGKPLDKPQPIKS +Q4U4K5 unreviewed Affertcholeramvirus CTXphi Q4U4K5_9VIRU MKKQIFTLDELQLDTNASPFVFVDYLAWSVPYASFRHAHKSDLSSLIWAPLPKPDYRMARTPEQKEKLIELYKQKWNVAMMERLEVFCLHVLGLRMSPWRDKGLYGYENSCHLMSKYSNKHVGFVALGGNRNTCYFQIEGVGCRTVLEHTSLFRLHWWLDLLGCSRLSRIDLAVDDFHGLFGREYAKKAYSDDAFRTARAGRAPNGGERLVSEPNGKIINESFEVGSRESRIYWRIYNKAAQLGLDMHWFRNEVELKDMPIDVLLNIEGYFAGLCAYSASIINSLPVKVVTKKRQVALDIHSRIKWARRQVGKTLFDISKHFGGDLERVFGALISKEIHDDSLNLPDSYMKLIDEIMGD +Q541A8 unreviewed Affertcholeramvirus CTXphi Q541A8_9VIRU MKIKERLANQRKAINKTQAQMADEIGISLTSYKKYESGEGLPTMENLVKIADALEISIDELCGRWATDENQELMLRLKKIQQLDEEEQKAISMVLESMLIRHSTKSILNHGA +Q54696 unreviewed Streptococcus pyogenes Q54696_STRPY KKIVYFVLAIFLGLTTSQEVFAQQDPNPSQLHRSSLVKNLQNIYFLYEGDPVVHENVKSVDQLLSHDLIYNVSGLNYDKLKTELKNREMSTLFKNKNVDIYGVEYYYHCYLCRNAKRRACIYGGVTNHEGNHLEIPKNILVKVSIDGIQSLSFDIETSKKMVTAQELDYKVRKHLTDNKQLYTNGPSKYETGYIKFISKDKETFWFDFFPEPEFNQVKYLMIYKDNETLDSSTSQI +Q54779 unreviewed Streptococcus pyogenes Q54779_STRPY KKMVFFVLVTFLGLTISQEVFAQQDPDPSQLHRSSLVKNLQNIYFLYEGDPVTHENVKSVDQLLSHDLIYNVSGPNYDKLKTELKNQEMATLFKDKNIDIYGVEYYHLCYLCENAERSACIYGGVTNHEGNHLEIPKKIVVKVSIDGIQSLSFDIETNKKMVTAQELDYKVRKYLTDNKQLYTNGPSKYETGYIKFIPKNKESFWFDFFPEPEFTQSKYLMIYKDNETLDSNTSQI +Q57193 unreviewed Vibrio cholerae Q57193_VIBCL MIKLKFGVFFTVLLSSAYAHGTPQNITDLCAEYHNTQIHTLNDKIFSYTESLAGKREMAIITFKNGATFQVEVPGSQHIDSQKKAIERMKDTLRIAYLTEAKVEKLCVWNNKTPHAIAAISMAN +Q57453 unreviewed Streptococcus pyogenes Q57453_STRPY KKMVFFVLVTFLGLTISQEVFAQQDPDPSQLHRSSLVKNLQNIYFLYEGDPVTHENVKSVDQLLSHDLIYNVSGPNYDKLKTELKNQEMATLFKDKNVDIYSVEYYHLCYLCENAERSACIYGGVTNHEGNHLEIPKKIVVKVSIDGIQSLSFDIETNKKMVTAQELDYKVRKYLTDNKQLYTNGPSKYETGYIKFIPKNKESFWFDFFPEPEFTQSKYLMIYKDNETLDSNTSQI +Q5IL09 unreviewed Corynebacterium ulcerans Q5IL09_CORUL MNRKLFASILIGAILGIGAPPLAHAGADDVDDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGAQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGSSLSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKIVSEEKAKQYLEEFHQTALEHPELSELKTVTGVNSVFAGANYASWAVNVAQVVDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDLGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFVHGGYAASWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDDATTFCRPKTPVYVGNGVHANLHVAFHRSSSEKIHSDETPLSSIDVLGYQKTVDHTKVNSKLSLFAEVKS +Q5PY51 unreviewed Corynebacterium diphtheriae Q5PY51_CORDP MGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +Q5Q043 unreviewed Streptococcus pyogenes Q5Q043_STRPY LEVDNNSLLRDIYSTIVYEYSDTVIDFKTSHNLVTKKLDVRDARDFFINSEMDEYAANDFKTGDKIAVFSVPFDWNYLSKGKVTAYTYGGITPYQKTSIPKNIPVNLWINGKQISVPYNEISTNKTTVTAQEIDLKVRKFLIAQHQLYSSGSSYKSGRLVFHTNDNSDKYSFDLFYTGYRDKESIFKVYKDNKSFNIDKIGHLDIEIDS +Q5Q044 unreviewed Streptococcus pyogenes Q5Q044_STRPY LEVDNNSLLRNIYSTIVYEYSDTVIDFKTSHNLVTKKLDVRDARDFFINSEMDEYAANDFKDGDKIAMFSVPFDWNYLSEGKVIAYTYGGMTPYQEEPISKNIPVNLWINGKQISVPYNEISTNKTTVTAQEIDLKVRKFLIAQHQLYSSGSSYKSGKLVFHTNDNSDKYSLDLFYTGYRDKESIFKVYKDNKSFNIDKIGHLDIKIDS +Q5Q045 unreviewed Streptococcus pyogenes Q5Q045_STRPY LEVDNNSLLRNIYSTIVYEYSDTVIDFKTSHNLVTKKLDVRDARDFFINSEMDEYAANDFKDGDKIAMFSVPFDWNYLSEGTVIAYTYGGMTPYQEEPISKNIPVNLWINGKQISVPYNEISTNKTTVTAQEIDLKVRKFLISQHQLYSSGSSYKSGKLVFHTNDNSDKYSLDLFYVGYRDKESIFKVYKDNKSFNIDKIGHLDIEIDS +Q5XQS8 unreviewed negative regulation of translation [GO:0017148] Enterobacteria phage 020324 Q5XQS8_9VIRU MKCILFKWVLCLLLGFSSVSYSREFTIDFSTQQSYVSSLNSIRTEISTPLEHISQGTTSVSVINHTPPGSYFAVGIRGLDVYQARFDHLRLIIEQNNLYVAGFVNTATNTFYRFSDFTHISVPGVTTVSTTTDSSYTTLQRVAALERSGMQISRHSLVSSYQALMEFSGNTMTRDASRAVLRFVTVTAEALRFRQIQREFRQALSETAPVYTMTPGDVDLTLNWGRISNVLPEYRGEDGVRVGRISFNNISAILGTVAVILNCHHQGARSVRAVNEDSQPECQITGDRPVIKINNTLWESNTAAAFLNRKSQFLYTTGK +Q6KB43 unreviewed Escherichia coli Q6KB43_ECOLX KASITEIKADKTTAVANGKDAIKYTVKVMKNGQPVNNQSVTFSTNFGMFNGKSQTQATTGNDGRATITLTSSSAGKATVSATVSDGAEVKATEVTFFDELKIDNKVDIIGNNVRGELPNIWLQYGQFKLKASGGDGTYSWYSENTSIATVDASGKVTLNGKGSVVIKATSGDKQTVSYTIKAPSYMIKVDKQAYYADAMSICKNLLPSTQTVLSDIYDSWGAANK +Q6KC42 unreviewed cell adhesion [GO:0007155] Escherichia coli Q6KC42_ECOLX MITHGFYARTRHKHKLKKTFIMLSAGLGLFFYVNQNSFANGENYFKLSSDSKLLTQNAAQDRLFYTLKTGETVANISKSQGISLSVIWSLNKHLYSSESEMMKAGPGQQIILPLKKLSVEYSALPVLGSAPVVAAGGVTGHTNKMTKMSPDVTKSNTTDDKALNYAAQQAASLGSQLQSRSLNGDYAKDTALGMASSQASSQLQAWLQHYGTAEVNLQSGNNFDGSSLDFLLPFYDSENMLAFGQVGARYIDSRFTANLGAGQRFFLPENMLGYNVFIDQDFSGDNTRLGIGGEYWRDYFKSSVNGYFRMSGWHESYNKKDYDERPANGFDIRFNGYLPSYPALGAKLMYEQYYGDNVALFNSDKLQSNPGAATVGVNYTPIPLVTMGIDYRHGTGNENDLLYSMQFRYQFDKPWSQQIEPQYVNELRTLSGSRYDLVQRNNNIILEYKKQDILSLNIPHDINGTEHSTQKIQLIVKSKYGLDRIVWDDSALRSQGGQIQHSGSQSAQDYQAILPAYVQGGSNIYKVTARAYDRNGNSSNNVQLTITVLPNGQVVDQVGVTDFTADKTSAKADNVDTITYTATVKKNGVAQANAPVTFSIVSGTATLGANSAKTDGNGKATVTLKSGTPGQVVVSAKTAEMTSPLNASAVIFVDQTKASITEIKADKTTAKANGSDAITYTVKVMKNNQPEANHSVTFSTNFGNLGGNSNTQIVKTDKDGRATVKLTSGVAGNAIVSAKVSEVNTEVKAPEVKFFSVLSIDSNVNIIGTSATGALPNIWLQYGQFKLTAKGGDGKYQWRSQDPKVASVDALTGRVTLLKKGTTTIEVVSGDNQTATYTINTPIKIISVETKNKVVYNDAEAICRTNNGRLPLSTNELKDVYNKWGAANSYEGYKGKNTITAWTQQTEDDKTKGWTSTFDIVTKNEIPSNGSNNKVNVTAANAFAVCVR +Q6KE85 unreviewed Corynebacterium diphtheriae Q6KE85_CORDP MSRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAMYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLG +Q6V7G7 unreviewed Vibrio cholerae Q6V7G7_VIBCL MFDDLPTLTHAEQQVAVEKIQKLMAEGISTGEAIKIVAQQIREDKKAQNQGTH +Q6YIX8 unreviewed Corynebacterium ulcerans Q6YIX8_CORUL MNRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGAQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKELSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGSSLSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGVNSVFAGANYASWAVNVAQVVDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDLGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFVHGGYAASWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDDATTFCRPKTPVYVGNGVHANLHVAFHRSSSEKIHSDETPLSSIDVLGYQKTVDHTKVNSKLSLFAEVKS +Q6YIX9 unreviewed Corynebacterium ulcerans Q6YIX9_CORUL MNRKLFASILIGALLGIGAPPSAHAGADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKYDAAGYSVDNENPLSGKAGGVVKVTYPGLTKILALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKELSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGSSLSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHRTALEHPELSELKTVTGVNSVFAGANYASWAVNVAQVVDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDLGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFVHGGYAASWNTVEDSIIKTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDDATTFCRPKTPVYVGNGVHANLHVAFHTSSSEKIHSDETPLSSIDVLGYQKTVDHTKVNSKLSLFAEVKS +Q70LP2 unreviewed Pasteurella multocida Q70LP2_PASMD MKTKHFFNSDFTVKGKSADEIFRRLCTDHPDKQLNNVKWKEVFINRFGQMMLDTPNPRKIVEKIINEGLEKQGLKNIDPETTYFNIFSSSDSSDGNVFHYNSLSESYRVTDACLMNIFVERYFDDWDLLHSLASNGIYSVGKEGAYYPDHDYGPEYNPVWGPNEQIYHSRVIADILYARSVWDEFKKYFMEYWQKYAQLYTEMLSDTFLAMAIQQYTRQTLTDEGFLMVCNTYYGNKEEVQITLLDIYGYPSTDIICIEQKGLPTPKVILYIPGGTQPFVEFLNTDDLKQWIAWHLKDNKHMVAFRKHFSLKQRQEGETFTGIDKALQYIAEESPEWPANKYILYNPTHLETENLFNIMMKRTEQRMLEDSDVQIRSNSEATRDYALSLLETFISQLSAIDMLVPAVGIPINFALSATALGLSSDIVVNGDSYEKRKYGIGSLVQSALFTGINLIPVISETAEILSSFSRTEEDIPAFFTEEQALAQRFEIVEEELHSISPDDPPREITDENLHKIRLVRLNNENQPLVVLRRLGGNKFIRIEPITFQEIKGSLVSEVINPVTNKTYYVSNAKLLGGSPYSPFRIGLEGVWTPEVLKARASVIGKPIGESYKRILAKLQRIHNSNILDERQGLMHELMELIDLYEESQPSSERLNAFRELRTQLEKALYLPEMEALKKQILQIPNKGSGAARFLLRTAMNEMAGKTSESTADLIRFALQDTVISAPFRGYAGAIPEAIDFPVKYVIEDISVFDKIQTNYWELPAYESWNEGSNSALLPGLLRESQSKGMLSKCRIIENSLYIGHSYEEMFYSISPYSNQVGGPYELYPFTFFSMLQEVQGDLGFEQAFATRNFFNTLVSDRLSLMENTMLLTESFDYTPWDAIYGDINYDEQFAAMSINERIEKCMNTYRGVAFQNSSKSIDFFLNNLTTFIDNGLTEIAISDLPYDIVQQEISQFLQGSNEWKTLDAMLFNLDKGDINGAFRKLLQSAKDNNIKFRAIGHSDNSVPPFNNPYKSLYYKGNIIAEAIEKLDREGQKFVVFADSSLLNSTPGTGRPMPGLVQYLKIPATVVDSDGAWQFLPDVASSRVPIEVTELENWQVLTPPQGKILGLKQFKLTAGFPTEQSRLPLLENSVSEDLREELMQKIDAIKNDVKMNSLVCMEAGSCDSVSPKVAARLKDMGLEAGMGASITWWRREGGMEFSHQMHTTASFKFAGKEFAVDASHLQFVHDQLDTTILILPVDDWALEIAQRNRAINPFVEYVSKTGNMLALFMPPLFTKPRLTRAL +Q71US9 unreviewed Streptococcus pyogenes Q71US9_STRPY SNVKSDLLYAYTITPYDYKDCRVNFSTTHTLNIDTQKYRGKDYYISSEMSYEASQKFKRDDHVDVFGLFYILNSHTGEYIYGGITPAQNNKVNHKLLGNLFISGESQQNLNNKIILEKDIVTFQEIDFKIRKYLMDNYKIYDATSPYVSGRIEIGT +Q76MJ3 unreviewed Streptococcus pyogenes Q76MJ3_STRPY MKKINIIKIVFIITVILISTISPIIKSDSKKDISNVKSDLLYAYTITPYDYKDCRVNFSTTHTLNIDTQKYRGKDYYISSEMSYEASQKFKRDDHVDVFGLFYILNSHTGEYIYGGITPAQNNKVNHKLLGNLFISGESQQNLNNKIILEKDIVTFQEIDFKIRKYLMDNYKIYDATSPYVSGRIEIGTKDGKHEQIDLFDSPNEGTRSDIFAKYKDNRIINMKNFSHFDIYLEK +Q776Q5 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q776Q5_BPVT2 MQYAIAGWPVAGCPSESLLERITRKLRDGWKRLIDILNQPGVPKNGSNTYGYPD +Q77DH3 unreviewed Affertcholeramvirus CTXphi Q77DH3_9VIRU MFSSKIRDLRVERDLNQEEVANGIGVGKNTYLAYEKGTQSPKLETVEKLAKFYGVPIAELVSDSETNIDEKLKSKIRMIESLDEPEKESLFILMEALLMRSKSREIQKEFR +Q77DH7 unreviewed modulation of host virulence by virus [GO:0098676] Affertcholeramvirus CTXphi Q77DH7_9VIRU MIKLKFGVFFTVLLSSAYAHGTPQNITDLCAEYHNTQIYTLNDKIFSYTESLAGKREMAIITFKNGAIFQVEVPGSQHIDSQKKAIERMKDTLRIAYLTEAKVEKLCVWNNKTPHAIAAISMAN +Q77DI6 unreviewed Affertcholeramvirus CTXphi Q77DI6_9VIRU MVKIIFVFFIFLSSFSYANDDKLYRADSRPPDEIKQSGGLMPRGQSEYFDRGTQMNINLYDHARGTQTGFVRHDDGYVSTSISLRSAHLVGQTILSGHSTYYIYVIATAPNMFNVNDVLGAYSPHPDEQEVSALGGIPYSQIYGWYRVHFGVLDEQLHRNRGYRDRYYSNLDIAPAADGYGLAGFPPEHRAWREEPWIHHAPPGCGNAPRSSMSNTCDEKTQSLGVKFLDEYQSKVKRQIFSGYQSDIDTHNRIKDEL +Q79AQ0 unreviewed Streptococcus pyogenes Q79AQ0_STRPY KKMVFFVLVTFLGLTISQEVFAQQDPDPSQLHRSSLVKNLQNIYFLYEGDPVTHENVKSVDQLLSHDLIYNVSGPNYDKLKTELKNQEMATLFKDKNVDIYGVEYYHLCYLCENAERSACIYGGVTNHEGNHLEIPKKIVVKVSIDGIQSLSFDIETNKKMVTAQELDYKVRKYLTDNKQLYTNGPSKYETGYIKFIPKNKESFWFDFFPEPEFTQSKYL +Q7A9R1 unreviewed Escherichia coli O157:H7 Q7A9R1_ECO57 MNDISSDDIFLLKQRLAEQEALIHALQEKLSNREREIDHLQAQLDKLRRMNFGSRSEKVSRRIAQMEADLNRLQKESDTLTGRVYDPAVQRPLRQTRTRKPFPESLPRDEKRLLPAAPCCPNCGGSLSYLGEDTAEQLELMRSAFRVIRTVREKHACTQCDAIVQAPAPSRPIERGIAGPGLLARVLTSKYAEHTPLYRQSEIYGRQGVELRRSLLSGWVDACCRLLSPLEEALHGYVMTDGKLHADDTPVQVLLPGNKKTKTGRLWAYVRDDRNAGSALAPAVWFAYSPDRKGIHPQTHLACFSGVLQADAYAGFNELYRNGGITEAACWAHARRKIHDVHVRIPSALTEEALEQIGQLYAIEADIRGMPAEQRLAERQRKTKPLLKSLESWLREKMKTLSRHSELAKAFAYALNQWPALTYYANDGWVEIDNNIAENALRAVSLGRKNFLFFGSDHGGERGAPLYSLIGTCKLNDVDPESYLRHVLGVIADWPVNRVSELLPWRIALPAE +Q7AEZ3 unreviewed Escherichia coli O157:H7 Q7AEZ3_ECO57 MSMYTTAQLLAANEQKFKFDPLFLRLFFRESYPFTTEKVYLSQIPGLVNMALYVSPIVSGEVIRSRGGSTSEFTPGYVKPKHEVNPQMTLRRLPDEDPQNLADPAYRRRRIIMQNMRDEELAIAQVEEMQAVSAVLKGKYTMTGEAFDPVEVDMGRSAANNITQSGGTEWSKRDKSTYDPTDDIEAYALNASGVVNIIVFDPKGWALFRSFKAVREKLDTRRGSHSELETAVKDLGKAVSYKGMYGDVAIVVYSGQYVENGVKKNFLPDNTMVLGNTQARGLRTYGCIQDADAQREGINASARYPKNWVTTGDPAREFTMIQSAPLMLLADPDEFVSVQLA +Q7AEZ5 unreviewed proteolysis [GO:0006508] Escherichia coli O157:H7 Q7AEZ5_ECO57 MTAELRNLPHIASMAFNEPLMLEPAYARVFFCALAGQLGISRLTDAVSGDSLTAGEAPAALALSGDDDGPRQARSYQVMNGIAVLPVSGTLVSRTRALQPYSGMTGYNGIIARLQQAASDPMVDGILLDMDTPGGMVAGAFDCADIIARVRDIKPVWALANDMNCSAGQLLASAASRRLVTQTARTGSIGVMMAHSNYGAALEKQGVEITLIYSGSHKVDGNPYSHLPGDVRETLQSRMDATRRMFAQKVSAYTGLSVQAVLDTEAAVYSGQEAIDAGLADELVNSTDAITVMRDALDARKSRLSGGRMTKETQSTTVSATASQADVTGVVPATEGENASAAQPDVNAQITAAVAAENSRIMGILNCEEAHGREEQARVLAETPGMTVETARRILAAAPQSAQARSDTALDRLMQGAPAPLAAGNPASDAVNDLLNTPV +Q7AEZ6 unreviewed virion assembly [GO:0019068] Escherichia coli O157:H7 Q7AEZ6_ECO57 MKMSTIPTLLGPDGMTSLREYAGYHGGGSGFGGQLRAWNPPGESVDAALLPNFTRGNARADDLVRNNGYAANAIQLHQDHIVGSFFRLSHRPSWRYLGIGEEEARAFSREVEAAWKEFAEDDCCCIDVERKRTFTMMIREGVAMHAFNGELFVQATWDTRPSRLFRTQFRMVSPKRISNPNNTSDSRNCRAGVQINDSGAALGYYVSEDGYPGWMPQKWTWIPRELPGGRASFIHVFEPVEDGQTRGANVFYSVMEQMKMLDTLQNTQLQSAIVKAMYAATIESELDTQSAMDFILGANSQEQRERLTGWIGEIAAYYAAAPVRLGGAKVPHLMPGDSLNLQTAQDTDNGYSVFEQSLLRYIAAGLGVSYEQLSRNYAQMSYSTARASANESWAYFMGRRKFVASRQASQMFLCWLEEAIVRRVVTLPSKARFSFQEARSAWGNCDWIGSGRMAIDGLKEVQEAVMLIEAGLSTYEKECAKRGDDYQEIFAQQVRETMERRAAGLKPPAWAAAAFESGLRQSTEEEKSDSRAA +Q7AF45 unreviewed chromosome organization [GO:0051276] Escherichia coli O157:H7 Q7AF45_ECO57 MLTTQKRKFALALMSGKNKTASALAAGYSAKTARVKGSQLAKDPEVLAFIARKQCETVEVDEVPVYRQKKSEPEDKPRRREAAAIPQPDETNPEMPPPVVISPGIEYMEDGLPDPVKAMGRLLVENINTDPRLALDAAYKLAQFTHHKKGDAGKKSAKGDAAKKAANRFAVPPPPRLVVNNDNEGNG +Q7AF46 unreviewed Escherichia coli O157:H7 Q7AF46_ECO57 MAVLRTLPGRIKTLNTRRINVLRGEQRRVSGSARVSLKRRIWRRDAGHCCLCRRVVDLCDSELDHRIALQFGGGNEETNLWTLCTECHRQKSASEAASGMPDPTLPELPDGTLRADGITGL +Q7AFY5 unreviewed proteolysis [GO:0006508] Escherichia coli O157:H7 Q7AFY5_ECO57 MRRNLSHIIAAAFNEPLLLEPAYARVFFCALGREMGAASLSVPQQQVQLDAPGMLAETDEYMAGGKRPARVYRVVNGIAVLPVTGTLVHRLGGMRPFSGMTGYDGIVACLQQAMADSQVRGVLLDIDSPGGQAAGAFDCADMIYRLRQQKPVWALCNDTACSAAMLLASACSRRLVTQTSRIGSIGVMMSHVSYAGHLAQAGVDITLIYSGAHKVDGNQFEALPAEVRQNMQQRIDAARRMFAEKVAMFTGLSVDAVTGTEAAVFEGQSGIDAGLADELVNASDAISVMATALNSNVRGGTMPQLTATEAAAQENQRVMGILTCQEAKGREQLATMLAGQQGMSVEQARAILAAAAPQQPVASTQSEADRIMACEEANGREQLAATLAAMPEMTVEKARPILAASPQADAGPSLRDQIMALDEAKGAEAQAEQLAACPGMTVESARAVLAAGSGKAEPVSASTTALFERIMANHSPAAVQGGVPQTSADGDADVKMLMAMP +Q7AFY6 unreviewed virion assembly [GO:0019068] Escherichia coli O157:H7 Q7AFY6_ECO57 MKRTPVLIDVNGVPLRESLSYNGGGAGFGGQMAEWLPPAQSADAALLPALRLGNARADDLVRNNGIAANAVALHKDHIVGHMFLISYRPNWRWLGMRETAAKSFVDEVEAAWSEYAEGMSGEIDVEGKRTFTEFIREGVGVHAFNGEIFVQPVWDTETTQLFRTRFKAVSPKRVDTPGHGMGNRFLRAGVEVDRYGRAVAYHICEDDFPRSGSGRWERIPRELPTGRPAMLHIFEPVEDGQTRGANQFYSVMERLKMLDSLQATQLQSAIVKAMYAATIESDLDTEKAFEYIAGAPQGQKDNPLINILEKFSSWYDTNNVTLGGVKIPHLFPGDDLKLQTAQDSDNGFSALEQALLRYIAAGLGVSYEQLSRDYSKVSYSSARASANESWRYFMGRRKFIAARLATQMFSYWLEEALLRGIIRPPRARFDFYQARSAWSRAEWIGAGRMAIDGLKEVQESVMRIEAGLSTYEKEPALMGEDYQDIFRQQVRESAERQKAGLSRPVWIAQAYQQQIAESRRPEEETTPRET +Q7AGG7 unreviewed polyphosphate catabolic process [GO:0006798]; RNA decapping [GO:0110154] Escherichia coli O157:H7 Q7AGG7_ECO57 MNIYERIDGSKYRNIWVVGDLHGCYTNLMKKLETIGFDTKKDLLISVGDLVDRGTENVECLELITFPWFRAVRGNHEQMMIDGLSERGNVNHWLLNGGGWFFNLDYDKEILAKALAHKADELPLIIELVSKGKKYVICHADYPCDKYEFGKPVDHQQVIWNRERISNSQDGIVKEIKGADTFIFGHTPAVKPLKFANQMYIDTGAVFCGNLTLIQVQGEGA +Q7AK24 unreviewed DNA transposition [GO:0006313] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q7AK24_BPVT2 MTKNTRFSPEVRQRAIRMVLESQDEYDSQWAAICSIAPKIGCTPETLRVWVRQHERDTGGGDGGLTSAERQRLKELERENRELRRSNDILRQASAYFAKAEFDRLWKK +Q7AK25 unreviewed DNA integration [GO:0015074] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q7AK25_BPVT2 MMPLLDKLREQYGVGPVCSELHIAPSTYYHCQQQRHHPDKRSARAQHDDWLKREIQRVYDENHQVYGVRKVWRQLLREGIRVARCTVARLMAVMGLAGVLRGKKVRTTISRKAVAAGDRVNRQFVAERPDQLWVADFTYVSTWQGFVYVAFIIDVFAGYIVGWRVSSSMETTFVLDALEQALWARRPSGTIHHSDKGSQYVSLAYTERLKEAGLLASTGSTGDSYDNAMAESINGLYKAEVIHRKSWKNRAEVELATLTWVDWYNNRRLLGRLGHTPPAEAEKAYYASIGNDDLAA +Q7AK26 unreviewed hemolysis by symbiont of host erythrocytes [GO:0019836]; modulation of host virulence by virus [GO:0098676] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q7AK26_BPVT2 MKKMFMAVLFALASVNAMAADCAKGKIEFSKYNEDDTFTVKVDGKEYWTSRWNLQPLLQSAQLTGMTVTIKSSTCESGSGFAEVQFNND +Q7AK28 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q7AK28_BPVT2 MTFTVKTIPDMLVEAYENQTEVARILNCSRNTVRKYTGDKEGKRHAIVNGVLMVHRGWGKDTDA +Q7AK29 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q7AK29_BPVT2 MRRQRRSITDIICENCKYLPTKRSRNKRKPIPKESDVKTFNYTAHLWDIRWLRHRARKTR +Q7AK30 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q7AK30_BPVT2 MTKQLSPYQDKIHKHILRDRFLSSFKQPGRFRAELEKVKLMQKEKGHE +Q7B9N1 unreviewed Vibrio cholerae Q7B9N1_VIBCL MIKLKFGVFFTVLLSSAYAHGTPQNITDLCAEYHNTQIHTLNDKILSYTESLAGNREMAIITFKNGATFQVEVPGSQHIDSQKKAIERMKDTLRIAYLTEAKVEKLCVWNNKTPHAIAAISMAN +Q7BBA3 unreviewed Affertcholeramvirus CTXphi Q7BBA3_9VIRU MLMMDTLYDWLIDGFTWLVIKLGIMWIESKIFVIQFFWEMSQKVIDMFTIYPLIQQAIDMLPPQYSGFLFFLGLDQALAIVLQALMTRFALRALNL +Q7BBA4 unreviewed Affertcholeramvirus CTXphi Q7BBA4_9VIRU MFSSLKNKLNTFKSTLSLGVFLLFSAFANQALAAADAGLVTEVTKTLGTSKDTVIALGPLIMGVVGAIVLIVTVIGLIRKAK +Q7BSU2 unreviewed DNA integration [GO:0015074] Escherichia coli O157:H7 Q7BSU2_ECO57 MPLLDKLREQYGVGPVCSELHIAPSTYYHCQQQRHHPDKRSARAQHDDWLKREIQRVYDENHQVYGVRKVWRQLLREGIRVARCTVARLMAVMGLAGVLRGKKVRTTISRKAVAAGDRVNRQFVAERPDQLWVADFTYVSTWQGFVYVAFIIDVFAGYIVGWRVSSSMETTFVLDALEQALWARRPSGTIHHSDKGSQYVSLAYTERLKEAGLLASTGSTGDSYDNAMAESINGLYKAEVIHRKSWKNRAEVELATLTWVDWYNNRRLLGRLGHTPPAEAEKAYYASIGNDDLAA +Q7BTR4 unreviewed Streptococcus pyogenes Q7BTR4_STRPY SNVKSDLLYAYTITPYDYKNCRVNFSTTHTLNIDTQKYRGKDYYISSEMSYEASQKFKRDDHVDVFGLFYILNSHTGEYIYGGITPAQNNKVNHKLLGNLFISGESQQNLNNKIILEKDIVTFQEIDFKIRKYLMDNYKIYDATSPYVSGRIEIGT +Q7DAR7 unreviewed viral release from host cell by cytolysis [GO:0044659] Escherichia coli O157:H7 Q7DAR7_ECO57 MNRVLCVVIIVLAVGYGALWLATNHYRDNALTYKAQRDKKARELEQANATITDMQVRQRDVAALDAKYSRELADARAENETLRADVAAGRKRLRINATCSGTVREATGTSGVDNATGPRLADTAERDYFILRERLMTMQKQLEGAQDYIRTQCLN +Q7DAS4 unreviewed Escherichia coli O157:H7 Q7DAS4_ECO57 MTEADLYPHLAHLAGGQVYPYVVPLLDGRPSVALPWVVFSLISSVSADVMGGQAESSVSVQIDVYAGTVTQARQIRQDAREAIMLLAPGSVSEMQDYIPENRCYRATLEFQVTV +Q7DB49 unreviewed Escherichia coli O157:H7 Q7DB49_ECO57 MKRKPLIAFFIAIFIALTILILFPFNCTYKKSPQEHSLSFIKQLPNTVNLSSLTYNKEDDFLYATQNSPAQLLKITKSGDIMDRAPLPFISDAETIEHIQGNIFAAVDEKTSELFFFTVTKDMHISFRNKIQLEKFNKKNRGFEGLAWKADDRMLFVAKERRPSKIFIYQLSPDLLSVKQATIPEALNDIRVNDISGLAFNNESLMILSDESRKLLKFNLTEMSFIEMLDLTKGNHSLTSDLPQPEGIVTLPDESIYVASEPDILAKFVPNK +Q7DB52 unreviewed Escherichia coli O157:H7 Q7DB52_ECO57 MITITELEDEIIKNKEAANVFIEKINDKKNEIHEKMKHPLDKVTYNEAKELLIACDAAIRTIEIMRIRINNK +Q7DB54 unreviewed Escherichia coli O157:H7 Q7DB54_ECO57 MTIFNKIDYNYYKLINYPIMMVHDEWLGDLTGVNQVSFRRLRETSSTRNQLNKILRQEIHDKISGVELSDINKEGFLYQSIGKIRLLALSSALFDIQCPDYIFSRLYRETLIREIGYQNVKQLSFYWQGGQCKPEYGEERFCAELIKYGAGNLEWLFADNPLWTIVKYLLPKSGEIKPTHINDLFLNRLNKILLPYETL +Q7DB56 unreviewed protein secretion [GO:0009306] Escherichia coli O157:H7 Q7DB56_ECO57 MSQLMTIGSQPIFLIIVFFLLSLLPIFVVIGTSFLKISIVLGILKNALGIQQVPPNMALTSVSLILTMFIMSPIILQINDNISQEPINYTDSDFFQKVDEKILSPYRGFLEKNTEKDNVEFFERAAQKKLGNETILKKDSLFILLPAFTMGQLEAAFKIGFLLYLPFIAIDLIISNILLALGMMMVSPVTISIPFKILLFILVGGWQKLFEFLLVVN +Q7DB58 unreviewed protein targeting [GO:0006605] Escherichia coli O157:H7 Q7DB58_ECO57 MNEIMTVIVSSFYCILRPLGMFIILPIFSTGVLLSNFIRNSIMIAFTLPIIVENYTFSEKLPSGIFQLTGIALKEISIGFFIGLSFTILFWAIDAAGQIIDTLRGSTISSIFNPSISDSSSITGVILYQFISVIFVIHGGIQSILDKLYLSYEILPLQADIAFNRALIDFLFSLWDSFIKLMLSFSVPMIIGIFLCDMGFGFLNKTAPQLNVFTLSLPVKSLIAIFILLLVIHVFPDFITANIHSDIIDRSLPSMINE +Q7DB59 unreviewed protein secretion [GO:0009306] Escherichia coli O157:H7 Q7DB59_ECO57 MSEKTEKPTPKKLRDLKKKGDVTKSEEVMAAVQSLILFSFFSLYGMSFFVDIVGLVNTTIDSLNRPFLYAIREILGAVLNIFLLYILPISLIVFVGTVTTGVSQIGFIFAVEKIKPSAQKISVKNNLKNIFSVKSIFELLKSVFKLVIIVLIFYFMGHSYANEFANFTGLNAYQALVVVAFFVFLLWKGVLFGYLLFSVFDFWFQKHEGLKKMKMSKDEVKREAKDTDGNPEIKGERRRLHSEIQSGSLANNIKKSTVIVKNPTHIAICLYYKLGETPLPLVIETGKDAKALQIIKLAELYDIPVIEDIPLARTLYKNIHKGQYITEDFFEPVAQLIRIAIDLDY +Q7DB60 unreviewed Escherichia coli O157:H7 Q7DB60_ECO57 MKKIILSIILISNYCYASDCFEITGKAYNIDPLILKAIAWNESKCKSGIKSKTNKNGTYDIGIMQINSSHLDLLSKFNISEDDLLNDACINISVAGYILASNIKSRGNTWDAVGAYNAGYFNTPNAVELRRQYAMKIYKTYNKLKNNEQIID +Q7DB61 unreviewed Escherichia coli O157:H7 Q7DB61_ECO57 MIMKDGIYSIIFISNEDSCGEGILIKNGNMITGGDIASVYQGVLSEDEDIILHVHRYNYEIPSVLNIEQDYQLVIPKKVLSNDNNLTLHCHVRGNEKLFVDVYAKFIEPLVIKNTGMPQVYLK +Q7DB62 unreviewed DNA-templated transcription [GO:0006351] Escherichia coli O157:H7 Q7DB62_ECO57 MESKNKNGDYVIPDSVKNYDGEPLYILVSLWCKLQEKWISRNDIAEAFGINLRRASFIITYISRRKEKISFRVRYVSYGNLHYKRLEIFIYDVNLEAVPIESPGTTGPKRKTYRVGNGIVGQSNIWNEMIMRRKKES +Q7DB63 unreviewed Escherichia coli O157:H7 Q7DB63_ECO57 MSRKFSSLEDIYDFYQDGGTLASLTNLTQQDLNDLHSYAYTAYQSGDVITARNLFHLLTYLEHWNYDYTLSLGLCHQRLSNHEDAQLCFARCATLVMQDPRASYYSGISYLLVGNKKMAKKAFKACLMWCNEKEKYTTYKENIKKLLGNTE +Q7DB65 unreviewed Escherichia coli O157:H7 Q7DB65_ECO57 MNNNNGIAKNDCDWLTALDFVKDVNGSPTHLTFYIYQKNAFLHDFGNYWVLYIELSGDFRQVPTDTFIRLCNILAVSNEYKQMGIFLSNKKWYLCQIFHKDNNHRANMSKAIMQHTLASLLDKQFDKLEQLSSSDTMMPPTHLFSDIGRIV +Q7DB68 unreviewed Escherichia coli O157:H7 Q7DB68_ECO57 MEAANLSPSGAVMPLATSLSGNNSVDEKTGVIKPENGTNRTVRVIAGLALTTTALAALGTGIAAACSETSSTEYLALGITSGVLGTLTAVGGALAMKYA +Q7DB69 unreviewed Escherichia coli O157:H7 Q7DB69_ECO57 MNLLVKRNVEEFLRLLGNDFYLFDNRVEIDFNGFSFFIEIIDNNVFVTFALEYNENAFFSFFSALAPERTQGVIEHIFVYDNKLCLSCLLTNIDVFFLMNTFQQHVQIIERVRRMTS +Q7DB70 unreviewed protein secretion [GO:0009306] Escherichia coli O157:H7 Q7DB70_ECO57 MNKLLNIFKKAESYHDLILALFFFMAVMMMIIPLPTVVVDIIIAINISTALLLLMLSIYIKNPLELTSFPTILLITTLMRLSLSVSTTRLILLHHDAGDIIYSFGNFVVGGNIVVGLVIFTIITIVQFMVITKGAERVAEVSARFSLDGMPGKQMSIDGDMRAGVIDPLEAKVLRSRVQKESQFYGSMDGAMKFVKGDAIAGIIIVLVNLFGGVLIGMWQFDMPFSAALSLFSVLSVGDALVAQIPALIISVTAGVVVTRVPGESEKEENLAGDIVQQVSVNSRPFLISAALMLVMAIIPGFPALVFLFLAVCLLGIAWKLQKKRTFGTGNNKDAMGADLSNSQNISPGAEPLILNLSSNIYSSDITQQIEVMRWNFFEESGIPLPKIIVNPVKNNDSAIEFLLYQESIYKDTLIDDTVYFEAGHAEISFEFVQEKLSTNSIVYKTNKTNQQLAHLTGMDVYATTNDKITFLLKKLVLSNAKEFIGVQETRYLMDIMERKYNELVKELQRQLGLSKIVDILQRLVEENVSIRDLRTIFETLIFWSTKEKDVVILCEYVRIALRRHILGRYSVSGTLLNVWLIGSDIENELRESIRQTSSGSYLNISPERTEQIIGFLKNIMNPTGNGVILTALDIRRYVKKMIEGSFPSVPVLSFQEVGNNIELKVLGTVNDFRA +Q7DB73 unreviewed Escherichia coli O157:H7 Q7DB73_ECO57 MKPLSSQLNMKINDFYLPLLPVIGTGRLYITSKGHACHAYFREVSGHGIRFTLTYSGYEGRFWISEEQFIQWCQELFPYSESRLIPEDTIKLMILWVMQTALPEGDVSVDDVQFTMLNKDIYPVIENNNGENRLNVIILETTVQSLQYLINDNWQLVPHSNALFFDGYIVPGWTDYPVTELRVGDSLRLYHVDDSQERRCWIVINTPLATVNLSDNNLSVTDVLAADLLSALSNETVMNRIYCAIGTVHIDIHMLRNVKKDDIINSDGYHLFGGCQLIRNNTTIAYGSIVKINEDFYFTVSIVCD +Q7DB74 unreviewed Escherichia coli O157:H7 Q7DB74_ECO57 MSLSGAVFKTFLTSEHASWNRFNRRLHIPNEDIVDEIQLKARMQQRHHRVYPEIGDSTIVSFRGKDYAVHFIKDGPKDDYVYKVQRITPENGCFSTLFSVFSGGVTKALERKLNERHITPLSSTWFPRTPLEGILAERGLSSLLRRVQSTERLDNRAIATRASSYSVL +Q7DB75 unreviewed Escherichia coli O157:H7 Q7DB75_ECO57 MNEKFRTDLAHTFGIALEEQTDVLSFHDNDGHEWILECASQSEILFFYCYLLNSESIQINSILEMNSNRELLGMFFLSLKDDNILLNIAFPADKIDITEFANLMENGYLLKNEIIRSLSSRPTDFLP +Q7DB76 unreviewed Escherichia coli O157:H7 Q7DB76_ECO57 MFSPMTMAGRSLVQATAQTLKPAVTRAAMQAGTGATGMRFMPVQSNFVINHGKLTNQLLQAVAKQTRNGDTQQWFQQEQTTYISRTVNRTLDDYCRSNNSVISKETKGHIFRAVENALQQPLDMNGAQSSIGHFLQSNKYFNQKVDEQCGKRVDPITRFNTQTKMIEQVSQEIFERNFSGFKVSEIKAITQNAILEHVQDTRL +Q7DB78 unreviewed Escherichia coli O157:H7 Q7DB78_ECO57 MLSSYKIKLLNGAMRNRELQLPMGNLTLGTEDNDIVYFPLEQGLNQFLLDIREEGVFLLSPVEFWIDGQPTPYEADKPLPVGKVIDIAGCCFIIGDIDHSLPLSDVPERFSAKSRRKKRLILASVIGATFALSGAIGSYVLLSPKAEPPTFTRADVYQQLKENKLHAITLVWHGKNIALYGRCESTTDLTPFFNYLKEKNIFYYNNIICNNQIISAINDVLTEYGYKDIIITKGNKPGFFLLSGYIPPSPKWSEVENLLLNTPGVAGWEIHNNSNNKINELASEFKKNKLINYVNIFKKNDVIIVAGEVSQQNESKILAIINAMNKNSNAKILFQNIQPYISADIFPGKILRISGTMKNPTIALDNGTSLGIGSILKGGYVIDAIDPKDGINISRPDEYIHIPLSY +Q7DB79 unreviewed secretion [GO:0046903] Escherichia coli O157:H7 Q7DB79_ECO57 MANGIEFNQNPASVFNSNSLDFELESQQLTQKNSSNISSPLINLQNELAMITSSSLSETIEGLSLGYRKGSARKEEEGSTIEKLLNDMQELLTLTDSDKIKELSLKNSGLLEQHDPTLAMFGNMPKGEIVALISSLLQSKFVKIELKKKYARLLLDLLGEDDWELALLSWLGVGELNQEGIQKIKKLYEKAKDEDSENGASLLDWFMEIKDLPEREKHLKVIIRALSFDLSYMSSFEDKVKTSSIISDLCRVIIFLSLDNYADIISISIKKDKDIILNEVLSIIEHVWLTEDWLLESPSRVSIVEDKHIYYFHLLKDFFTSLPDACFIDSEQRENALLMIGKVIDYKEEII +Q7DB80 unreviewed Escherichia coli O157:H7 Q7DB80_ECO57 MDTSNATSVVNVSASSSTSTIYDLGNMSKDEVVKLFEELGVFQAAILMFSYMYQAQSNLSIAKFADMNEASKASTTAQKMANLVDAKIADVQSSTDKNAKAKLPQDVIDYINDPRNDISVTGIRDLSGDLSAGDLQTVKAAISAKANNLTTVVNNSQLEIQQMSNTLNLLTSARSDVQSLQYRTISAISLGK +Q7DB81 unreviewed Escherichia coli O157:H7 Q7DB81_ECO57 MLNVNNDTLSVTSGVNTASGTSGITQSETGLSLDLQLVKSMNSSAGWTESSPLPTPPAGHSLVTPSAAEDVLSKLFGGISGEVTSRTEEAEPQRTSYPYLSQVNTVDPQQMMMMVTLLSLDTSAQKVSSLKNSNEIYMDGQTKALENKTQEYKKQLEEQQKAEEKSQKSKIVGQVFGWLGVALTAVAAVFNPALWAVVAIGATAMALQTAVDVMGENAPQGLKTAAQVFGGISMAASILTAGVGGVSSLLSKFGNVANKIGSSVVKVVEKAAEALVKNVFAKISTVAEGVTNGIRSAGTTALNNEAAQLQMLSQLAAFAVQNLTRQSESLGESAKLELDKAASELQNQASYLQSVSQLMSDSARVNSRIVSGRI +Q7DB82 unreviewed Escherichia coli O157:H7 Q7DB82_ECO57 MVDTFNDEVFNYYLEQKGYTIQKEFLCGSAFFIGWRIETPFFSLAYRLDEQELILCSFEARNQTGLNGPVLSLTHLLEELYHHFSGIKKISAMKSKIGSDSERQKREELFNYFIRKGAVQQETEDGIWFVMNVNS +Q7DB83 unreviewed protein secretion by the type III secretion system [GO:0030254] Escherichia coli O157:H7 Q7DB83_ECO57 MNLSEITQQMGEVGKTLSDSVPELLNSTDLVNDPEKMLELQFAVQQYSAYVNVESGMLKTIKDLVSTISNRSF +Q7DB84 unreviewed Escherichia coli O157:H7 Q7DB84_ECO57 MVNDISANKILVWAAVAAANHKLPKYAEAILNVFPQIIPDKKDIAHLEFIILFGLNRKNDAVKALEDCMDDETSQLLYSLVHENGSGWVRGF +Q7DB85 unreviewed Escherichia coli O157:H7 Q7DB85_ECO57 MLNGISNAASTLGRQLVGIASRVSSAGGTGFSVAPQAVRLTPVKVHSPFSPGSSNVNARTIFNVSSQVTSFTPSRPAPPPPTSGQASGASRPLPPIAQALKEHLAAYEKSKGPEALGFKPARQAPPPPTSGQASGASRPLPPIAQALKEHLAAYEKSKGPEALGFKPARQAPPPPTSGQASGASRPLPPIAQALKEHLAAYEKSKGPEALGFKPARQAPPPPTGPSGLPPLAQALKDHLAAYEQSKKG +Q7DBE5 unreviewed Escherichia coli O157:H7 Q7DBE5_ECO57 MWMEFDRVSPLGDERGDIRNAQIVKAVFGAQGMNVALKDAMLCWGEDEDKPEVDPFAALEDALSLAAMS +Q7DBI1 unreviewed DNA transposition [GO:0006313] Escherichia coli O157:H7 Q7DBI1_ECO57 MTKNTRFSPEVRQRAVRMVLESQGEYDSQWAAICSIAPKIGCTPETLRVWVRQHERDTGGGDGGLTTAERQRLKELERENRELRRSNDILRQASAYFAKAEFDRLWKK +Q7DBL5 unreviewed DNA transposition [GO:0006313] Escherichia coli O157:H7 Q7DBL5_ECO57 MTKNTRFSPEVRQRAIRMVLESQDEYDSQWAAICSIAPKIGCTPETLRVWVRQHERDTGGGDGGLTSAERQRLKELERENRELRRSNDILRQASAYFAKAEFDRLWKK +Q7DBM4 unreviewed Escherichia coli O157:H7 Q7DBM4_ECO57 MSDDISLAMEGALAVIAVVGVYCLVVFLMDRLGN +Q7DC32 unreviewed Escherichia coli O157:H7 Q7DC32_ECO57 MNDISSDDIFLLKQRLAEQEALIHALQEKLSNREREIDHLQAQLDKLRRMNFGSRSEKVSRRIAQMEADLNRLQKESDTLTGRVYDPAVQRPLRQTRTRKPFPESLPRDEKRLLPAAPCCPNCGGSLSYLGEDTAEQLELMRSAFRVIRTVREKHACTQCDAIVQAPAPSRPIERGIAGPGLLARVLTSKYAEHTPLYRQSEIYGRQGVELRRSLLSGWVDACCRLLSPLEEALHGYVMTDGKLHADDTPVQVLLPGNKKTKTGRLWAYVRDDRNAGSALAPAVWFAYSPDRKGIHPQTHLACFSGVLQADAYAGFNELYRNGGITEAACWAHARRKIHDVHVRIPSALTEEALEQIGQLYAIEADIRGMPAEQRLAERQRKTKPLLKSLESWLREKMKTLSRHSELAKAFAYALNQWPALTYYANDGWVEIDNNIAENALRAVSLGRKNFLFFGSDHGGERGALLYSLIGTCKLNDVDPESYLRHVLGVIADWPVNRVSELLPWRIALPAE +Q7DC33 unreviewed Escherichia coli O157:H7 Q7DC33_ECO57 MIPLPSGTKIWLVAGITDMRNGFNGLAAKVQTALKDDPMSGHVFIFRGRSGSQVKLLWSTGDGLCLLTKRLERGRFAWPSARDGKVFLTQAQLAMLLEGIDWRQPKRLLTSLTML +Q7DC34 unreviewed DNA transposition [GO:0006313] Escherichia coli O157:H7 Q7DC34_ECO57 MQKNVTPGRRKGCPNYPPEFKQQLVAASCEPGISISKLALENGINANLLFKWRQQWREGKLLLPSSESPQLLPVTLDAAAEQPESLAEDPETLSISCEVTFRHGTLRFNGNVSEKLLTLLIQELKR +Q7DH26 unreviewed hemolysis by symbiont of host erythrocytes [GO:0019836] Escherichia coli O157:H7 Q7DH26_ECO57 MKKTLLIAASLSFFSASALATPDCVTGKVEYTKYNDDDTFTVKVGDKELFTNRWNLQSLLLSAQITGMTVTIKTNACHNGGGFSEVIFR +Q8LT24 unreviewed modulation of host virulence by virus [GO:0098676] Affertcholeramvirus CTXphi Q8LT24_9VIRU MIKLKFGVFFTVLLSSAYAHGTPQNITDLCAEYHNTQIHTLNDKIFSYTESLAGKREMAIITFKNGATFQVEVPGSQHIDSQKKAIERMKDTLRIAYLTEAKVEKLCVWNNKTPHAIAAISMAN +Q8VLI6 unreviewed Vibrio cholerae Q8VLI6_VIBCL MVKIIFVFFIFLSSFSYANDDKLYRADSRPPDEIKQSGGLMPRGQNEYFDRGTQMNINLYDHARGTQTGFVRHDDGYVSTSISLRSAHLVGQTILSGHSTYYIYVIATAPNMFNVNDVLGAYSPHPDEQEVSALGGIPYSQIYGWYRVHFGVLDEQLHRNRGYRDRYYSNLDIAPAADGYGLAGFPPEHRAWREEPWIHHAPPGCGNAPRSSMSNTCDEKTQSLGVKFLDEYQSKVKRQIFSGYQSDIDTHNRIKDEL +Q8VLW7 unreviewed Staphylococcus aureus Q8VLW7_STAAU MPIWRCNIKKGAIKMNKIFRILTVSLFFFTFLIKNNLAYADVGVINLRNFYANYEPEKLQGVSSGNFSTSHQLEYIDGKYTLYSQFHNEYEAKRLKDHKVDIFGISYSGLCNTKYMYGGITLANQNLDKPRNIPINLWVNGKQNTISTDKVSTQKKEVTAQEIDIKLRKYLQNEYNIYGFNKTKKGQEYGYQSKFNSGFNKGKITFHLNNEPSFTYDLFYTGTGQAESFLKIYNDNKTIDAENFHLDVEISYEKTE +Q8X285 unreviewed viral release from host cell by cytolysis [GO:0044659] Escherichia coli O157:H7 Q8X285_ECO57 MRELKMKLCVLMLPLVVSACASTPTVQAPCVKPPSPPAWIMQPVPDWQKPLNGIISSSENG +Q8X547 unreviewed Escherichia coli O157:H7 Q8X547_ECO57 MRKLYAAILSAAICLAVSGAPAWASEQQATLSAGYLHARTSAPGSDNLNGINVKYRYEFTDTLGLVTSFSYAGDKNRQLTRYSDTRWHEDSVRNRWFSVMAGPSVRVNEWFSAYAMAGVAYSRVSTFSGDYLRVTDNKGKTHDVLTGSDDGRHSNTSLAWGAGVQFNPTESVAIDIAYEGSGSGDWRTDGFIVGVGYKF +Q8X572 unreviewed Escherichia coli O157:H7 Q8X572_ECO57 MTALLTLEEIKAHLRVDHDADDDMLMDKVRQATAVLLAYIQGSRDKVIREDGELIPGEALTRMKGAAMRLTGMLYRNPDLAEREELIQGELPFSVSVLIYDLRCPTVL +Q8X573 unreviewed symbiont-mediated killing of host cell [GO:0001907] Escherichia coli O157:H7 Q8X573_ECO57 MEKITTGVSYTTSAVGTGYWLLQLLDKVSPSQWVAIGVLGSLLFGLLTYLTNLYFKIKEDRRKAARGE +Q8X586 unreviewed DNA transposition [GO:0006313] Escherichia coli O157:H7 Q8X586_ECO57 MTKNTRFSPEVRQRAVRMVLESQGEYDSQWAAICSIAPKIGCTPETLRVWVRQHERDTGSGDGGLTTAERQRLKELERENRELRRSNDILRQASAYFAKAEFDRLWKK +Q8X5D5 unreviewed Escherichia coli O157:H7 Q8X5D5_ECO57 MGRPDWRAMLAGMTSTEYADWHRFYRTHYFQDTQLDMHFSGLTYAVLSLFFCDPDMHPSDFSLLVPRHEEEQVERPDEDKMLMQKAAGLAGGVRFGGDGGRDILSSADVADVMVDDAALMMASAGIPGGVRYVPAGW +Q8X5F4 unreviewed Escherichia coli O157:H7 Q8X5F4_ECO57 MIETLLDFSGLEDISRDLQLLSGAENNRVLREATRAGANVLKEEVVSRAPVRRGKLRRNVVVLSRRSRDGGMESGVHIRGVNPDTGNSDNTMKADNPRNAFYWRFVEMGTVNMPPHPFVRPAFDVRSEQAAQVAIARMNRAIDEVLRR +Q8XAH4 unreviewed Escherichia coli O157:H7 Q8XAH4_ECO57 MWNLLRRTRKNQKSGRDVREVGWRSLFQAVAEPFAGAWQQGVKADPETVLSFHAVFSCISLISQDIAKMRLRLMQTDVQGIRREKRQGDTARLCRRPNAQQNRIQFFELWLNSKLRHGNTVVLKIRTPRGQIKELRILDWNRVEPLVADDGEVFYRITPDRNCGITESVTVPAREVIHDRFNCFFHPLVGLPPVYAAGLAAMQGHHIQANSTYFFRNGGRPSGVIEVPGSITEENAKKLKGNWDSGYTGENAGKTAILSNGAKYSPTTFSPVDAQTVEQLKMTAEIVCSVFRVPAYKIGVGHPPSSDNVEALEQQYYSQCLQTLIESIELLLDEALETGENESTEFDVTTLLRMDSERRMKTLGDAVKNTLLTPNEARKRENLPPLAGGDALYLQQQNYSLEALSRRDAREDPFASAGKTVSSQLPDGASDGNKAISETEHDAVKAMFRGDTEKMTERELSIIRALGEEFSTVLADLQRTFEGKMASQAQAFEEKLTSLSAVLQKHVTVDEVRPVLQAMVDDAVGAIPVPRDGRDYDPDVLQQAVNDAVANIPQPADGKSLTPDDVRPMLEQMVKEAVSHIPVPRDGRDYDPEVLQKAVNDAVANIPQPADGKSLTPDDVRPMLEQMVKEAVSHIPVPRDGRDYDPEVLQKAVNDAVANIPQPADGKSLTPDDVRPMLEQMVKEAVSHIPVPRDGRDYDPDVLQKAVLDAVSALPAPQDGRDATALEILPAIDDQKSFPRGTYATHQGGLWRAYEKTHGMRGWECLVDGVADIDVSMTGERLFSVVVRQSSGQRTEKTFSLPVMLYRGVFRAGETYHPGDTVTWGGSLWHCNSMTEDKPGEAHSSAWTLAAKRGRDAGG +Q8XB51 unreviewed Escherichia coli O157:H7 Q8XB51_ECO57 MFLKTEQFEYNGVSVTLSELSALQRIEHLALLKRRAEQAESSGNLQVSVEDLVRTGAFLVAMSLWHNHPQKTQSPSMNEAVMKIEQEVLTTWPADAIARAEDVVLCLSGMSGAVRPDTDITEVAKNNTLTDDDFSAGKSSTAS +Q8XB54 unreviewed Escherichia coli O157:H7 Q8XB54_ECO57 MGLFTTRQLLGYTEQKVKFRALFLELFFRRTVNFHTEEVMLDKITGKTPVAAYVSPVVEGKVLRHRGGETRVLRPGYVKPKHEFPWSR +Q8XB56 unreviewed Escherichia coli O157:H7 Q8XB56_ECO57 MVTKNITEQRAEVRIFAGNDPAHTATGSSGISSATPALTPLMLDEASGKLVVWDGQKAGSAVGILVLPLEGTETVLTYYKSGTFATEAIRWPESVDEHKKANAFAGSALSHAALP +Q8XBH6 unreviewed ethanolamine catabolic process [GO:0046336] Escherichia coli O157:H7 Q8XBH6_ECO57 MNTRQLLSVGIDIGTTTTQVIFSHLELVNRAAVSQVPRYEFIKREISWQSPVFFTPVDKQGGLKEAELKSLILEQYQAAGIAPESVDSGAIIITGESAKTRNARPAVMALSQSLGDFVVASAGPHLESVIAGHGAGAQTLSEQRLCRVLNIDIGGGTANYALFDAGKISGTACLNVGGRLLETDSQGRVVYAHKPGQMIVDECFGAGTDVRSLTGAQLVQVTRRMAELIVEVIDGTLSPLAQALMQTGLLPAGVTPEIITLSGGVGECYRHQPADPFCFADIGPLLATALHDHPRLREMNVQFPAQTVRATVIGAGAHTLSLSGSTIWLEGVQLPLRNLPVAIPIDETDLVSAWQQALIQLDLDPKTDAYVLALPASLPVRYAAVLTVINALVDFVARFPNPHPLLVVAGQDFGKALGMLLRPQLQQLPLAVIDEVIVRAGDYIDIGTPLFGGSVVPVTVKSLAFPS +Q8XC86 unreviewed Escherichia coli O157:H7 Q8XC86_ECO57 MNTIDNTQVTMVNSASESTTGASSAVAASALSIDSSLLTDGKVDICKLMLEIQKLLGKMVTLLQDYQQKQLAQSYQIQQAVFESQNKAIEEKKAAATAALVGGIISSALGILGSFAAMNNAAKGAGEIAEKASSASSKAAGAASEVANKALVKATESVADVAEEASSAMQKAMATTTKAASRASGVADDVAKASDFAEDLADAAEKTSRINKLLNSVDKLTNTTAFVAVTSLAEGTKTLPTTISESVKSTHEVNEQRAKSLENFQQGNLELYKQDVRRTQDDITTRLRDITSAVRDLLEVQNRMGQSGRLAG +Q8XCB6 unreviewed transmembrane transport [GO:0055085] Escherichia coli O157:H7 Q8XCB6_ECO57 MKKLTVAALAVTTLLSGSAFAHEAGEFFMRAGSATVRPTEGAGGTLGSLGGFSVTNNTQLGLTFTYMATDNIGVELLAATPFRHKIGTRATGDIATVHHLPPTLMAQWYFGDASSKFRPYVGAGINYTTFFDNGFNDHGKEAGLSDLSLKDSWGAAGQVGVDYLINRDWLVNMSVWYMDIDTTAKYKSGVTTVKDSVRLDPWVFMFSAGYRF +Q8XD17 unreviewed Escherichia coli O157:H7 Q8XD17_ECO57 MAKIRYLQGTHDARAGDIRDVAQPCAEVLVRLGKAEYITARRPAGQKKKRDAEHGECGTFCGEPEKTRNQDVT +Q8XD18 unreviewed proteolysis [GO:0006508] Escherichia coli O157:H7 Q8XD18_ECO57 MTLKRACSLLTVKSFSEDERVITGIASTPSPDRDGDILEPEGAEFGSAIPFLWQHDHSRPVGQCTVRRVSEGLEITATLVKPVPDMPSQLAARLDEVWAAIKTGLVRGLSVGFRPHEYTFLDGGGLHFLRWELMEVSAVTVPANAECTIRTIKSYDRPFSAASGNRKPVVKIASSAGAAAQSTTVFHKEKTIMNIGEQIKSFENKRAALAASLEEVMTKAAEEGRTLDVEEEEHYDNTAAEIRQVDAHLKRLRELEAGKAATAQPVKQAGNGNVAAVASAPVIRVEQKLDKGIGFARFAKSLAAAKGVRSEALEVARRQYPDDSRLHHVLKSAVGAGTTTDPQWAGSLSEYQEYAQDFIDYLRPQTIIGRFGQGGIPALRQVPFNIRVHAQVSGGAAGWVGEGKAKPLTKFDFESITFSHAKVSAIAVLTEELIRFSSPAADALVRNALAEAVVARLDTDFVDPKKAAVADVSPASITHDVKGTASTGNPDADAEAAFGQFVAANLQPTGAVWLMSSTNALALSMRKNALGQKEYPDMTLLGGSFQGLPVIVSQYVGDQLVLVNAPDIYLADDGGVAVDMSREASLEMQSEPGGDSTTPSPVELVSMFQTGSVAIRAERWINWRRRRTAAVAVITGVNYGSASGG +Q8XD34 unreviewed Escherichia coli O157:H7 Q8XD34_ECO57 MSSKNRPRRTTTRNIRFPNQMIEQINIALDQKGSENFSAWVIESCRRELAADIKYARQLTIKKNDTQYALRWLFI +Q938P4 unreviewed Streptococcus dysgalactiae subsp. equisimilis (Streptococcus equisimilis) Q938P4_STREQ GLTTSQEVFAQQDPNPSQLHRSSLVKNLQNIYFLYEGDPVVHENVKSVDQLLSHDLIYNVSGLNYDKLKTELKNREMSTLFKNKNVDIYGVEYYYHCYLCRNAKRRACIYGGVTNHEGNHLEIPKNILVKVSIDGIQSLSFDIETSKKMVTAQELDYKVRKHLTDNNQLYTNGPSKYETGYIKFISKDKETFWFDFFPEPEFNQVKYLMIYKDNETLDSSTS +Q9FAR6 unreviewed proteolysis [GO:0006508] Clostridium butyricum Q9FAR6_CLOBU MLYMPTINSFNYNDPVNNRTILYIKPGGCQQFYKSFNIMKNIWIIPERNVIGTIPQDFLPPTSLKNGDSSYYDPNYLQSDQEKDKFLKIVTKIFNRINDNLSGRILLEELSKANPYLGNDNTPDGDFIINDASAVPIQFSNGSQSILLPNVIIMGAEPDLFETNSSNISLRNNYMPSNHGFGSIAIVTFSPEYSFRFKDNSMNEFIQDPALTLMHELIHSLHGLYGAKGITTKYTITQKQNPLITNIRGTNIEEFLTFGGTDLNIITSAQSNDIYTNLLADYKKIASKLSKVQVSNPLLNPYKDVFEAKYGLDKDASGIYSVNINKFNDIFKKLYSFTEFDLATKFQVKCRQTYIGQYKYFKLSNLLNDSIYNISEGYNINNLKVNFRGQNANLNPRIITPITGRGLVKKIIRFCKNIVSVKGIRKSICIEINNGELFFVASENSYNDDNINTPKEIDDTVTSNNNYENDLDQVILNFNSESAPGLSDEKLNLTIQNDAYIPKYDSNGTSDIEQHDVNELNVFFYLDAQKVPEGENNVNLTSSIDTALLEQPKIYTFFSSEFINNVNKPVQAALFVGWIQQVLVDFTTEANQKSTVDKIADISIVVPYIGLALNIGNEAQKGNFKDALELLGAGILLEFEPELLIPTILVFTIKSFLGSSDNKNKVIKAINNALKERDEKWKEVYSFIVSNWMTKINTQFNKRKEQMYQALQNQVNALKAIIESKYNSYTLEEKNELTNKYDIEQIENELNQKVSIAMNNIDRFLTESSISYLMKLINEVKINKLREYDENVKTYLLDYIIKHGSILGESQQELNSMVIDTLNNSIPFKLSSYTDDKILISYFNKFFKRIKSSSVLNMRYKNDKYVDTSGYDSNININGDVYKYPTNKNQFGIYNDKLSEVNISQNDYIIYDNKYKNFSISFWVRIPNYDNKIVNVNNEYTIINCMRDNNSGWKVSLNHNEIIWTLQDNSGINQKLAFNYGNANGISDYINKWIFVTITNDRLGDSKLYINGNLIDKKSILNLGNIHVSDNILFKIVNCSYTRYIGIRYFNIFDKELDETEIQTLYNNEPNANILKDFWGNYLLYDKEYYLLNVLKPNNFINRRTDSTLSINNIRSTILLANRLYSGIKVKIQRVNNSSTNDNLVRKNDQVYINFVASKTHLLPLYADTATTNKEKTIKISSSGNRFNQVVVMNSVGNNCTMNFKNNNGNNIGLLGFKADTVVASTWYYTHMRDNTNSNGFFWNFISEEHGWQEK +Q9KX93 unreviewed pyrimidine nucleobase catabolic process [GO:0006208] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KX93_BPVT2 LCVNTLSAGQKPLSNLFGGKTPMEHRFAAARWQNGVTGCPQLEEALVSFDCRISQVVSVGTHDILFCAIEAIHRHATPYGLVWFDRSYHALMRPAC +Q9KX94 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KX94_BPVT2 MAMFGLPHWQLKSTSTESGVVAPDERLPFAQTAVMGVQHAVAMFGATVLMPILMGLDPNLSILMSGIGTLLFFFITGGRVPSYLGSSAAFVGVVIAATGFNGQGINPNISIALGGIIACGLVYTVIGLVVMKIGTRWIERLMPPVVTGAVVMAIGLNLAPIAVKSVSASGFDSWMAVMTVLCIGLVAVFTRGMIQRLLILVGLIVACLLYGVMTNVLGLGKAVDFTLVSHAAWFGLPHFSTPAFNGQAMMLIAPVAVILVAENLGHLKAVAGMTGRNMDPYMGRAFVGDGLATMLSGSVGSSGVTTYAENIGVMAVTKVYSTLVFVAAAVIAMLLGFSPKFGALIHTIPAAVIGGASIVVFGLIAVAGARIWVQNRVDLSQNGNLIMVAVTLVLGAGDFALTLGGFTLGGIGTATFGAILLNALLSRRLVDVPPPEVVHQEP +Q9KX95 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KX95_BPVT2 MAAAIRRVNSSNMMSIPRRRSVMANHRGGSGNFAEDRERASEAGKKGGKSSHGKSDN +Q9KX96 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KX96_BPVT2 MTSQLIPVFNGTIANETTLLVNARDLHTFLGVGKRFASWITERIEEYGFVENQDYIAISQKREIGYGRGKKDYHLTLDTAKETAMVERNEKGRQIRRYFIECEKKLRNMQSAQAEPQQQFTDEEIILLCYMQLWMEKAQDLSKHLYPIMKELNSSYTNKLYDIAFETIYMVTKNRDVLLREAARLDQSSFVVQRARPMLKSLRARQFEF +Q9KX97 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KX97_BPVT2 MADIIDNAAEIEELQRNLSLQKYKSDSNAPSATHCCECGDPIDERRRLAVRGCRACASYQQDIELINKQRGVK +Q9KX98 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KX98_BPVT2 MNINTTITIDTALNTGLALLGYFYIMFCSGRWLSLLFMKKWNKRRKQEQRQKAIDAFFEAFGIDGMDPGDPARAISRGGVVILVYRSEEKNEQD +Q9KX99 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KX99_BPVT2 MSKIDYQALREKAEKATKGSYIVGHTSVNQHGNLTGVFVCQKWKGEPGGVIAECHVNCLVETDAQAYATLNS +Q9KXA0 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXA0_BPVT2 MLELLDERERNQQYIKRRDQENEEIALTVGKLRVELGAAENNLIDSECHVAELEEALRDKQALLEASEKRIAELEAELVSQTYKLPHTQFEQIANLYEMQFDDGRTCAFHTDAQKAEQWLQACDGNRVQEYVKLERLQDALSGNSPVTPDGWISCSERMPDTKTAVLVAVEFDRKGDWRMKWATYIPGHPDANDGWIIPGASWKPSHWMPLPEPPQEVN +Q9KXA1 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXA1_BPVT2 MENEGDNIITLVQPKRDEEKLLNITVTGRKNYTQQSCKHRAIEVHEQDHVILCLQCGCVVDPFQYVLRCANDGEAVVREIRQLHNRHDQLRESAAASSVKKRTPKHGCGQQELQYCMRKMTLKILSRR +Q9KXA2 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXA2_BPVT2 MKQQKAMLIALIVICLTVIVTALVTRKDLCEVRIRTGQTEVAVFVDYESEK +Q9KXA3 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXA3_BPVT2 MLNTCRLASYVPKGKEKQAMKQQKAMLIALIVICLTVIVTALVTRKDLCEVRIRTGQTEVAVFVDYESEK +Q9KXA4 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXA4_BPVT2 MKKVLIAALISGVSFGAFAQQGGFQGPEAERSTVAQAKELKDDAWVILEGSIVKKVGDERYEFRDNSGTIVTDIDDSIWAGQNVSPKDKVRIEGEIDKDLSSVEVDVKALKLLK +Q9KXA5 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXA5_BPVT2 MMFTPYRRGTIPAIRIADGTIQAHDDIDEEFFQPVLDGFLISKYTPFDILHALKDGVLQRTG +Q9KXA6 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXA6_BPVT2 MPGHCAGFFYGVCMAYSEEQRPEAQLGNQNRNSLNIQQPGETDSYEAFFSDPNRWKDNSTSFSLGDVLPTMGKGFAQSVRGTGEMARGLGDAMIQSPVKTGARILNEFSRMGLPGVATVQDIFAGGSRGADEVIDTLPDGKNAVTDTVGKGLKATGKAVSDGAKATDEWLTGKMSPGAVRALNTPMTEGYNDSAVWVAKGVNLIGALVPDMVAGGVARKVGDVTLRKMLTAGLEKKYIAAGMQPERATALAAEAVDKKMPDLFQAGLITHSTVSAQGQSAMAAADAVLNADYSELAQSPKFQQTFLSIDADPQHAQLTDRQKMDLAKERVADEVRAQLATDPELLAVNAMAAKLGDAQLFNLVTRGTAKTVKSGIVRNATEQGAINAAQGGYSRYQENTALRETAGMGVSPWEGVADATIEGAAFGAAMGAPFGAVAGYRGRRQAAEETAMRDAETVQQDDAAPQPESVDPVAQQRESMQGMNREQLLEQYADADMATEGDASAAHRREAASQLLNELDEQTKRQAVMNELKAKPRSELLEEYRRLSQKEGRTETEEQQFQAIREVIRPQQEVTPEAQSQPENAEDGNGSIYPTVRFRDPNEVRIEINGNGASRPAERIEKVRPDNRYFTDEKSAMGSDVFRNAAATGLKPSVVKKGENQYAVEMDNPAFSEDVATETINTLADGERIADADPMEQPAFMRDPRFRGFTGDDTEVQARLARGNAPTAEELVRSQMAEGDAGPTAQELTERPRLPAPGDIHPGQGYPLPGEVARTPDENQAGRGGRFTTTGEVKGQSFQKGQAPAPENAAGRQGETLEGDMVRRGLPSPDAQNATAPVREGLPAPDIARNVRMPQPESLPRTVRDSLPELAQQAEVRRQAGGNRDIPQPETIAPESETTVSTDREATVRGGEVRGKKIEDFGEEIKGAAKHRYAQLAETLGKTLEDRDYATQPLSKLFPKPDYAKLANEGADADTLAMIALYRSDIPAKTKHNTAGWGESIKKVRHSVSEMLNGTVSAKRLAEWMEGRMPSRYADTWQLLRTLPPSQMDRASAYRVVSGVYQAAGGKRYDPPQKLYSLRNKDNKGSNLFFSESRDELLAKAKVWFAEQEEKSQAKGDEKTAPSPDDKIRFDVYRNTRSGDIFIAYGKNKMRVRGGFKSASDARKYIDSHRDELVRHVKEMREISREEQRNATNRDRTGPERRKGNVSPEQFSDAFGFRGVQFGNYVEGPRRQADLNRAYDSLHDLAEVLNVPTKALSLNGRLGLAFGARGKGKAAAHYESGEVAINLTKGNGPGALAHEWFHSLDNYFGRYDVSNDGKITSGGDFMTEAQRVRRIFKDGRYVDAEYPVRQEVYDAFKGVIQAIKNSDMPRRSALLDEVRSKPYWSTDVEMAARAFERYVQDKARMAGVENDYLVNIRKAPEHNTDNTYAYPTNAELDGGIREAFDHLFRTLKTRETDKGVAFYSRKGVTRTPEGNLISDVNRSAEAKGSPVPQVEAVARGVMSGIKGSDLKVRVVKSQKEAEALAGELFDGYGRVHAFYRPDKREIVLVADNIPDGRTVREKLRHEIIHHAMEHVVTPAEYQTIIKTVLKTRDSDNVTIREAWRKVDASYGKESPEVQAGEFLAHMAEKQPNKFVAAWERVVALVKGVLRRTGLLKPTELNDIRLVRETIRTLGQRVREGYTPREDGAGASFQYSRSGKRDPFKVPEGEGERYRDDLARMMKSLRTTDLTVNIGRTPPVLRHLGAPDLPLVISRDTVRKATNGVKHVVPMDVIERLPELMHDPDAIYRSATERNAVVMLLDAVDKNGDPVVSAVHMKAVRSRLEINKVASVYGTENGKKLKSMEMTGLTLYRREKLSRDNLLHRGLQLPKGEHSYRGSADKILYPEDIRKGPYYSRTSSLTPEETIASRFVRQMQDKFQVLKAVQENIRKTGGKVDDSNNAYMAEELFHGKAENDLNVMKERYVQPLAKLLADYKIAQADLDEYLYARHAPERNAHIAKINPKMPDGGSGMTNAEAAEIMQRVRNSGKQAQYDRLAGIIDDMLARRRELIREAGLEENGVVDAWQNAYRYYVPLKGQDVDGVVSLPRTGKGFTIGGRESRQAMGRASRAQSPSTQAIQDLSESLIRHRKNEVGNAFLKLVQDNPDKDYWQVFTDDRPDTMRTIAERKDQETGETIREVVERPVPMAMMADRYFTTKKNGKTYYIKLHDPRLMRAMKSMGPETSNAFVRTLGKVNRFLATVNTSYNPEFLVSNFIRDVQTAVMNLKAEQGRSDGKLKGLDNLSALAVVKDSRSAMSAVYASLRGKTLTGKGAQWQKVWKEFVEDGGKTGWFNMGDLEGQQKEMDRLVSLAKGGWKGQSIGAWNSFLNLVEDANGAVENALRLSAYKHARDAGLSRQQAASLAKNMTVNFNRRGEQGALMNSLYMFANASIQGTANLVRTLGHLNGEGPLLERLRWKNLNVPQKIALAAVGAGYLLGSLNRSVAGEDDDGVNWYDKVPSHVKERNLVIMKSVFGGKAGEYWSIPLPYGYNVFFLLGHTAEGVAAGDLTASRAAGNVVGGVLGAFSPIGSETSETLSGALLKNAAPTILRPFANLAMNENFMGAQIYQENMPFGTPKPDSQLGRRSTPEAYKAFASWLNAFSGGSQYRPGAVDITPESLKFWIDYISGGTGRFISKTTDAAVKSLNGIDIPEQQVPFLGKISGEVMPYADQQKMYDRMTEIAQYHAELKSLTGAERTAFIDENNGKLSMNGLMQDTRKRLKDLRKQRDAIYADSTLSLAQQSAMVKSVERDMKIAVDRFNREYNKKVGVD +Q9KXA7 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXA7_BPVT2 MSGFAQGLLAGFSTVDQAMTRRKELGLREAQLARQQKNNERDFEFAQSQFEHNKNVDQRNFDYRAKVDDRNYALKEREFNANQNYRNASLGMEQQRLQLQKYNQRRLEYNDMIAHSQPLMEALGKAIEAGDQEAATRLFGQLPKGHPLILMSNEGYAAKAGQAVINLQKIFGDKPDMAIDSLNTPENLDVLSGVFAPELQQRIGMPDSTGDKTIKEARIGSIVPAQQEGYVLIGLDLTYSDGSTAHKPVTEYGSAHPDDQTVLAIPVDKAIAQVRDRSKFAEISKNYGYFMPKQQGLSLKELQKGASNVAADAIKNGGNAQAAVDEYYAATGSQPHQQKIQQQKLQQQVINWAGDDPDKLSFARNVAARQPEMLEPQNQKLLENGYANFLRIQKARGEQARDESASSASQFIRGLKQNYAQ +Q9KXA8 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXA8_BPVT2 MGYGLLDIANQSRREALQGISDADRRREEIEAANKQMAAQQKAQNKQNIGTGIGTGAAIGASVGGPVGAVAGAVIGGIAGSLF +Q9KXA9 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXA9_BPVT2 MEYGKYETLARAGYSGADRPQGDWQTSAALTRQQYDDWRTRYLPRVARLADLGENNSLMNAQLARVGGLATSSLRTAQMAQDNQMARYGVNRPDNPDSNTLGLRNALAIAGAKNGIREAEQDRQMNILTGASAPARQKLSVGGQLVAA +Q9KXB0 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXB0_BPVT2 MGGSKGGGDTKVKPTAAQIAQEEVAWKGWQDYKNILRPAEDNFMEKVDDLNSEQQYDNIAGTTNLGYQKQFGEARKELAGNLAQSGVDPSSGRFNAVMNANQSDQVTGQIDTTTRGQVSQADKYVAGLQDVAALGSGQKADALQSFNSLADSSLAKAKSDAQAAFTKQQGRASLVGAGLGAAGAYAMHKAGGSGGSGGAKTPGTGANAIQHQAQNWRL +Q9KXB1 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXB1_BPVT2 MAPVSGERMNDKILWYMQRVVRNSRNPEFMNEVKDACLKKQAFCFEAPDGFLVLRSVLSADGIPYVLVLLGVCTGSNSVERYLPEVKTLTHLAGGRWAEFHTARRGFIRLGKRLGFERMPDDEDGFMVFRIAV +Q9KXB2 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXB2_BPVT2 MKSIATLVVCAISGIACVNLSAHAAEGEHTISLGYAHFQFPGLKDFVKDATAHNRETFSHFVNRNYFSSLGEYTDGRVSGYEGKDKNPQGINIRYRYEITDDFGVITSFTWTRSLTNSQTFIDVQSADHTRKIKNPAASARTDIRANYWSLLAGPSWRVNQYMSLYAMAGMGVAKVSADLKIKDNINSSGGFSESNSTKKTSLAWAAGAQFNLNESVTLDVAYEGSGSGDWRTSGVTAGIGLKF +Q9KXB3 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXB3_BPVT2 MYGLSIMKPDGSVWISPGFTPQCLINKGTIPATEKSFFKTSIPSGKSCFFFIRTEKKADVMYTHEQIDGYHALRLHVIVRGTNPGVTTVYAFANMVTPPSEYGIAMYNPDGEMIYHGEMMLLDAKLIPVDIKFEKDLGYPCAIMPALVGYYNWKRTPYDRPIYTTSTGATGNKIYSCEHYSGGATWDIRKPYIDKVLVINTSVYD +Q9KXB4 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXB4_BPVT2 MLTPVFFLDRFTAESGSKTYTNKPDGKSLQAVCCLFPWNNVFADRKVPKITINDNTVTWSNLEQGMGSYIYTFWG +Q9KXB5 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXB5_BPVT2 MTRKPWRAGKDLSTVVENMEIGTGQRGDGRHAFVTREELVGLKLARRRTSGGASYALNPGIEIDSTLMTVDFPTKPLNFKATGGFGSVLLEWDMPNYRGHSLTEIWRGTEDDLADAVLVATTPGQVYGDPVDPGWSGFYWIRFVNAAGVKGPWNAEKGTQAQTQIGVKAIIDQIRDEAAKSPVVSELRKEIKNAQGQAVKDAAIKTTEVVGTLREETTRTIGGIETRISTLDSSTSESLNEVDKRITKLDKEGGEAFLAMWSKKAGVDGITAGIGIVAGKDSEGRPVSQVAISASQLFVFDPNNPDNTAYPFAVSGGKVVIPKAMIYDAVIETLVSRKVVADEVKAGVSITSPVIRSAVIQNGNFQVDSQGNLNIGGLFSVTSQGQLTIRYSNQNVGLVIRNDKIEVYDQNGRLAVRIGRLR +Q9KXB6 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXB6_BPVT2 MMPIAILANSIINPLIFKPEAVKGISMPYIDITTMRGMMPRVVTSMLPEHSAVLAEDCHFRFGVITPERQISGVEKTFTIKPKTIFHYRDDFWFAWPDVVDVIRSPIAQDPHGRIYYTDGRFPKVTDATIATKGDGNHPTSSYRLGIPAPTTAPVCTVQQGGDVSDDNPNDDETRFYTETFVSDYGEEGPPGPASLEVTLRTPGTAVQLTLAPVPLQNASIKRRRIYRSASGGGEADFLLVAELDASVLSYTDKIPAKNLGPSLATWDYLPPPENMTGLCLMANGIAAGFAGNEVMFSEAYLPYAWPEVNRHTTAEDIVAICPLGTSLVVATKGEPYLFSGVSPSTISGSKIPSMQACLSRRSMVAMEGFVLYAGTNGLVSVDANGNVALATEQIVSPEQWQSQFNPASIVAYPWRGEYIACYTKPDGKQDVFVFSPVNMDIRYLSTPFDCAWVDLAKDMMRVVTGDKMSVLAGGALPSTIRWHSKIFSLPERTSFSCIRVKSPAPERVGITIMADDVPVIHFAPGTFKGSVVRLPAATGQNWQVMVSGFGQVERITLSTSMSEMPV +Q9KXB7 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXB7_BPVT2 MYRGGLLESEVINMLIRWSEGCRVILVQEFFMPENRRIILDSKESWLIICDSQLGHLMRSMYQGRRFIQLNLEKLKGVHDVALPVKWEFTRRQ +Q9KXB8 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXB8_BPVT2 MPHGFRVFFSICRSGLVTGRGRMSRGWMKWAVIQAEQENDMNILRKLMQSLCGCGKHDDCENGQSLTAQLRLGPADILESDENGIIPEQDRVITQVVILDADKKQIQCVVRPLQILRADGTWENIGGMK +Q9KXB9 unreviewed symbiont entry into host cell [GO:0046718]; virion attachment to host cell [GO:0019062] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXB9_BPVT2 MSVVVSGTLKSPDGEAISGANITLTALTVSPDALSGTSASAVTREGGYYGMTMDPGEYAVSVTVKGKTAVYGRVRIEGTESTVTLNMLLRRSLVEVSIPGELLTDFRQIQNNVADDLATIRRLNEDTATKNTQATQSKESAAASAKSASDSAKTATSRAAEAGQKATDATEAATRAVTAAGNAEESSTRAGESEKAAGADAEKARQHAEKARLAQESAGEILKRAEAATVSAEEARRMAENARGPRGPQGETGPKGDVGPKGETGPVGPQGPAGPKGERGDVGAQGAVGPAGPRGEKGEQGERGPQGIPGLKGDTGERGPKGDQGDMGPKGEKGDPGGPAGPQGPKGERGEAGPQGPMGARGERGETGPRGEPGPAGPRGERGETGPQGPRGEPGPAGSAANVADATTAQKGIVQLSSATDSDDETKAATPKAVKAAMDVANEAKTKAEEAAAGGGVPGPKGDKGDTGPAGPAGPKGDKGERGDTGPVGATGERGPAGDAGPAGPQGPKGDRGERGETGLTGNAGPQGPKGNTGAAGPAGPQGPKGETGAAGPVGATGPQGPKGDPGETQIRFRLGPGNIIETNSHGWFPDTDGALITGLTFLDPKDATRVQGFFQHLQVRFGDGPWQDVKGLDEVGSDTGRTGE +Q9KXC0 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXC0_BPVT2 MTTITEIIGRVNTQLVDPMMVRWPLQELCDYYNDAVRAVILARPDAGASLETISCVPGARQVLPDGVIQLLDVICLSDGSAVRPLSREVLDAQYPEWPTMKGIPECFISNDLSPRVFWLFPAPDKEISIDAVVSRIPEAVYVLTQDDDTPVPLEEAYVNPLVEWMLFRAFSKDAAGGAESGLAAQHYQSFVEQLGIKQGADSALYARKKVFNGGGV +Q9KXC1 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXC1_BPVT2 MRQKSWIFTKTRKKRLRHSVSGCVRHFAVVLRRLNSMAELSDFLPYVRRHISGPLNIMMTDALSMAAVAFSRQSLVCRREVTVVPVAGKEIVLPYDKDDEECVHIIRISDDNHELFVGRDVDISSGRSLRFACSPGEVSVLYAVAPKAGRSQIPDELLTWPEEVAAGALERLFMQTGVSWSDPLRAQYFSVQFSEGIRRAYRHTLATSPYSSYRNPVRRQRFF +Q9KXC2 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXC2_BPVT2 MRAFYPGNYMSEKIAVVYIGPKPVKKDTITGSRTLFPRLEPVHVDSAMAWQLLGFPDVWVRHEELDDVLKKQQQNEQLRQAQQAQERVLAALAEAENSFVVSVNGQEVDLSKLTSARLATLCEAEELDIHKDPKETAEAFRIRVREAFRRRVAETEQHGGTE +Q9KXC3 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXC3_BPVT2 MAKTILAPSLSERVYTGTHGNESVAEGVFTVNAAEADSVIHLLSLPVGIRINSLQLVSTGGLGTATVSIKSGEHALIDNSEAVSAKFARYVPVEPYTTQRDGELVTVTIKTAAATGTLNVLLRYTVVGY +Q9KXC4 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXC4_BPVT2 MTTVTSAQANKLYQVALFTAANRNRSMVNILTEQQEAPKAVSPDKKSTKQTSAGAPVVRITDLNKQAGDEVTFSIMHKLSKRPTMGDERVEGRGEDLSHADFSLKINQGRHLVDAGGRMSQQRTKFNLASSARTLLGTYFNDLQDQCAIVHLAGARGDFVADDTILPTAEHPEFKKIMINDVLPPTHDRHFFGGDATSFEQIEAADIFSIGLVDNLSLFIDEMAHPLQPVRLSGDELHGEDPYYVLYVTPRQWNDWYTSTSGKDWNQMMVRAVNRAKGFNHPLFKGECAMWRNILVRKYAGMPIRFYQGSKVLVSENNLTATTKEVAAATNIDRAMLLGAQALANAYGQKAGGHFNMVEKKTDMDNRTEIAISWINGLKKIRFPEKSGKMQDHGVIAVDTAVKL +Q9KXC5 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXC5_BPVT2 MDFEFTGEETPEQLEKMLEGLGDVDIDSHAQDVVTEDTTEKHADEEAQTQTGDNNVAPTPDASVEQTQDVKEPEAKGVLTRDGKHVIPYEVLEAERSGKQRAEQEAALLRGQIAEEKRRVELLTSQIHQAGMKPTPLPENEKISDEQIARIREMYPEIGDAVASLIRKNNYLQSRVQQSAQQAEGNGGEDLSPVLDAMNAVPVLKTWQESDPDRFSVAVSIDGKLQNDPAWKDKTLTERFAEVARRTQVAFGEVSESSADNKADKTDIRKTAEEKVKTAEQEQAVPASPSDLGTTASVGTGDNFERLLGASHSEAEAIMRGMTNAEIDALLEKLG +Q9KXC6 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXC6_BPVT2 MKNETNTMATKNDNGATPRFSQRQLQALCSDIDSQPKWRDAANKACAYYDGDQLPPEVLQVLKDRGQPMTIHNLIAPTVDGVLGMEAKTRTDLVVMSDEPDDETEKLAEAINAEFADACRLGNMNKARSDAYAEQIKAGLSWVEVRRNSDPFGPEFKVSTVSRNEVFWDWLSREADLSDCRWLMRRRWMDTDEAKATFPGMAQVIDYAIDDWRGFVDTTVTEGQPSPLMSAWEEYQSWDRQQNEWLQRERRRVLLQVVYYRTFERLPVIELSNGRVVAFDKNNLMQAVAVASGRVQVKVGRVSRIREAWFVGPHFIVDRPCSAPQGMFPLVPFWGYRKDKTGEPYGLISRAIPAQDEVNFRRIKLTWLLQAKRVIMDEDATQLSDNDLMEQIERPDGIIKLNPVRKNQKSVADVFRVEQDFQVASQQFQVMQESEKLIQDTMGVYSAFLGQDSGATSGVAISNLVEQGATTLAEINDNYQFACQQVGRLLLAYLLDDLKKRRNHAVVINRDDRQRRQTIVLNAEGDNGELTNDISRLNTHIALAPVQQTPAFKAQLAQRMSEVIQGLPPQVQAVVLDLWVNLLDVPQKQEFVERIRAALGTPKSPDEMTPEEQEVAAQQQALQQQQAELQMREMAGRVAKLEADAARAHAAAQRDNASAQREVALTQGQRYVDALNQAHTAEIITGVQNMEQEQDVLQQQMLYTLQQRMNEMSL +Q9KXC7 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXC7_BPVT2 MYNNAFVGPERNNHGHAVILKLRELYPTRYIYNEQHLDQAYDDDTPRLGWLTTRQSKPVLTEGMKTLLNNGISGIRWSGTLSEMNTYVYDAKGSMNAQEGCFDDQLMSYMIAQEMRARMPVRVKQKTDKRRTTHWMAH +Q9KXC8 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXC8_BPVT2 MTFRKNEPRCDEPSEMTEAEQRLFIMTKLSNPWWRLNHLYKIQNEKGELVTFRMRPAQRQLFRSMHNKNIILKARQLGFSTAIDIYLLDQALFIPHLKCGIVAQDKQAASEIFRTKIAVPFDHLPDWLRASFTIVERRSGASGGYILFGHGSSIQVATSFRSGTVQRLHISEHGKICAKYPAKAKELRTGTLNAVSDECIIFDESTAEGVGGDFYEMSNRAQEITASGLLLTAQDYKFHFYAWWQDPKYSARVPESGLKLSREKMTYFSAVEKAMNITLTDEQKQWYINKETEQREEMKQEFPSTPQEAFLTSGRRVFSAESTLQAESFCSPPMIVYDIEPVTGAKTKAQSLREGNKNELQRTLMNYLLVWELPDPDEEYVCGADTAEGWSTETAHRWMLSNAVMASRWLTGSGISMLNFLLISFRRSVVCITTRLWGRSVIITDMQLS +Q9KXC9 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXC9_BPVT2 MAKLDWKKLEQAFRREHAETGITLLDWCRKKKINYNTARTRIKMGKIDHEIDHKTDHEIDHDISDEEPCNDAGSGDEKCAKNSEKNCANSAETKRIRGSRLLPPSNAFSQRNTHAVRHRGYAKYLEADNLMDDASDMVLFDELVFTRARALSVTKALKGMFADLEEATDVETRVALYDKILKAEQALDRNIARIESIERSLLTLDVLAETAPKLRADRERINAARDKLRAETDILTNQRRGVVTPVSDIVSSLHEMSNSGRLDDIPEE +Q9KXD0 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXD0_BPVT2 MKKMLLATALALLITGCAQQTFTVQNKQTAVAPKETITHHFFVSGIGQKKTVDAAKICGGTENVVKTETQQTFVNGLLGFITLGIYTPLEARVYCSQ +Q9KXD1 unreviewed viral release from host cell by cytolysis [GO:0044659] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXD1_BPVT2 MRELKMKLCVLMLPLVVSACGSTPPAPVPCVKPPAPPAWIMQPAPDWQTPLNGIISSSERG +Q9KXD3 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXD3_BPVT2 MNMMAVPFHGNSLYVVNHNGEPYVPMKPVVAGMGLAWQSQLAKLRQRFASTITEIVMVAEDGKQRNMVSMPLRKLAGWLQTINPNKVKPEIRDKVIRYQEECDDVLYEYWTKGFVVNPRKMSVMEELNQACADMKRDKNIASVFATGLNEWKQVKAAHVSKIRTLVNEANMLIDFVLADTGKGKITKAD +Q9KXD4 unreviewed symbiont-mediated killing of host cell [GO:0001907] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXD4_BPVT2 MYQMEKITTGVSYTTSAVGTGYWFLQLLDRVSPSQWAAIGVLGSLLFGLLTYLTNLYFKIKEDRRKAARGE +Q9KXD5 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXD5_BPVT2 MSEITSLVTAEAVKDVLRSEEVRSALKQKLRHNLEARLDAEVDAILDELLGVQAEPPTEAGDTTAESGEVQPESPVADATEPQPESVMML +Q9KXD6 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXD6_BPVT2 MTFLNQLMLYFCTVVCVLYLLSGGYRAMRDFWRRQIDKRAAEKISASQSAGSKPEEPLI +Q9KXD7 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXD7_BPVT2 MIKKPPATGMDAGGGDTYGEKIKERYFRTLVFNENSSYCQQ +Q9KXD8 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXD8_BPVT2 MSRLNHPKADLSKGQYGTVGQGLHIAKKLLPFIPANAGILLVPCCRGGSAFTTGADGTYSDASGASENSTRWGVDKPLYKDLIGRTKAALKKNPKNVLFAVVWMQGEFDFGGTPVNHAAQFGALVDKFRADLADMAGQCVGGSAGGVPWICGDTTYFWKQKNESTYQTVYGSYKNKTEKNIHFVPFMTDENGVNVPTNKPEEDPDIPGIGYYGSKWRDSSATWTSQDRASHFSSWARRGIISDRLATAILRHAGRVALNAGASSTVSEVRPSSPSGAEATGVTTLLSYLASESEGSLKVQGWSASGGRAEVVSDAEGTGGKAVKLTKEAGKSSWVLEYAAGNGAALLQKGGQIRCRFKVSGALAANQYVMAFYWPVSSLPQGVALTGDGGNNLLAAFYIQTDAKDLNVMYHNAKVNRPGFPGECFICELRLPDHRFRWKHNKLFLLLPEEYGPAFPAIVDCYTSPPTLVWPVPLLHGFSSSYGVLPPLCKDH +Q9KXD9 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXD9_BPVT2 MAFKHYDVVRAASPSDLAKRITQKLKEGWQPYGSALISTAGYGAEFIQPVVSEGSISSPEEPGNRPTTSAPSVAPEYYYVIALAGQSNGMSYGEGLPLPDTFDSPDPRIKQLARRSTVTPGRCSMQI +Q9KXE0 unreviewed negative regulation of termination of DNA-templated transcription [GO:0060567] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXE0_BPVT2 MFLWFTADGVKILMRDIRQVLERWGAWAANNYEDVTWSPIAAGFKGLIPEKVKSRPQCCDDDAMVICGCIARLYRNNRDLHDLLVDYYVLGETFMALARKHGCSDTCIGKRLHKAEGIVEGMLMMLGVRLEMDRYVERELPGGRTSVFYQRKNSLRS +Q9KXE1 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXE1_BPVT2 MAKPARRKCKICKEWVHPAFSNQWWCCPEHGTQLALERRSKEREKAEKAAEKKRRREEQKQKDKLKIRKLALKPRSYWIKQAQQAVNAFIRERDRDLPCISCGTLTSAQWDAGHYRTTAAAPQLRFDERNIHKQCVVCNQHKSGNLVPYRVELINRIGQEAVDEIESNHNRHRWTVEECRAIKAKYQQKLKDLRNSRSEAA +Q9KXE3 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXE3_BPVT2 MTTQISVETLSPITHNQIPVITTELLAQLYGTEPVRIRQNHHENKVRFVEGKHFFKVVGNDLKELRVALNYSQNLRVTLSNSQNLQPSLRGLQISPKARSLILWTERGAARHAKRLETDQGCVRKTGRLLFQPVREKYWQTREEAQRAFRKRNRQPCMAVGLCQPLTGIVP +Q9KXE4 unreviewed DNA restriction-modification system [GO:0009307]; methylation [GO:0032259] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXE4_BPVT2 MTIKSNTPAHDKDCWQTPLWLFDALDIEFGFWLDSAASDKNALCAHWLTEADDALNSEWVSHGAIWNNPPYSNIRPWVEKAAEQCIQQRQTVVMLVPEDMSVGWFSKALESVDEVRIITDGRINFIEPSTGLEKKGNSKGSMLLIWRPFISPRRMFTTVSKAALMAIGQGVRRAA +Q9KXE5 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXE5_BPVT2 MKQTFLLRNEAIRNNAIDAILSLPIDDKSPHEVHVKEPKRSKAQNDRMWPMLNDVSRQVLWHGQRLAPEDWKDLFTALWLKTKKLEQRSVPGIDGGVVMLGVRTSKMRKASMTELIEIMFWFGSERNVRWSDDSRREYEWSQRKGRAA +Q9KXE6 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXE6_BPVT2 MSDLSLTQPKLKECPFCGGNARLWVEAGINIDVWGYAECDLCEARGAWAPSVAAAAEKWNRRAGDEANLSASQRSNQK +Q9KXE7 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXE7_BPVT2 MSTIAELVRANFREELVRWYRYRSSSSLPLDELYEHSPAARRYPRDRVLRRLFKLNNEFQRNRIIRSLDLK +Q9KXE8 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXE8_BPVT2 MNDSYRQFENWWSKDKSQFTGDDELKEFAWVIWQASRSAIELDIDWPESNDDFWKDGEEGAYAMGYEDGRDKTVIAVMKAIRAAGIKEKNFD +Q9KXE9 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXE9_BPVT2 MNKKQLAILEKAWDAQISYALKEQALPIIQTKSKIARQLCDGGFLNEIEITRQMVTFKGYEINHHGIAAYCSHLPDDVDIDEMEREMKQ +Q9KXF1 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXF1_BPVT2 MSNISNLAEAREARRLQQPHQSSGKGYALLHRKIMDVPFYKDAEAAHLWVHLILKAKHTPEYVMTDAGEILVGRGKLLGGRNSLAFETGLKPDRVQYLLRKFKKLGMIDWVSHGKFSVFSVEKYDDYQSNFVPADYQQITTSKPAIPMPASNTVPADYQQITTDKEYNNIISNTDVLESATADKKSDKKKPSVSCQDVVDAYHEILPEAPKIRALNDKRKNQIRTFWRKAGVITRQLDGHGFTMQDWRNYLSYVGENCRWMFEERPNHQRGTVWHKKGFDFLLNDNTYLKVREGEHDDR +Q9KXF2 unreviewed regulation of DNA-templated transcription [GO:0006355] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXF2_BPVT2 MEQTSYSKLSQREIDRAETDLLINLSTLTQRGLAKMIGCHESKISRTDWRFIASVLCAFGMASDISPISRAFKYALDEITKKKSPAATEDFKQIDMQF +Q9KXF3 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXF3_BPVT2 MSNLRKYRESLNISQTTLAKAVGCTQGAIGHWESGRRFPDLKTCRALVACLNKLGAKVSLDDVFPPEHKAA +Q9KXF4 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXF4_BPVT2 MKWYELARSRMKELGITQEKLAEELGMTQGGIGHWLRGSRHPSLSDIGVVFKYLGIDNISFNHDGTFSPVGEYSSAPVKKQYEYPVFSHVQAGMFSPELRTFTKGDAERLVSTTKKASDSAFWLEVEGNSMTAPTGSKPSFPDGMLILVDPEQAVEPGDFCIARLGGDEFTFKKLIRDSGQVFLQPLNPQYPMIPCNESCSVVGKVIAGQWPEETFG +Q9KXF5 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXF5_BPVT2 MAKSNVSVQAFKDFLEELMSLNIMKEATARNLKNSSARLLTVVQEEEMGDVTQLDVNELAERYINATEPKPSDSSITAYKSRMESAIKKFVAFQSGEEIPYTPIDKESSEEKDLTGEPTKVEGKANALHTYDLPVVLRPESGVTVTIKGIPNDITNEEAERISSILKVYVRPQ +Q9KXF6 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXF6_BPVT2 MSTYNLNDQFDREVHVNAYERIRHGNLEHVCEHYRSRPHR +Q9KXF7 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXF7_BPVT2 MSRKTEFKGTAASRRRARRANLQSQEAISSDKLHRPTPSRVVLQCKRKPAMRAEVITLTTLTRKYEGSTCLPNVALYAAGYRKSKQLTAR +Q9KXF8 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXF8_BPVT2 MEFHESAICDFRANANSVKPQPIAVLFKTMGAWAVLCFAADDTDARMAIGQEMEMDPTNDEFIIYGAPSNYLLDTCNIYNKAA +Q9KXF9 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXF9_BPVT2 MSNIKKYIIDYDWKASIEIEIDHDVMTEEKLHQINNFWSDSEYRLNKHGSLLNAVLIMLAQHALLIAISSDLNAYGVVCEFDWNDGNGQEGWPPMDGSEGIRITDIDTSGIFDSDDMTIKAA +Q9KXG0 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXG0_BPVT2 MPLQGGLLLAALPNLYLNESPVNYVTDGNALSTYLISQESQKMDQTLMAIQTKFTIATFIGDEKMFREAVDAYKKWILILKLRSSKSIH +Q9KXG1 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXG1_BPVT2 MNAYYIQDRLEAQSWTRHYQQIAREEKEAELADDMGKGLPQHLFESLCIDHLQRHGASKKAITRAFDDDVEFQERMAEHIRYMVETIAHHQVDIDSEV +Q9KXG2 unreviewed DNA recombination [GO:0006310] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXG2_BPVT2 MSTALATLAGKLAERVGMDSVDPQELITTLRQTAFKGDASDAQFIALLIVANQYGLNPWTKEIYAFPDKQNGIVPVVGVDGWSRIINENQQFDGMDFEQDNESCTCRIYRKDRNHPICVTEWMDECRREPFKTREGREITGPWQSHPKRMLRHKAMIQCARLAFGFAGIYDKDEAERIVENTAYTAERQPERDITPVNDETMQEINTLLIALDKTWDDDLLPLCSQIFRRDIRASSELTQAEAVKVLGFLKQKASEQKVAA +Q9KXG3 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXG3_BPVT2 MTPDIILQRTGIDVRAVEQGDDAWHKLRLGVITASEVHNVIAKPRSGKKWPDMKMSYFHTLLAEVCTGVAPEVNAKALAWGKQYENDARTLFEFTSGVNVIESPIIYRDESMRTACSPDGLCSDGNGLELKCPFTSRDFMKFRLGGFEAIKSAYMAQVQYSMWVTRKDAWYFANYDPRMKREGLHYVVVERDEKYMASFDEMVPEFIEKMDEALAEIGFVFGEQWR +Q9KXG5 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXG5_BPVT2 MHKASPVELRTSIEMAHSLAQIGVRFVPIPVETDEEFHTLATSLSQKLEMMAAKAEANERDPA +Q9KXG6 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXG6_BPVT2 MHFSGSRLHILCAYACRHGTCSMTPQQENALRSIARQANSEIKKARQQFPDKNVDDICRSVLKKHRETVTLMGFTPTHLSLAIGMLNGVFKER +Q9KXG7 unreviewed positive regulation of secondary metabolite biosynthetic process [GO:1900378] Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXG7_BPVT2 MADLIDSASEIEELQRNTAIKMRRLNHQAISATHCCECGDPIDERRRLAVQGCRTCASCQEDLELISKQRGSK +Q9KXG8 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXG8_BPVT2 MSEINYQALREKAEKATKGSYIVGHTSVNQHGNLTGVFVCQKWKGEPGGVIAECHVNCLIESDDQAYANAEFIAEANPATVLALLDEQERNQQYIKRRDQENEEIALTVGKLRVELEAAENNLIDSECHVAELEEALRDKQALLEASEKRNAKLQSENAYIRNRYKELDLLIGKNILVMQAAIIEWQATGDAKSGLAWIYNTLFGPGELPDESEKDAQAYFNRKYAPIDEKLMALHKWFWEQSEAERAAGIRIKRGE +Q9KXG9 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXG9_BPVT2 MVFAKSPARSKTAGKTTCALKESDMAIAASYTMHLYCDCLQCTDGKYKSPDFGEYIGTSWAGCAKEARKDGWRISKDKTRAFAPGHKILRSNKGE +Q9KXH0 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXH0_BPVT2 MPTLFRKEYPRKSRATEFLFLILFIVLMTPISPLIFVWAIGKIIELVIELYNDVVWASFNTLHNKINPYKEN +Q9KXH1 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXH1_BPVT2 MATLQELIDLTPEQEKAWNRLVKAVKDFRAAGGKFYSVLDTLSAYNGEHVASIDNDKGYHTASVYMPSIDAPGLTSWADDWHGITLKDGVEVDKD +Q9KXH2 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXH2_BPVT2 MTTFTDKEMIKEIKERIGSLDVRDNIERRAYEIALTALTTEPFATIDTVGIELVKYGCNTFICPDNSMEPGNVPLYIGLPRIDPASQTAKLSFQEWLSEQKEKIDVDCGCVSIETLTHWMKSAYEAGNSPVTPDSWISCSDRMPEKGQNVLISVNFDSSLVEPLICSARYTGSTFRRGDATIKPGNGIEQATHWMPLPEPPQEVNRG +Q9KXH3 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXH3_BPVT2 MQRLSRVMVLSKQLTGCRYQNRRRRLTVANLQLAVKGEYFDAMIRGEKTEEYRLFNDYWNKRIMFREYDRLIITKGYPKRDDSSRRIDVPYDGYEIKTITHPHFGDKPVKVFAIKVNIGNE +Q9KXH4 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXH4_BPVT2 MEMIMACSTFNPLTLQKYQPDPEDLCSLCGGNHGKAAMIECKDKIHICLNCVDVLVDIKNEREDKKRSEAVRALDSWMRDGYSAAQIYDLAISKGEIPGVRIE +Q9KXH5 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9KXH5_BPVT2 MQRQLMRELVNQHNHGIQPVITPVVQINANEWVTLELLMAVTGLRKGTILRARDSAWMNGREYKQIAPDGTPKKNSECLYHLPTINTWIKNQPLPSQDV +Q9LAN9 unreviewed response to tellurium ion [GO:0046690] Escherichia coli O157:H7 Q9LAN9_ECO57 MAVSLVKGGNVSLTKEAPTMNVAMVGLGWDARVTDGQGFDLDASVFAVGEDGKVLSDAHFIFFNNKTSPDGAVEHQGDNRTGEGDGDDEQVKIDLTKVSADIKKLVFAVTIYDAEARKQNFGMVSNSFMRVYNNDNGTEIARFDLSEDASTETAMVFGELYRHGTEWKFKAVGQGFAGGLSALASQHGVNV +Q9LAP0 unreviewed response to tellurium ion [GO:0046690] Escherichia coli O157:H7 Q9LAP0_ECO57 MNLQSGQNIPLQQSTIRLNLQYPAKSGFKGEPDTCLFMLNAQGKVSGDSDFIFYNNLSSPEGAVRLVTGSQQASIEIALDRVPANVSKIAITVVIDGEDTIS +Q9LAP1 unreviewed Escherichia coli O157:H7 Q9LAP1_ECO57 MRITTLASVVIPCLGFSASSIAAAEDVMIVSASGYEKKLTNAAASVSVISQEELQSSQYHDLAEALRSVEGVDVESGTGKTGGLEISIRGMPASYTLILIDGVRQGGSSDVTPNGFSAMNTGFMPPLAAIERIEVIRGPMSTLYGSDAMGGVVNIITRKNADKWLSSVNAGLNLQESNKWGNSSQFNFWSSGPLVDDSVSLQVRGSTQQRQGSSVTSLSDTAATRIPYPTESQNYNLGARLDWKASEQDVLWFDMDTTRQRYDNRDGQLGSLTGGYDRTLRYERNKISAGYDHTFTFGTWKSYLNWNETENKGRELVRSVLKRDKWGLAGQPRELKESNLILNSLLLTPLGESHLVTVGGEFQSSSMKDGVVLASTGETFRQKSWSVFAEDEWHLTDALALTAGSRYEHHEQFGGHFSPRAYLVWDVADAWTLKGGVTTGYKAPRMGQLHKGISGVSGQGKTNLLGNPDLKPEESVSYEAGVYYDNPAGLNANVTGFMTDFSNKIVSYSINDNTNSYVNSGKARLHGVEFAGTLPLWSEDVTLSLNYTWTRSEQRDGDNKGAPLSYTPEHMVNAKLNWQITEEVASWLGARYRGKTPRFTQNYSSLSAVQKKVYDEKGEYLKAWTVVDAGLSWKMTDALTLNAAVNNLLNKDYSDVSLYSAGKSTLYAGDYFQTGSSTTGYVIPERNYWMSLNYQF +Q9LBU3 unreviewed Escherichia coli O157:H7 Q9LBU3_ECO57 MVISIRRSRHEEGEELVAIWCRSVDATHDFLSAEYRAELEELVRSFLPEAPLWVAVNERDQPVGFMLLSGQHMDALFIDPDVRGCGVGRMLVKHALSMAPELTTNVNEQNEQAVGFYKKVGFKVTGRSEVDDLGKPYPLLDLAYVGE +Q9MBL8 unreviewed Corynephage beta Q9MBL8_CORBE GADDVVDSSKSFVMENFSSYHGTKPGYVDSIQKGIQKPKSGTQGNYDDDWKGFYSTDNKHDAAGYSVDNENPLSGKAGGVVKVTYPGLTKVLALKVDNAETIKKELGLSLTEPLMEQVGTEEFIKRFGDGASRVVLSLPFAEGSSSVEYINNWEQAKALSVELEINFETRGKRGQDAKYEYMAQACAGNRVRRSVGSSLSCINLDWDVIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGTNPVFAGANYAAWAVNVAQVIDSETADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDIGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFLHDGYAVSWNTVEDSIIRTGFQGESGHDIKITAENTPLPIAGVLLPTIPGKLDVNKSKTHISVNGRKIRMRCRAIDGDVTFCRPKSPVYVGNGVHANLHVAFHRSSSEKIHSNEISSDSIGVLGYQKTVDHTKVNSKLSLFFEIKS +Q9QTG7 unreviewed proteolysis [GO:0006508] Clostridium botulinum D phage (Clostridium botulinum D bacteriophage) Q9QTG7_CBDP MTWPVKDFNYSDPVNDNDILYLRIPQNKLITTPVKAFMITQNIWVIPERFSSDTNPSLSKPPRPTSKYQSYYDPSYLSTDEQKDTFLKGIIKLFKRINERDIGKKLINYLVVGSPFMGDSSTPEDTFDFTRHTTNIAVEKFENGSWKVTNIITPSVLIFGPLPNILDYTASLTLQGQQSNPSFEGFGTLSILKVAPEFLLTFSDVTSNQSSAVLGKSIFCMDPVIALMHELTHSLHQLYGINIPSDKRIRPQVSEGFFSQDGPNVQFEELYTFGGLDVEIIPQIERSQLREKALGHYKDIAKRLNNINKTIPSSWISNIDKYKKIFSEKYNFDKDNTGNFVVNIDKFNSLYSDLTNVMSEVVYSSQYNVKNRTHYFSRHYLPVFANILDDNIYTIRDGFNLTNKGFNIENSGQNIERNPALQKLSSESVVDLFTKVCLRLTKNSRDDSTCIKVKNNRLPYVADKDSISQEIFENKIITDETNVQNYSDKFSLDESILDGQVPINPEIVDPLLPNVNMEPLNLPGEEIVFYDDITKYVDYLNSYYYLESQKLSNNVENITLTTSVEEALGYSNKIYTFLPSLAEKVNKGVQAGLFLNWANEVVEDFTTNIMKKDTLDKISDVSVIIPYIGPALNIGNSALRGNFNQAFATAGVAFLLEGFPEFTIPALGVFTFYSSIQEREKIIKTIENCLEQRVKRWKDSYQWMVSNWLSRITTQFNHINYQMYDSLSYQADAIKAKIDLEYKKYSGSDKENIKSQVENLKNSLDVKISEAMNNINKFIRECSVTYLFKNMLPKVIDELNKFDLRTKTELINLIDSHNIILVGEVDRLKAKVNESFENTMPFNIFSYTNNSLLKDIINEYFNSINDSKILSLQNKKNALVDTSGYNAEVRVGDNVQLNTIYTNDFKLSSSGDKIIVNLNNNILYSAIYENSSVSFWIKISKDLTNSHNEYTIINSIEQNSGWKLCIRNGNIEWILQDVNRKYKSLIFDYSESLSHTGYTNKWFFVTITNNIMGYMKLYINGELKQSQKIEDLDEVKLDKTIVFGIDENIDENQMLWIRDFNIFSKELSNEDINIVYEGQILRNVIKDYWGNPLKFDTEYYIINDNYIDRYIAPESNVLVLVQYPDRSKLYTGNPITIKSVSDKNPYSRILNGDNIILHMLYNSRKYMIIRDTDTIYATQGGECSQNCVYALKLQSNLGNYGIGIFSIKNIVSKNKYCSQIFSSFRENTMLLADIYKPWRFSFKNAYTPVAVTNYETKLLSTSSFWKFISRDPGWV +Q9R563 unreviewed Escherichia coli Q9R563_ECOLX AEVYNKDGNKLDLYG +Q9R564 unreviewed Escherichia coli Q9R564_ECOLX APKDNTWYTGAKLGW +Q9R931 unreviewed Streptococcus pyogenes Q9R931_STRPY GLTISQEVFAQQDPDPSQLHRSSLVKNLQNIYFLYEGDPVTHENVKSVDQLLSHDLIYNVSGPNYDKLKTELKNQEMATLFKDKNVDIYGVEYYHLCYLCENAERSACIYGGVTNHEGNHLEIPKKIVVKVSIDGIQSLSFDIETNKKMVTAQELDYKVRKYLTDNKQLYTNGPSKYETGYIKFIPKNKESFWFDFFPEPEFTQSKYLMIYKDNETLDSNTS +Q9T1L6 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9T1L6_BPVT2 MTMNELINSNAIKMTSIEIAELVGSRHDKVKQSIERLAVRGVIRNPPMVVFEKINNLGLLRGVEAYVFEGEQGKRDSIIVVAQLSPEFTARLVDRWRELEGATAKIPQTFSEALRLAADLEDQKAELEKQLALAAPKVEFADRVGEASGILIGNFAKVVGIGPNKLFAWMRDHKILIASGSRRNVPMQEYMDRGYFTVKETAVNTNHGIQISFTTKITGRGQQWLTRKLLDNGMLKVTREAA +Q9T1N8 unreviewed Enterobacteria phage VT2-Sa (Bacteriophage VT2-Sa) Q9T1N8_BPVT2 MAMKHPHDNIRVGAITFVYSVTKRGWVFPGLSVIRNPLKAQRLAEEINNKRGAVCTKHLPLS +T1TEV1 unreviewed Corynebacterium ulcerans T1TEV1_CORUL ASRVVLSLPFAEGSSSVEYINNWEQAKELSVELEINFETRGKRGQDAMYEYMAQSCAGNRVRRSVGSSLSCINLDWDAIRDKTKTKIESLKEHGPIKNKMSESPNKTVSEEKAKQYLEEFHQTALEHPELSELKTVTGVNSVFAGANYASWAVNVAQVVDSKTADNLEKTTAALSILPGIGSVMGIADGAVHHNTEEIVAQSIALSSLMVAQAIPLVGELVDLGFAAYNFVESIINLFQVVHNSYNRPAYSPGHKTQPFVHGGYAASWNTVEDSIIKTGFQGESGHDIKITAENTP +U5N0U5 unreviewed Enterobacteria phage fiAA91-ss U5N0U5_9CAUD MKRLIIIVTMLLISGCSSSQEAVNNQIDELGKENNSLFTFRNLQSGLMLHNRLDLHGREITGWEVVPVKTPAEALVTDQSGWIMIRTPNTDQCLGTPDGKNLLKMTCNPTDKKTLFSLIPSTTGAVQIKSVLSGLCFLDSKNSGLSFETGKCIADFKKPFEVVPQSHLWMLNPLNTESPII +U5N3G4 unreviewed Enterobacteria phage fiAA91-ss U5N3G4_9CAUD MANKYTPIFIAGILLPILLNGCSSGKNKAHLDPKVFPPQVEGGPTIPSPDEPGLPLPGAGPALPTNAPIPIPVPGTAPAVSLMNMDGSVLTMWSRGAGSSLWAYYISDSNSFGELRNWQIMPGTRPNTIQFRNVDVGTCMTSFPGFKGGVQLSTAPCQFGPDRFDFQPMVTRNGNYQLKSLSTGLCIRANFLERTPSSPYATTLTMERCPSSGERNFEFMWSISEPLRPALATIVKPEIRPFPPLPIEPDRHSAGGEQ From 5535038df9bb65adb583bd0bcdae1af79a2c2a74 Mon Sep 17 00:00:00 2001 From: Dreycey Date: Sat, 17 Aug 2024 11:03:21 -0700 Subject: [PATCH 14/17] adding toxin GO term analysis --- misc/plot_results.ipynb | 53 ++++++++++++++++++++++++++--------------- 1 file changed, 34 insertions(+), 19 deletions(-) diff --git a/misc/plot_results.ipynb b/misc/plot_results.ipynb index c368973..45e73e6 100644 --- a/misc/plot_results.ipynb +++ b/misc/plot_results.ipynb @@ -1589,42 +1589,38 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 84, "id": "176bb043", "metadata": {}, "outputs": [], "source": [ + "# parse the uniprot CSV\n", "all_toxins = [(k, v) for k,v in get_go_names(FilePaths.toxin_uniprot_info).items()]\n", "all_toxins = sorted(all_toxins, key=lambda a: a[1], reverse=True)" ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 85, "id": "5820debb", "metadata": { "scrolled": true }, "outputs": [ { - "ename": "ValueError", - "evalue": "'explode' must be of length 'x'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[79], line 16\u001b[0m\n\u001b[1;32m 13\u001b[0m explode \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.01\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mlen\u001b[39m(toxin2count)\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# plotting data on chart \u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpie\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshades_of_green\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mexplode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43mautopct\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m%.0f\u001b[39;49;00m\u001b[38;5;132;43;01m%%\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# displaying chart \u001b[39;00m\n\u001b[1;32m 23\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", - "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/matplotlib/pyplot.py:3676\u001b[0m, in \u001b[0;36mpie\u001b[0;34m(x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels, normalize, hatch, data)\u001b[0m\n\u001b[1;32m 3653\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mpie)\n\u001b[1;32m 3654\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpie\u001b[39m(\n\u001b[1;32m 3655\u001b[0m x: ArrayLike,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3674\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 3675\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mlist\u001b[39m[Wedge], \u001b[38;5;28mlist\u001b[39m[Text]] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mlist\u001b[39m[Wedge], \u001b[38;5;28mlist\u001b[39m[Text], \u001b[38;5;28mlist\u001b[39m[Text]]:\n\u001b[0;32m-> 3676\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpie\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3677\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3678\u001b[0m \u001b[43m \u001b[49m\u001b[43mexplode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3679\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3680\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3681\u001b[0m \u001b[43m \u001b[49m\u001b[43mautopct\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautopct\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3682\u001b[0m \u001b[43m \u001b[49m\u001b[43mpctdistance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpctdistance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3683\u001b[0m \u001b[43m \u001b[49m\u001b[43mshadow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshadow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3684\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabeldistance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabeldistance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3685\u001b[0m \u001b[43m \u001b[49m\u001b[43mstartangle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstartangle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3686\u001b[0m \u001b[43m \u001b[49m\u001b[43mradius\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mradius\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3687\u001b[0m \u001b[43m \u001b[49m\u001b[43mcounterclock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcounterclock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3688\u001b[0m \u001b[43m \u001b[49m\u001b[43mwedgeprops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwedgeprops\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3689\u001b[0m \u001b[43m \u001b[49m\u001b[43mtextprops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtextprops\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3690\u001b[0m \u001b[43m \u001b[49m\u001b[43mcenter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcenter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3691\u001b[0m \u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3692\u001b[0m \u001b[43m \u001b[49m\u001b[43mrotatelabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrotatelabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3693\u001b[0m \u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3694\u001b[0m \u001b[43m \u001b[49m\u001b[43mhatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3695\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3696\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/matplotlib/__init__.py:1473\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1470\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1471\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1472\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1474\u001b[0m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1475\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1476\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1478\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1479\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1480\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", - "File \u001b[0;32m~/miniconda3/envs/phagescannerv2/lib/python3.12/site-packages/matplotlib/axes/_axes.py:3300\u001b[0m, in \u001b[0;36mAxes.pie\u001b[0;34m(self, x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels, normalize, hatch)\u001b[0m\n\u001b[1;32m 3298\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be of length \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(x) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(explode):\n\u001b[0;32m-> 3300\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexplode\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be of length \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3301\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m colors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3302\u001b[0m get_next_color \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_patches_for_fill\u001b[38;5;241m.\u001b[39mget_next_color\n", - "\u001b[0;31mValueError\u001b[0m: 'explode' must be of length 'x'" - ] + "data": { + "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1639,12 +1635,12 @@ "data = []\n", "keys = []\n", "for i, (k, v) in enumerate(all_toxins[1:]):\n", - " if i > max_goterms_to_plot: break\n", + " if i > 6: break\n", " data.append(v)\n", " keys.append(k)\n", "\n", "# declaring exploding pie \n", - "explode = [0.01]*len(toxin2count)\n", + "explode = [0.01]*len(data)\n", "\n", "# plotting data on chart \n", "plt.pie(data,\n", @@ -1656,7 +1652,8 @@ "# displaying chart \n", "plt.show()\n", "\n", - "plt.savefig('toxin_piechart.png', dpi=300, bbox_inches='tight')" + "plt.savefig('toxin_piechart.png', dpi=300, bbox_inches='tight')\n", + "#" ] }, { @@ -1666,6 +1663,24 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "bcdfb86e", + "metadata": {}, + "outputs": [], + "source": [ + "## END OF FILE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8deada06", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From e14afcd1f46cd291824806f68dddceb1f75a1c57 Mon Sep 17 00:00:00 2001 From: Dreycey Date: Fri, 29 Nov 2024 03:41:24 -0600 Subject: [PATCH 15/17] Refactor pipeline classes. This commit removes using self.dataframe from several of the pipeline classes. These refactorings will ensure the code/flow is cleaner and allow for adding specific tests. --- PhageScanner/main/feature_extractors.py | 37 +- PhageScanner/main/models.py | 24 +- .../main/pipelines/database_pipeline.py | 6 +- .../main/pipelines/pipeline_interface.py | 45 --- .../main/pipelines/prediction_pipeline.py | 45 +-- .../main/pipelines/training_pipeline.py | 336 +++++++----------- .../main/tool_wrappers/orffinder_wrappers.py | 33 +- PhageScanner/main/utils.py | 11 +- README.md | 4 +- phagescanner.py | 2 + requirements.txt | 10 +- 11 files changed, 229 insertions(+), 324 deletions(-) diff --git a/PhageScanner/main/feature_extractors.py b/PhageScanner/main/feature_extractors.py index 22e814a..4a2e6ab 100644 --- a/PhageScanner/main/feature_extractors.py +++ b/PhageScanner/main/feature_extractors.py @@ -16,6 +16,7 @@ from abc import ABC, abstractmethod from enum import Enum from typing import Dict, List, Optional +from concurrent.futures import ProcessPoolExecutor import numpy as np from Bio.SeqUtils.ProtParam import ProteinAnalysis @@ -116,7 +117,7 @@ class ProteinFeatureExtraction(ABC): "Y": {"C": 9, "H": 9, "N": 1, "O": 2, "S": 0}, # Tyrosine } - canonical_amino_acids = set(sorted(list(amino_acid_atom_counts.keys()))) + canonical_amino_acids = sorted(amino_acid_atom_counts.keys()) @abstractmethod def extract_features(self, protein: str): @@ -498,7 +499,7 @@ class SequentialOneHot(ProteinFeatureExtraction): def __init__(self, parameters: Optional[Dict] = None): """Instantiate tokenization extract method.""" self.aa2index = {aa: ind for ind, aa in enumerate(self.canonical_amino_acids)} - self.matrix_length = 2000 + self.matrix_length = 500 def extract_features(self, protein: str): """Obtain an tokenization of the protein sequence.""" @@ -580,7 +581,7 @@ def __init__(self, parameters: Optional[Dict] = None): self.aa_dict = aa_dict - def extract_features(self, protein: str, max_length=1000): + def extract_features(self, protein: str, max_length=500): """ Integer encoding for a protein. """ integer_encoding = [] for amino_acid in protein: @@ -721,6 +722,36 @@ def extract_features(self, protein): count += 1 return [sequential_features] + +def extract_feature_vector(proteins, model_features, segment_size:int=0): + """ Extract the feature vector from a list/array of proteins. """ + logging.info(f"Starting to extract protein features.") + + # get feature extractors. + feature_list = [] + for feature_name, parameters in model_features: + extractor = FeatureExtractorNames.get_extractor(feature_name, parameters) + feature_list.append(extractor) + + # create feature aggregator (combines features) + if segment_size: + aggregator = SequentialProteinFeatureAggregator( + extractors=feature_list, segment_size=segment_size + ) + else: + aggregator = ProteinFeatureAggregator(extractors=feature_list) + + # extract features + features = [] + if len(proteins) < 1000: + features = [aggregator.extract_features(protein) for protein in proteins] + else: + with ProcessPoolExecutor() as executor: + features = list(executor.map(aggregator.extract_features, proteins)) + + logging.info(f"Finished extracting features.") + + return np.vstack(np.array(features)) if __name__ == "__main__": diff --git a/PhageScanner/main/models.py b/PhageScanner/main/models.py index 7d79967..fba4571 100644 --- a/PhageScanner/main/models.py +++ b/PhageScanner/main/models.py @@ -356,9 +356,9 @@ def train(self, train_x, train_y, test_x, test_y): early_stopping = EarlyStopping( monitor="loss", mode='min', - verbose=2, - patience=5, - min_delta=0.02 + verbose=1, + patience=1, + min_delta=0.01 ) # train the model @@ -366,10 +366,10 @@ def train(self, train_x, train_y, test_x, test_y): train_x, train_y, validation_data=(test_x, test_y), - epochs=200, + epochs=5, callbacks=[early_stopping], batch_size=5000, - verbose=2, + verbose=1 ) @@ -431,7 +431,7 @@ def train(self, train_x, train_y, test_x, test_y): early_stopping = EarlyStopping( monitor="val_loss", min_delta=0.01, - patience=2, + patience=1, verbose=1, mode="auto", baseline=None, @@ -457,7 +457,7 @@ def __init__(self): """Initialize the RNN MultiClassifier.""" self.model = None - def build_model(self, row_length, number_of_classes, max_length=1000): + def build_model(self, row_length, number_of_classes, max_length=500): """Build the RNN Model. Description: @@ -466,8 +466,8 @@ def build_model(self, row_length, number_of_classes, max_length=1000): """ # model model = Sequential() - model.add(Input(shape=(max_length,))) - model.add(Embedding(21, 128, input_length=max_length)) + model.add(Input(shape=(row_length,))) + model.add(Embedding(21, 50, input_length=row_length)) model.add( Bidirectional(LSTM(64, kernel_regularizer=l2(0.01), @@ -497,8 +497,8 @@ def train(self, train_x, train_y, test_x, test_y): # set up early stopping criterion. early_stopping = EarlyStopping( monitor="val_loss", - min_delta=0.01, - patience=2, + min_delta=0.1, + patience=1, verbose=1, mode="auto", baseline=None, @@ -510,7 +510,7 @@ def train(self, train_x, train_y, test_x, test_y): train_x, train_y, validation_data=(test_x, test_y), - epochs=300, + epochs=5, callbacks=[early_stopping], batch_size=32, verbose=1, diff --git a/PhageScanner/main/pipelines/database_pipeline.py b/PhageScanner/main/pipelines/database_pipeline.py index f886673..3b3ca40 100644 --- a/PhageScanner/main/pipelines/database_pipeline.py +++ b/PhageScanner/main/pipelines/database_pipeline.py @@ -334,13 +334,13 @@ def run(self): # Step 4: create k-fold partitioned clusters. logging.info("Step 4 - Create k-fold partitions...") - min_cluster_size = self.get_min_partition_size( + max_cluster_size = self.get_min_partition_size( clustering_identity_threshold=self.config_object.get_clustering_threshold() ) - min_cluster_size = 100 + max_cluster_size = 10 self.partition_proteins( clustering_identity_threshold=self.config_object.get_clustering_threshold(), - max_cluster_size=min_cluster_size, + max_cluster_size=max_cluster_size, k_partitions=self.config_object.get_k_partition_count(), ) logging.info("Step 4 (Finished) - Create k-fold partitions...") diff --git a/PhageScanner/main/pipelines/pipeline_interface.py b/PhageScanner/main/pipelines/pipeline_interface.py index 4f367c8..3cb50ae 100644 --- a/PhageScanner/main/pipelines/pipeline_interface.py +++ b/PhageScanner/main/pipelines/pipeline_interface.py @@ -1,17 +1,7 @@ """This pipeline interface enforces each inheriting class impliments the run method.""" - -import logging import time from abc import ABC, abstractmethod -import swifter - -from PhageScanner.main.feature_extractors import ( - FeatureExtractorNames, - ProteinFeatureAggregator, - SequentialProteinFeatureAggregator, -) - class Pipeline(ABC): """Abstract class for creating new pipelines""" @@ -23,38 +13,3 @@ class Pipeline(ABC): def run(self): """Run the pipeline.""" pass - - def extract_feature_vector(self, model_name): - """Extract the feature vector from each protein. - - Description: - This method extracts feature vectors for each protein in - the dataframe "self.dataframe". - """ - logging.info(f"extracting protein features: '{model_name}'") - - # get feature extractors. - feature_list = [] - for feature_name, parameters in self.config_object.get_model_features( - model_name - ): - extractor = FeatureExtractorNames.get_extractor(feature_name, parameters) - feature_list.append(extractor) - - # create feature aggregator (combines features) - segment_size = self.config_object.sequential(model_name) - if segment_size: - aggregator = SequentialProteinFeatureAggregator( - extractors=feature_list, segment_size=segment_size - ) - else: - aggregator = ProteinFeatureAggregator(extractors=feature_list) - - # use the aggregator to extract features from self.dataframe - logging.info(f"extracting features for model: '{model_name}': {feature_list}") - self.dataframe["features"] = self.dataframe["protein"].apply( - aggregator.extract_features - ) - - - logging.info(f"done extracting features for model: '{model_name}'") diff --git a/PhageScanner/main/pipelines/prediction_pipeline.py b/PhageScanner/main/pipelines/prediction_pipeline.py index 12c4a9b..838b1e3 100644 --- a/PhageScanner/main/pipelines/prediction_pipeline.py +++ b/PhageScanner/main/pipelines/prediction_pipeline.py @@ -105,7 +105,7 @@ def assemble_reads(self): return full_assembly_path - def get_proteins(self): + def get_proteins(self, orf_score_threshold=-1000): """Get the proteins from an input set of contigs/genomes. Description: @@ -114,20 +114,20 @@ def get_proteins(self): the expected protein products. """ # eventual outputs - orf_path = self.directory / (str(self.pipeline_name) + "_" + "orfs") + orf_path = self.directory / (str(self.pipeline_name) + "_orfs") orf_file_prefix_proteins = self.directory / ( - str(self.pipeline_name) + "_" + "orfs" + "_proteins.csv" + str(self.pipeline_name) + "_orfs_proteins.csv" ) # check if completed.. - if ( - os.path.exists(orf_file_prefix_proteins) - and os.stat(orf_file_prefix_proteins).st_size > 0 - ): - message = f"(Skipping) Found ORF file CSV: '{orf_file_prefix_proteins}'. " - message += "Please delete this file if you'd like to rerun!" - logging.info(message) - return orf_file_prefix_proteins + # if ( + # os.path.exists(orf_file_prefix_proteins) + # and os.stat(orf_file_prefix_proteins).st_size > 0 + # ): + # message = f"(Skipping) Found ORF file CSV: '{orf_file_prefix_proteins}'. " + # message += "Please delete this file if you'd like to rerun!" + # logging.info(message) + # return orf_file_prefix_proteins # Get ORFs. orf_file_path = self.orffinder.find_orfs( @@ -144,20 +144,27 @@ def get_proteins(self): for orf_name, orf_sequence in FastaUtils.get_proteins( orf_file_path, withfullname=True ): - accession_id, start_pos, stop_pos, score = ( + accession_id, start_pos, stop_pos, score, is_complement = ( self.orffinder.get_info_from_name(orf_name) ) + + if score > orf_score_threshold: + continue - if stop_pos == -1: - stop_pos = start_pos + len(orf_sequence) + # correct for compliments found + contiglen = accession2length[accession_id] + # if is_complement: + # start_pos = contiglen - stop_pos + # stop_pos = contiglen - start_pos # save to output file. orf_results = { "accession": accession_id, - "length": accession2length[accession_id], + "length": contiglen, "start_pos": start_pos, "stop_pos": stop_pos, "orf_score": score, + "is_complement": is_complement, "protein": DNA.dna2protein(orf_sequence), } CSVUtils.appendcsv( @@ -165,6 +172,7 @@ def get_proteins(self): fieldnames=orf_results.keys(), file_path=orf_file_prefix_proteins, ) + print(start_pos, stop_pos) return orf_file_prefix_proteins @@ -187,7 +195,7 @@ def get_predictions(self): # for each model, get predictions. for model in self.config_object.get_model_names(): # extract features from each protein. - self.extract_feature_vector(model) + x_test = self.extract_feature_vector(model, self.dataframe["protein"].to_numpy()) # instantiate the model. model_path = self.config_object.get_model_path( @@ -197,11 +205,7 @@ def get_predictions(self): model_object = ModelNames.get_model(model_predictor_name) model_object = model_object.load(model_path) - # get the model classes. - index2class = self.config_object.get_model_classes(model) - # predict features from proteins. - x_test = np.vstack(self.dataframe["features"].to_numpy()) predictions, probabilities = model_object.predict(x_test) self.dataframe[model] = predictions @@ -214,6 +218,7 @@ def get_predictions(self): ) # replace values with class names. + index2class = self.config_object.get_model_classes(model) self.dataframe[model] = self.dataframe[model].replace(index2class) def run(self): diff --git a/PhageScanner/main/pipelines/training_pipeline.py b/PhageScanner/main/pipelines/training_pipeline.py index 85b278e..a9fc7de 100644 --- a/PhageScanner/main/pipelines/training_pipeline.py +++ b/PhageScanner/main/pipelines/training_pipeline.py @@ -3,16 +3,17 @@ import logging import os from pathlib import Path -from typing import Dict, Union +from typing import Dict, Union, Iterator, Optional, Tuple, Any import gc import numpy as np import pandas as pd -import swifter +from imblearn.over_sampling import SMOTE +from imblearn.combine import SMOTEENN from PhageScanner.main import utils from PhageScanner.main.exceptions import IncorrectValueError -from PhageScanner.main.feature_extractors import ProteinFeatureExtraction +from PhageScanner.main.feature_extractors import ProteinFeatureExtraction, extract_feature_vector from PhageScanner.main.models import ModelNames from PhageScanner.main.pipelines.pipeline_interface import Pipeline @@ -34,123 +35,111 @@ def __init__(self, config: Path, db_directory: Path, directory: Path): self.number_of_classes = 0 # updated during pipeline run. self.db_directory = db_directory - # pandas dataframe holds all of the training data - self.dataframe = pd.DataFrame() - - # dictionary to map index to class name - self.class2name = dict() - # create directory if it doesn't exist if not os.path.exists(self.directory): os.mkdir(self.directory) - - def combine_partitions(self): - """Combine the partitions for different classes CSVs. - - Description: - This method combines classes into different partitions. - The output of using this method void, since the self.dataframe - is added to for each input. In addition, the input value ("value") - is added to each input dataframe, for the new column "class", - before adding to self.dataframe. - - Args: - data (.csv file path): contains a csv file path. - """ - index = 0 - # get classes + + # Create class name to index mapping. model_classes = self.config_object.get_classes() - - # save number of classes self.number_of_classes = len(model_classes) - for class_name in model_classes: - # Read the csv file into a pandas DataFrame + + # Create class name to index mapping. + self.class2index = dict() + self.class2filepath = dict() + for index, class_name in enumerate(model_classes): class_name = class_name.get("name") - logging.debug(f"Combining class {class_name} into the dataframe.") class_path = self.config_object.get_csv_path_from_name( class_name, self.db_directory ) - df = pd.read_csv(class_path) - - # Add a new column 'class' with the provided value - # NOTE: index must be an integer. - df["class"] = index - - # add information about class to name mapping - # NOTE: index must be an integer. - self.class2name[index] = class_name - - # Append this data to the main dataframe - # NOTE: this is not optimal since Pandas - # DFs will result in quadratic behaviour due - # to duplicating the dataframe. However, - # here we expect few classes. - # see: https://stackoverflow.com/questions/36489576/ - self.dataframe = pd.concat([self.dataframe, df]) - - index += 1 - - # only select columns needed - try: - self.dataframe = self.dataframe[["partition", "class", "protein"]] - except KeyError as ex: - raise IncorrectValueError( - f"Columns in {class_path} are not correct: {ex.message}" - ) + self.class2index[class_name] = index + self.class2filepath[class_name] = class_path + + # Get model information + models = self.config_object.get_models() + self.modelname2information = dict() + for model_name in models: + self.modelname2information[model_name] = { + 'path2savemodel' : self.directory / f"{model_name}", + 'model_features' : [i for i in self.config_object.get_model_features(model_name)], + 'segement_size' : self.config_object.sequential(model_name), + 'model_predictor_name' : self.config_object.get_predictor_model_name(model_name) + } + + @staticmethod + def create_combined_df(class2filepath, class2index): + """Create a Pandas dataframe from the combined partitions for different classes CSVs.""" + dataframes = [] + + for class_name, class_path in class2filepath.items(): + logging.debug(f"Combining class {class_name} into the dataframe.") + df_tmp = pd.read_csv(class_path) + df_tmp["class"] = class2index[class_name] + dataframes.append(df_tmp) - def balance_partitions(self): - """Balance classes within each partition. + # Concatenate all dataframes at once + combined_dataframe = pd.concat(dataframes, ignore_index=True) + + return combined_dataframe - Description: - Here we upsample the smaller classes with replacement. The - justification is that there may be large negative classes, - for example non-pvp, that contain more unique proteins. For the - models to learn these, we don't want to downsample. Likewise, if - there are really small classes, then the models would lose context - on other potential proteins within the other classes. + @staticmethod + def hybrid_sample_partitions(df, percentile: float = 50, interpolation: str = 'linear') -> pd.DataFrame: """ - new_balanced_partitions_df = [] - for partition in self.dataframe["partition"].unique(): - partition_df = self.dataframe[self.dataframe["partition"] == partition] + Balance classes within each partition by upsampling smaller classes + and downsampling larger classes based on an interpolated percentile. + """ + balanced_partitions = [] - # get smallest class size. + for partition, partition_df in df.groupby("partition"): class_sizes = partition_df["class"].value_counts() - max_class_size = max(class_sizes) - - # create a balanced partition. - balanced_partition_df_list = [] - for class_index in partition_df["class"].unique(): - # get proteins corresponding to the class index. - class_df: pd.DataFrame = partition_df[ - partition_df["class"] == class_index - ] - - # add to balanced_partition_df_list - balanced_partition_df_list.append(class_df) - - # get differrence from the max class size, then upsample w/ replacement - size_difference = max_class_size - len(class_df) - if size_difference > 0: - balanced_class_df = class_df.sample( - n=size_difference, random_state=1, replace=True - ) - balanced_partition_df_list.append(balanced_class_df) - - # combine balanced classes for this partition. - balanced_partition_df = pd.concat( - balanced_partition_df_list, ignore_index=True - ) + percentile_class_size = int(np.percentile(class_sizes, percentile, interpolation=interpolation)) + + balanced_classes = [] + for _, class_df in partition_df.groupby("class"): + if len(class_df) > percentile_class_size: + sampled_df = class_df.sample(n=percentile_class_size, random_state=1) + else: + sampled_df = class_df.sample(n=percentile_class_size, replace=True, random_state=1) + balanced_classes.append(sampled_df) + + balanced_partitions.append(pd.concat(balanced_classes, ignore_index=True)) + + return pd.concat(balanced_partitions, ignore_index=True) + + def train_and_test_models(self, df, modelname2information): + """ Perform model training and testing. """ + for model_name, model_information in modelname2information.items(): + kfold_iteration = 0 + path2savemodel, model_features, segement_size, model_predictor_name = (model_information['path2savemodel'], + model_information['model_features'], + model_information['segement_size'], + model_information['model_predictor_name']) + for x_train, y_train, x_test, y_test in TrainingPipeline.get_kfold_training(df, model_features, segement_size): + logging.info(f"Training and testing model: {model_name} (Iteration {kfold_iteration})...") - # ensure the min class size is now the same as max. - assert max_class_size == min(balanced_partition_df["class"].value_counts()) + model_object = ModelNames.get_model(model_predictor_name) + model_object.train(x_train, y_train, x_test, y_test) + model_object.save(path2savemodel) + model_results = model_object.test(x_test, y_test) - # add balanced partition to new_balanced_partitions_df. - new_balanced_partitions_df.append(balanced_partition_df) + TrainingPipeline.save_model_results( + directory_to_save_results=self.directory, + model_name=model_name, + model_features=model_features, + pipeline_start_time=self.pipeline_start_time, + iteration=kfold_iteration, + model_results=model_results, + ) - # combine all balanced partitions to a single dataframe. - self.dataframe = pd.concat(new_balanced_partitions_df, ignore_index=True) + # increment the kfold iteration + kfold_iteration += 1 + # free up memory + del x_train, y_train, x_test, y_test + gc.collect() + + logging.info(f"Finished Training and testing Model: {model_name} (Iteration {kfold_iteration})...") - def get_kfold_training(self): + @staticmethod + def get_kfold_training(df, model_features, segment_size): """Get X_train, Y_train, X_test, Y_test. Description: @@ -158,42 +147,31 @@ def get_kfold_training(self): for training and testing to be performed based on how the data is partitioned. """ - unique_partitions = self.dataframe["partition"].unique() + unique_partitions = df["partition"].unique() for test_partition in unique_partitions: - logging.debug("Obtaining training splits.") - # get partition. - test = self.dataframe[self.dataframe["partition"] == test_partition] - train = self.dataframe[self.dataframe["partition"] != test_partition] - - # get testing and training splits. - x_test = np.vstack(test["features"].to_numpy()) - y_test = test["class"].to_numpy() - x_train = np.vstack(train["features"].to_numpy()) - y_train = train["class"].to_numpy() - - logging.debug("Done: Obtaining training splits.") - yield x_train, y_train, x_test, y_test + test_df = df[df["partition"] == test_partition] + train_df = df[df["partition"] != test_partition] - def save_class_mapping(self): - """Save class mapping.""" - # obtain information to save about class mapping. - index2class_name_file = self.directory / "index2class_name.csv" - output_index2class = {"datetime": self.pipeline_start_time} + x_train = extract_feature_vector(train_df["protein"].to_numpy(), model_features, segment_size) + y_train = train_df["class"].to_numpy() + x_test = extract_feature_vector(test_df["protein"].to_numpy(), model_features, segment_size) + y_test = test_df["class"].to_numpy() - # convert keys to string for saving. - for index, class_name in self.class2name.items(): - output_index2class[str(index)] = class_name + # up and down sample + # sm = SMOTE(random_state=42) + # x_train, y_train = sm.fit_resample(x_train, y_train) + + smote_enn = SMOTEENN(random_state=0) + x_train, y_train = smote_enn.fit_resample(x_train, y_train) - # save the index to class mapping. - utils.CSVUtils.appendcsv( - data_dict=[output_index2class], # input must be an array. - fieldnames=output_index2class.keys(), - file_path=index2class_name_file, - ) + yield x_train, y_train, x_test, y_test + @staticmethod def save_model_results( - self, + directory_to_save_results: Path, model_name: str, + model_features: Iterator[Tuple[str, Optional[Any]]], + pipeline_start_time, iteration: int, model_results: Dict[str, Union[str, float, str]], ): @@ -208,7 +186,7 @@ def save_model_results( """ # save the confusion matrix confusion_matrix_output_path = ( - self.directory / f"{model_name}_confusion_matrix.csv" + directory_to_save_results / f"{model_name}_confusion_matrix.csv" ) np.savetxt( confusion_matrix_output_path, @@ -219,7 +197,7 @@ def save_model_results( del model_results["confusion_matrix"] # Save the f1score, accuracy, and precision - output_path = self.directory / "model_results.csv" + output_path = directory_to_save_results / "model_results.csv" csv_file_headers = [ "datetime", "model", @@ -235,7 +213,7 @@ def save_model_results( # obtain a list of features and parameters. features = [] - for feature, params in self.config_object.get_model_features(model_name): + for feature, params in model_features: if params: param_strs = [f"{k}={v}" for k, v in params.items()] params_text = "; ".join(param_strs) @@ -245,10 +223,12 @@ def save_model_results( features.append(feature_string) # add columns not originally present in the dataframe - model_results["datetime"] = self.pipeline_start_time - model_results["model"] = model_name - model_results["kfold_iteration"] = iteration - model_results["features"] = features + model_results.update({ + "datetime": pipeline_start_time, + "model": model_name, + "kfold_iteration": iteration, + "features": features + }) # convert columns with many entries to strings for key in ["f1score", "precision", "recall", "features"]: @@ -263,82 +243,24 @@ def save_model_results( def run(self): """Run the training pipeline.""" - # Step 1: combine partitions from different classes. - logging.info("Step 1 - Class balance and combine partitions...") - self.combine_partitions() - self.save_class_mapping() - logging.info("Step 1 (Finished) - Class balance and combine partitions...") + # Step 1: Combine partitions from different classes. + logging.info("Step 1 - Combine partitions...") + combined_dataframe = TrainingPipeline.create_combined_df(self.class2filepath, self.class2index) + logging.info("Step 1 (Finished) - Combine partitions...") - # Step 2: clean proteins + # Step 2: Clean proteins logging.info("Step 2 - Cleaning proteins...") - self.dataframe["protein"] = self.dataframe["protein"].apply( + combined_dataframe["protein"] = combined_dataframe["protein"].apply( ProteinFeatureExtraction.clean_protein ) logging.info("Step 2 (Finished) - Cleaning proteins...") # Step 3: Balance the classes with different partitions. logging.info("Step 3 - Balancing classes in each partition...") - self.balance_partitions() + #combined_dataframe = TrainingPipeline.hybrid_sample_partitions(combined_dataframe, percentile=70) logging.info("Step 3 (Finished) - Balancing classes in each partition...") - # Step 3: extract features and train. - logging.info("Step 4 - Training Models") - models = self.config_object.get_models() - for iteration, model_name in enumerate(models): - # obtain features. - logging.info(f"Step 4.{iteration} - Feature Extraction...") - self.extract_feature_vector(model_name) - logging.info(f"Step 4.{iteration} (Finished) - Feature Extraction...") - - # train model - logging.info(f"Step 4.{iteration} - k-fold testing model: {model_name}...") - kfold_iteration = 0 - bestmodel_f1score = 0 - for x_train, y_train, x_test, y_test in self.get_kfold_training(): - # obtain model. - model_predictor_name = self.config_object.get_predictor_model_name( - model_name - ) - model_object = ModelNames.get_model(model_predictor_name) - - # train model. - logging.info(f"Training Model: {model_name}...") - model_object.train(x_train, y_train, x_test, y_test) - logging.info(f"(Finished) Training Model: {model_name}...") - - # test trained model. - logging.info(f"Testing Model: {model_name}...") - model_results = model_object.test(x_test, y_test) - logging.info(f"(Finished) Testing Model: {model_name}...") - - # save model results. - self.save_model_results( - model_name=model_name, - iteration=kfold_iteration, - model_results=model_results, - ) - - # save the model. - model_f1score = np.average([float(i) for i in model_results["f1score"].split("\t")]) - if model_f1score > bestmodel_f1score: - bestmodel_f1score = model_f1score - path2savemodel = self.directory / f"{model_name}" - model_object.save(path2savemodel) - - # make sure saved model works. - new_model = model_object.load(path2savemodel) - new_model.test(x_test, y_test) - - # increment the kfold iteration - kfold_iteration += 1 - - # free up memory - del x_train, y_train, x_test, y_test - gc.collect() - - if kfold_iteration >= 3: - break - - logging.info( - f"Step 4.{iteration} (Finished) - k-fold testing model: {model_name}..." - ) + # Step 4: Extract features and train. + logging.info("Step 4 - Training and Testing Models") + self.train_and_test_models(combined_dataframe, self.modelname2information) + logging.info("Step 4 (Finished) - Training and Testing Models") \ No newline at end of file diff --git a/PhageScanner/main/tool_wrappers/orffinder_wrappers.py b/PhageScanner/main/tool_wrappers/orffinder_wrappers.py index 7f2e225..e6076d7 100644 --- a/PhageScanner/main/tool_wrappers/orffinder_wrappers.py +++ b/PhageScanner/main/tool_wrappers/orffinder_wrappers.py @@ -95,29 +95,18 @@ def find_orfs(self, fasta_path: Path, outpath: Path) -> Path: @staticmethod def get_info_from_name(fasta_entry_name: str): """Get information from the fasta entry name.""" - # First parsing strategy with regex - pattern = r"([^_\s]+)_CDS_\[(\d+)\.\.(\d+)\] \[note=score:(-?\d+\.\d+[eE][+-]\d+)\]" - logging.info(f"Attempting to parse with regex: {fasta_entry_name}") + pattern = r"([^_>\s]+)_CDS_\[(complement\()?(\d+)\.\.(\d+)\)?\] \[note=score:([-+]?\d+\.\d+[eE][+-]\d+)\]" + logging.debug(f"Attempting to parse with regex: {fasta_entry_name}") match = re.search(pattern, fasta_entry_name) - - if match: - accession_id = match.group(1) - start_pos = int(match.group(2)) - end_pos = int(match.group(3)) - score = float(match.group(4)) - logging.info(f"Parsed with regex: {accession_id}, {start_pos}, {end_pos}, {score}") - return accession_id, start_pos, end_pos, score - - # Fallback parsing to older phanotate output if the first method fails. - logging.info("Regex failed, attempting to parse with split method") + try: - parts = fasta_entry_name.split(" ") - accession_id = ".".join(parts[0].split(".")[:-1]) - start_pos = int(parts[1].replace("[START=", "").replace("]", "")) - score = float(parts[2].replace("[SCORE=", "").replace("]", "")) - end_pos = -1 # Default or fallback end_pos - logging.info(f"Parsed with split: {accession_id}, {start_pos}, {end_pos}, {score}") - return accession_id, start_pos, end_pos, score + accession_id = match.group(1) + is_complement = bool(match.group(2)) + start_pos = int(match.group(3)) + stop_pos = int(match.group(4)) + score = float(match.group(5)) + logging.debug(f"Parsing values: {accession_id}, {start_pos}, {stop_pos}, {score}") except IndexError as e: logging.error("Parsing failed: Incorrect format") - raise ValueError("Provided fasta entry name is in an incorrect format") from e + + return accession_id, start_pos, stop_pos, score, is_complement diff --git a/PhageScanner/main/utils.py b/PhageScanner/main/utils.py index 9df0759..f6d88a6 100644 --- a/PhageScanner/main/utils.py +++ b/PhageScanner/main/utils.py @@ -12,7 +12,7 @@ import os import subprocess from pathlib import Path -from typing import Dict, List, Union +from typing import Dict, List, Union, Iterator, Tuple, Optional, Any import pandas as pd import yaml @@ -202,17 +202,12 @@ def get_models(self): models = [m["name"] for m in self.config["models"]] return models - def get_model_features(self, model_name): + def get_model_features(self, model_name: str) -> Iterator[Tuple[str, Optional[Any]]]: """Get the model features for a given model.""" for m in self.config["models"]: if m["name"] == model_name: for feature_info in m["features"]: - feature_name = feature_info["name"] - if "parameters" in feature_info: - parameters = feature_info["parameters"] - else: - parameters = None - yield feature_name, parameters + yield feature_info["name"], feature_info.get("parameters") def sequential(self, model_name): """Return True if the model takes in sequential data.""" diff --git a/README.md b/README.md index a13cf77..e28b0b7 100644 --- a/README.md +++ b/README.md @@ -96,11 +96,11 @@ There are three fundamental pipelines in the PhageScanner tool. Each of these pi ``` - Example (genomes; though sequencing reads and proteins can be used as input) ``` - python phagescanner.py predict -t "genome" -c configs/multiclass_config.yaml -n "OUTPREFIX" -tdir .\training_output\ -o prediction_output -i examples/GCF_000912975.1_ViralProj227117_genomic.fna -v debug + python phagescanner.py predict -t genome -c configs/multiclass_config.yaml -n "OUTPREFIX" -tdir .\training_output\ -o prediction_output -i examples/GCF_000912975.1_ViralProj227117_genomic.fna -v debug ``` - Example using Docker (genomes) ``` - docker run --rm -v "$(pwd)/configs:/app/configs" -v "$(pwd)/examples:/app/examples" -v "$(pwd)/prediction_output:/app/prediction_output" -v "$(pwd)/training_output:/app/training_output" dreyceyalbin/phagescanner predict -t "genome" -c /app/configs/multiclass_config.yaml -o /app/prediction_output -n "OUTPREFIX" -tdir .\training_output\ -i /app/examples/GCF_000912975.1_ViralProj227117_genomic.fna -v debug + docker run --rm -v "$(pwd)/configs:/app/configs" -v "$(pwd)/examples:/app/examples" -v "$(pwd)/prediction_output:/app/prediction_output" -v "$(pwd)/training_output:/app/training_output" dreyceyalbin/phagescanner predict -t genome -c /app/configs/multiclass_config.yaml -o /app/prediction_output -n "OUTPREFIX" -tdir .\training_output\ -i /app/examples/GCF_000912975.1_ViralProj227117_genomic.fna -v debug ``` # PhageScanner GUI diff --git a/phagescanner.py b/phagescanner.py index ec0a16c..00d554b 100644 --- a/phagescanner.py +++ b/phagescanner.py @@ -9,6 +9,8 @@ from enum import Enum import argparse import sys +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' # prevent TF from showing logs. from pathlib import Path import logging diff --git a/requirements.txt b/requirements.txt index c0d3352..7b414b1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,7 +7,8 @@ pandas==2.2.2 pyyaml==6.0.1 requests==2.32.3 lxml==5.2.2 -swifter==1.4.0 +#swifter==1.4.0 + # used for testing black==23.1.0 @@ -22,4 +23,9 @@ tensorflow==2.16.1 # gui dna-features-viewer==3.1.3 -tqdm==4.66.4 \ No newline at end of file +tqdm==4.66.4 + +# plotting results +seaborn==0.13.2 +reportlab==4.2.2 +matplotlib-venn==1.1.1 \ No newline at end of file From 5b23e95d8158bf5b0d9e02745528d823c52bc2c9 Mon Sep 17 00:00:00 2001 From: Dreycey Date: Thu, 22 May 2025 22:05:30 -0700 Subject: [PATCH 16/17] additional changes --- .../main/pipelines/prediction_pipeline.py | 3 +- .../main/pipelines/training_pipeline.py | 52 +- configs/binary_pvps_config.yaml | 39 +- configs/multiclass_config.yaml | 58 +- examples/KR029087.gb | 2589 +++++++++++++++++ examples/parsegenbank.py | 211 ++ misc/parsing_proteins.py | 76 + misc/plot_results.ipynb | 120 +- predict_protein.py | 42 + testing.ipynb | 285 ++ 10 files changed, 3382 insertions(+), 93 deletions(-) create mode 100644 examples/KR029087.gb create mode 100644 examples/parsegenbank.py create mode 100644 misc/parsing_proteins.py create mode 100644 predict_protein.py create mode 100644 testing.ipynb diff --git a/PhageScanner/main/pipelines/prediction_pipeline.py b/PhageScanner/main/pipelines/prediction_pipeline.py index 838b1e3..b06321c 100644 --- a/PhageScanner/main/pipelines/prediction_pipeline.py +++ b/PhageScanner/main/pipelines/prediction_pipeline.py @@ -39,7 +39,8 @@ def get_type(name: str): if name == pipeline_type.value: logging.info(f"prediction type: {pipeline_type}") return pipeline_type - raise ValueError(f"Unknown prediction pipeline type: {name}. Must be: {",".join([predtype.value for predtype in PredictionPipelineType])}") + possible_prediction_types = ",".join([predtype.value for predtype in PredictionPipelineType]) + raise ValueError(f"Unknown prediction pipeline type: {name}. Must be: {possible_prediction_types}") class PredictionPipeline(Pipeline): diff --git a/PhageScanner/main/pipelines/training_pipeline.py b/PhageScanner/main/pipelines/training_pipeline.py index a9fc7de..59b9e3a 100644 --- a/PhageScanner/main/pipelines/training_pipeline.py +++ b/PhageScanner/main/pipelines/training_pipeline.py @@ -5,6 +5,7 @@ from pathlib import Path from typing import Dict, Union, Iterator, Optional, Tuple, Any import gc +from enum import Enum import numpy as np import pandas as pd @@ -18,6 +19,12 @@ from PhageScanner.main.pipelines.pipeline_interface import Pipeline +class DataframeColumns(str, Enum): + CLASS_LABEL = "class" + PARTITION_LABEL = "partition" + PROTEIN_SEQ = "protein" + + class TrainingPipeline(Pipeline): """A pipeline for training and testing ML models. @@ -69,33 +76,30 @@ def __init__(self, config: Path, db_directory: Path, directory: Path): def create_combined_df(class2filepath, class2index): """Create a Pandas dataframe from the combined partitions for different classes CSVs.""" dataframes = [] - + for class_name, class_path in class2filepath.items(): logging.debug(f"Combining class {class_name} into the dataframe.") df_tmp = pd.read_csv(class_path) - df_tmp["class"] = class2index[class_name] + df_tmp[DataframeColumns.CLASS_LABEL] = class2index[class_name] dataframes.append(df_tmp) - # Concatenate all dataframes at once - combined_dataframe = pd.concat(dataframes, ignore_index=True) - - return combined_dataframe + return pd.concat(dataframes, ignore_index=True) @staticmethod - def hybrid_sample_partitions(df, percentile: float = 50, interpolation: str = 'linear') -> pd.DataFrame: + def hybrid_sample_partitions(df: pd.DataFrame, percentile: float = 50, interpolation: str = 'linear') -> pd.DataFrame: """ - Balance classes within each partition by upsampling smaller classes - and downsampling larger classes based on an interpolated percentile. + Balance classes within each partition by upsampling smaller classes and + downsampling larger classes based on an interpolated percentile. """ balanced_partitions = [] - for partition, partition_df in df.groupby("partition"): - class_sizes = partition_df["class"].value_counts() + for _, partition_df in df.groupby(DataframeColumns.PARTITION_LABEL): + class_sizes = partition_df[DataframeColumns.CLASS_LABEL].value_counts() percentile_class_size = int(np.percentile(class_sizes, percentile, interpolation=interpolation)) balanced_classes = [] - for _, class_df in partition_df.groupby("class"): - if len(class_df) > percentile_class_size: + for _, class_df in partition_df.groupby(DataframeColumns.CLASS_LABEL): + if class_df.count() > percentile_class_size: sampled_df = class_df.sample(n=percentile_class_size, random_state=1) else: sampled_df = class_df.sample(n=percentile_class_size, replace=True, random_state=1) @@ -149,20 +153,18 @@ def get_kfold_training(df, model_features, segment_size): """ unique_partitions = df["partition"].unique() for test_partition in unique_partitions: - test_df = df[df["partition"] == test_partition] train_df = df[df["partition"] != test_partition] + test_df = df[df["partition"] == test_partition] - x_train = extract_feature_vector(train_df["protein"].to_numpy(), model_features, segment_size) - y_train = train_df["class"].to_numpy() - x_test = extract_feature_vector(test_df["protein"].to_numpy(), model_features, segment_size) - y_test = test_df["class"].to_numpy() + x_train = extract_feature_vector(train_df[DataframeColumns.PROTEIN_SEQ].to_numpy(), + model_features, + segment_size) + x_test = extract_feature_vector(test_df[DataframeColumns.PROTEIN_SEQ].to_numpy(), + model_features, + segment_size) - # up and down sample - # sm = SMOTE(random_state=42) - # x_train, y_train = sm.fit_resample(x_train, y_train) - - smote_enn = SMOTEENN(random_state=0) - x_train, y_train = smote_enn.fit_resample(x_train, y_train) + y_train = train_df[DataframeColumns.CLASS_LABEL].to_numpy() + y_test = test_df[DataframeColumns.CLASS_LABEL].to_numpy() yield x_train, y_train, x_test, y_test @@ -257,7 +259,7 @@ def run(self): # Step 3: Balance the classes with different partitions. logging.info("Step 3 - Balancing classes in each partition...") - #combined_dataframe = TrainingPipeline.hybrid_sample_partitions(combined_dataframe, percentile=70) + combined_dataframe = TrainingPipeline.hybrid_sample_partitions(combined_dataframe, percentile=70) logging.info("Step 3 (Finished) - Balancing classes in each partition...") # Step 4: Extract features and train. diff --git a/configs/binary_pvps_config.yaml b/configs/binary_pvps_config.yaml index 61d03c2..44da1e5 100644 --- a/configs/binary_pvps_config.yaml +++ b/configs/binary_pvps_config.yaml @@ -10,12 +10,6 @@ classes: uniprot: "capsid NOT cc_subcellular_location: virion AND reviewed: true" entrez: "bacteriophage[Organism] NOT (bacteriophage[Organism] AND ((shaft[Title] OR sheath[Title]) AND tail[Title]) OR head-tail[Title] OR tail fiber[Title] OR portal[Title] OR minor tail[Title] OR major tail[Title] OR baseplate[Title] OR minor capsid[Title] OR major capsid[Title]))" models: - - name: "PhageScanner_BLAST_binary_PVP" - model_info: - model_name: "BLAST" - sequential: false - features: - - name: "PROTEINSEQ" # only option for blast - name: "iVIREONS_FFNN_binary_PVP" model_info: model_name: "FFNN" @@ -23,32 +17,29 @@ models: features: - name: "AAC" - name: "ISO" - - name: "Feng_NaiveBayes_binary_PVP" + - name: "Feng2013_NaiveBayes_binary_PVP" model_info: model_name: "MULTINAIVEBAYES" sequential: false - features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - - name: "AAC" - - name: "DPC" - parameters: - gap_size: 0 + features: + - name: "PSEUDOAAC" - name: "PVP-SVM_SVM_binary_PVP" model_info: model_name: "SVM" sequential: false - features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - - name: "DPC" - parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC - gap_size: 0 + features: - name: "AAC" - name: "ATC" - name: "CTD" + - name: "DPC" + parameters: + gap_size: 0 - name: "PCP" - name: "SCORPION-partial_RF_binary_PVP" model_info: model_name: "RANDOMFOREST" sequential: false - features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" + features: - name: "AAC" - name: "CTD" - name: "DPC" @@ -67,13 +58,19 @@ models: model_info: model_name: "RNN" sequential: 3 - features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" + features: - name: "DPC" - parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC + parameters: gap_size: 0 - name: "DeePVP_CNN_binary_PVP" model_info: model_name: "CNN" sequential: false - features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - - name: "SEQUENTIALONEHOT" \ No newline at end of file + features: + - name: "SEQUENTIALONEHOT" + - name: "PhageScanner_BLAST_binary_PVP" + model_info: + model_name: "BLAST" + sequential: false + features: + - name: "PROTEINSEQ" # only option for blast \ No newline at end of file diff --git a/configs/multiclass_config.yaml b/configs/multiclass_config.yaml index 4613fa5..5052486 100644 --- a/configs/multiclass_config.yaml +++ b/configs/multiclass_config.yaml @@ -1,7 +1,7 @@ clustering: deduplication-threshold: 100 clustering-percentage: 90 - k_partitions: 5 # number of partitions in k-fold cross validation + k_partitions: 10 # number of partitions in k-fold cross validation classes: - name: MajorCapsid uniprot: "major capsid AND cc_subcellular_location: virion AND reviewed: true" @@ -35,55 +35,53 @@ classes: entrez: "head-tail[Title]" - name: non-PVP uniprot: "capsid NOT cc_subcellular_location: virion AND reviewed: true" - entrez: "bacteriophage[Organism] NOT ((shaft[Title] OR sheath[Title]) AND tail[Title]) OR head-tail[Title] OR tail fiber[Title] OR portal[Title] OR minor tail[Title] OR major tail[Title] OR baseplate[Title] OR minor capsid[Title] OR major capsid[Title])" + entrez: "bacteriophage[Organism] NOT (((shaft[Title] OR sheath[Title]) AND tail[Title]) OR head-tail[Title] OR tail fiber[Title] OR portal[Title] OR minor tail[Title] OR major tail[Title] OR baseplate[Title] OR minor capsid[Title] OR major capsid[Title])" models: - - name: "PhageScanner_BLAST_multiclass_PVP" - model_info: - model_name: "BLAST" - sequential: false - feature_selection: false # Options: - features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - - name: "PROTEINSEQ" - - name: "PhANNs_FFNN_multiclass_PVP" + - name: "PhageScanner_LogisticRegression_multiclass_PVP" model_info: - model_name: "FFNN" + model_name: "LOGREG" sequential: false features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 0 - - name: "DPC" - parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC - gap_size: 2 - name: "DeePVP_CNN_multiclass_PVP" model_info: model_name: "CNN" sequential: false features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - name: "SEQUENTIALONEHOT" - - name: "PhageScanner_LogisticRegression_multiclass_PVP" + - name: "PhageScanner_RNN_multiclass_PVP" model_info: - model_name: "LOGREG" - sequential: false + model_name: "RNN" + sequential: 2 features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" - name: "DPC" parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC gap_size: 0 - - name: "PhageScanner_FFNN_Hashseq_multiclass_PVP" + - name: "TPC" + - name: "PhageScanner_BLAST_multiclass_PVP" model_info: - model_name: "FFNN" + model_name: "BLAST" sequential: false - features: - - name: "HASH_SEQ" - parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC - vec_size: 1000 - kmer_size: 10 - - name: "PhageScanner_RNN_multiclass_PVP" - model_info: - model_name: "RNN" - sequential: 2 + feature_selection: false # Options: features: # Options: "AAC", "DPC", "ISO", "PSEUDOAAC", "ATC", "CTD" + - name: "PROTEINSEQ" + - name: "PhANNs_FFNN_multiclass_PVP" + model_info: + model_name: "FFNN" + sequential: false + features: - name: "DPC" - parameters: # DPC must have 'gap_size' parameter. 0 for regular DPC + parameters: + gap_size: 0 + - name: "TPC" + parameters: gap_size: 0 - - name: "TPC" \ No newline at end of file + - name: "ISO" + # - name: "PhageScanner_RNN" + # model_info: + # model_name: "RNN2" + # sequential: false + # features: # Options: "AAC", "DPC", "ISO", + # - name: "INTEGERENCODING" \ No newline at end of file diff --git a/examples/KR029087.gb b/examples/KR029087.gb new file mode 100644 index 0000000..75dd0f9 --- /dev/null +++ b/examples/KR029087.gb @@ -0,0 +1,2589 @@ +LOCUS KR029087 69171 bp DNA linear PHG 20-APR-2016 +DEFINITION Mycobacterium phage PDRPxv, complete genome. +ACCESSION KR029087 +VERSION KR029087.1 +KEYWORDS . +SOURCE Mycobacterium phage PDRPxv + ORGANISM Mycobacterium phage PDRPxv + Viruses; Duplodnaviria; Heunggongvirae; Uroviricota; + Caudoviricetes; Bclasvirinae; Pegunavirus; Pegunavirus oline. +REFERENCE 1 (bases 1 to 69171) + AUTHORS Bajpai,U. + TITLE Whole genome Sequence of Mycobacteriophages + JOURNAL Unpublished +REFERENCE 2 (bases 1 to 69171) + AUTHORS Bajpai,U. and Mehta,A.K. + TITLE Direct Submission + JOURNAL Submitted (27-MAR-2015) Biomedical Science, Acharya Narendra Dev + College, Govindpuri, Kalkaji, New Delhi, Delhi 110019, India +FEATURES Location/Qualifiers + source 1..69171 + /organism="Mycobacterium phage PDRPxv" + /mol_type="genomic DNA" + /isolation_source="soil" + /db_xref="taxon:1640883" + /lab_host="Mycobacterium smegmatis mc2 155" + /geo_loc_name="India" + gene 1..567 + /locus_tag="PDRPxv_1" + CDS 1..567 + /locus_tag="PDRPxv_1" + /note="gp1" + /codon_start=1 + /transl_table=11 + /product="terminase small subunit" + /protein_id="AKF12497.1" + /translation="MLVMTTNHRLIYLIGAPGAGKSTMMAHLTDCFDREHVPGTDTAP + AYDLLKYTDGEVAGVELGKRRPAFSGTDALPASIIEKAIPWLNTKPFRLVFAEGARLA + NKRFLDAAVAAGYEVHLGWLNHPDVDAWRAARSAEVGKVQSESWVKGRVTAVTKLVEN + YINRPERGVKVYVGGPEQLTSDMEHLLG" + gene 564..2351 + /locus_tag="PDRPxv_2" + CDS 564..2351 + /locus_tag="PDRPxv_2" + /note="gp2" + /codon_start=1 + /transl_table=11 + /product="terminase large subunit" + /protein_id="AKF12496.1" + /translation="MSYPDEPLSPIIEIPSPDAPDPDVWGEDGAYDERKAAVILEQAK + SWSTEQKHAMLASLRAAKARASMKVKYAHPAELAAALDPNYVVTPAIELISTSIERVL + TSPKQINLEISMPPQEGKSTLAAVATPLRALQHNPHRKIILATYALDLAETHSRTMRE + WIETYGTDVVDPLTGLPVEDKIGLKLARGANKVTAWSVAGGRGGLVAAGIGSRLTGMP + ADLMIIDDPFKNMMEADSALYRKRVDEWFSSVARTRLAPDASIIMIQTRWHPEDLAGK + VIAAERSLPRNERTWRVINIPAIAEKGIPDALKREPGTPMVSARDTPEAKRNFPKIRR + EVGERTWYALYQGSPRNPEGGIFQQKWFDATRLPEAPLNPYTAVVGIDPADSGEGDEA + GIIGGMAATIDKRLTAVLTHDRSGQYTSDQWAKVGVTLALEIGARVLAVEGYTTAKTY + TRVVREAYNAIHREARKKQLAGVPLTPVEHRALPDLPPFTIKPWRGANKADAVARAGG + MSQSFETGRARTIDYALATFEQQAVDWQAGQHQPDRVAAGIIVHDTIFDLMGGSMSIA + APPTGTNPSGGQGREIPPPPAWMRRSIGK" + gene 2435..2650 + /locus_tag="PDRPxv_3" + CDS 2435..2650 + /locus_tag="PDRPxv_3" + /note="gp3" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12495.1" + /translation="MIETTATSVTEFDGTFESVITWRFDGLWDVAVAHMGTWLCHAQV + AASFEDARFLAGYICGQHMRAESMVAA" + gene 2647..3009 + /locus_tag="PDRPxv_4" + CDS 2647..3009 + /locus_tag="PDRPxv_4" + /note="gp44" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12494.1" + /translation="MSRRPARAALRRWLHDRVCVGATSPDPYCTGSGADEHARTQDAN + ARRLMACSTREEVARELHHQACTVWIQQAHGGVYVELARNTCGPDGAHHVERLIGATL + ISELCEMIGIVDKTDLGG" + gene 3077..3214 + /locus_tag="PDRPxv_5" + CDS 3077..3214 + /locus_tag="PDRPxv_5" + /note="gp5" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12493.1" + /translation="MADQDFYFEVDGQRFDQRVEAMAHCDLYGYHYSAIQWVDPNAPW + A" + gene complement(3361..3765) + /locus_tag="PDRPxv_6" + CDS complement(3361..3765) + /locus_tag="PDRPxv_6" + /note="gp6" + /codon_start=1 + /transl_table=11 + /product="RuvC resolvase" + /protein_id="AKF12492.1" + /translation="MARRVKALVDQIQWVFAEDRPDLVVMETLAFAAKGEGVWVLPWI + FGKVIELCEGFDVELLCAGTSQVKKYATGKGNADKDTVMLTVAKRWPELAPANNNEAD + AACLAAIGCHYLGQPLGEPTAYQTEVLDKIGP" + gene 3764..4408 + /locus_tag="PDRPxv_7" + CDS 3764..4408 + /locus_tag="PDRPxv_7" + /note="gp7" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12491.1" + /translation="MARFDLSAVAAGAPTVTHSPCHASSPTEGGSDTRHMPVARSEVS + IPRTTGIPRSYPTATGNSRLPCMTIDNPATLALALVILMLAVARVTRLINSDKISDPI + RVAIASRVRHHTLIAAEAEAHGQSQRAAEAHRREARWAGVYEFVQCPWCIGMWVSFGG + AAVLVWVVKYSWTLDWWALLPVALAASHLVGVLARFADTEEIEIEDAAEDELDD" + gene 4438..6357 + /locus_tag="PDRPxv_8" + CDS 4438..6357 + /locus_tag="PDRPxv_8" + /note="gp8" + /codon_start=1 + /transl_table=11 + /product="portal protein" + /protein_id="AKF12490.1" + /translation="MAATSLRVVRRPKGSAPAARRRSLTAASQLITDPQKQMKTSLMG + TARNEWQSEAWDFSESIGELSYYVSWRANSCSRTTLIPSSIDPDTGLPTGEVDIEKDP + DAQIVADYVKGIADGPLGQAALIKRAVECMTVVGEVWIAVLIRQEKDPVTGIAGPRAR + WYAVTREEIKSKAGETAEILLPDGKTHEFNRDLDSLVRIWNPRPRKASQATSPVRACL + ETLREIERTTRKIKNAAKSRVMNNGVLFVPAEMSLPAAQAPIPEGQAQIPGAPVPEVS + GVPASEQLATMIYQASVAAMEDENSQAAYIPLVASVAAEHLEKVQHIKFGNEVTEVEI + KTRVDAITRLAMGLDVSPERLLGMSKGNHWSAWAIGDEDVQLHIKPVMDLICQAIYSD + ILTPLLAREGIDPTRYILWYDASGLTSDPDLSDEAVEAHDRGAITSAALRRLLNVGDD + SGYDLTTLDGCREFAADVVTKNPELIAMYAPLLSSQLAGIEFPQPAGAIESTREDEED + DEDSGARQQREPETEDERSTEEAASLNDRAAYLVAERLLVNRALDLAGKRRFKVNDAA + LKTKLRDVPAHEYHRVLPPVRSSEIPRLIAGWDTALEDEVVASLGLDNEKLRSAVLAT + VRRQLTQPLIEGEVV" + gene complement(6579..6764) + /locus_tag="PDRPxv_9" + CDS complement(6579..6764) + /locus_tag="PDRPxv_9" + /note="gp9" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12489.1" + /translation="MRDLDTLVTHTVTAHTRCLDLGLTINLRSTPPTNIDINVRQLRY + RYRNTGHCFYQGQRKQY" + gene 6735..8927 + /locus_tag="PDRPxv_10" + CDS 6735..8927 + /locus_tag="PDRPxv_10" + /note="gp10" + /codon_start=1 + /transl_table=11 + /product="portal protein" + /protein_id="AKF12518.1" + /translation="MRDERVEIAHPEVLRPEVVETITSVTDYERDEIVEAVERVYADP + YLRERAEDYVDTQRERVHRTVPMVQSKVEAAAREAAAAPAPERETELPTTTGDLTPDI + EVVITRQREAVAEVLTPGSNMLLEVAQLQGYQAAGVQNAAVIAAAQISPDAHLLDKVW + IATMDGKTRDSHFAADGQRVELTGEFTVGGAGLLFPADPTGPPHEILNCRCRVGILAR + DEEIPDEVDRHTERLNGRDSVQVNRQGSQQDEIDRRARKGVVRAREDTDGIGRVAAGG + WTAPSEQDYSMAKTELFRTFTDQPLAFVGIETSDGRMLAPEIEFSVRTPPLPAMWCKQ + TGEGHTEAYTVGVLESARIENGTVLGSGYWLNKPEADDAYADARHKVSRPSVDLAATE + WKLTVDGRELTEEEMFDLPPDAHVVQTITKAELIGFTMVAKPAFGDTVIEFNATRESR + DLAMVASIADDFRPRVYAADLFAQPNLAEPTALQMDPATGRIFGHIACFGACHRSVQN + ACVMAPKSPSNYAQFHTSPPVLLDDGTRLAVGRLTVGTGHADERLRPGPAMAHYDNTG + ACWALVRAYETSVGIEVSGVAAPWATPEQIEMGLASPLSGDWRDFGQGLDLIAALSVN + TPGFAVRGRTDDDGRPAALVASLAPARKRVIELSADEIGKIVARSVEQAFAAREAAEA + AAKAAQDEADNIEALVELAIEKVGEPPEPEPEKTPNDIVAELIEKAGL" + gene 8927..9190 + /locus_tag="PDRPxv_11" + CDS 8927..9190 + /locus_tag="PDRPxv_11" + /note="gp11" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12488.1" + /translation="MACGCGGGAKANSNSPGGDTLGYRAILPNKSVVPPRNEPPFFMA + ADAMREVTLARGGTVRRIRKSDPTDDYAVAKREAELAAAATTA" + gene 9304..11049 + /locus_tag="PDRPxv_12" + CDS 9304..11049 + /locus_tag="PDRPxv_12" + /note="gp12" + /codon_start=1 + /transl_table=11 + /product="major capsid subunit" + /protein_id="AKF12487.1" + /translation="MYKRPEQLPGTAAELDALLDAARADINVITARHKAGESLTPEDA + QRLKDLLSEVDELNGEKAKIAVADELPDLLAKADAATAPADDKTADESEGDDDGDAEG + DDDGEPEGETEGGEGAEQREPAEAITASTGAQRPQRRSPNFAGAGANDTPGEGDGGGD + APTPRWKLHPGAPGYREGMGTVGFADISQALEKIRPGSRAAIRPNRPSRNLDGQEFAR + QVVSTLDREVDVVGDSHALVAAITKATDQRNLPGGSLIAAGGWCAPSEQLYDFCDVPE + ATDLISLPEITINRGGIRWPREPDLSGIFEEFEWFFTEPELEATDPDTGKPTAVKTCV + EIPCADEFDEIRLNAVGWCVEAGILQEQGWPELVEWFMRSLTQEHFRALSRRTILNMV + AGSTGVTIPATSTMGAMASVLNSLALVATNIRLKRGLSRTATIEGVAPSWFHEVIRAD + LAMRAGGVEVFNVSDAQIQQALAARNIALQYVGDWQTRESGKPGNLATVDWPDTVDVL + LYPAGTWFRSMSNVIELGVMYPKEQLQYNRFTRMFTEDAIAVGKRCGESVKVTLTLDV + SGATGLPQRRTNLSA" + gene 11149..11946 + /locus_tag="PDRPxv_13" + CDS 11149..11946 + /locus_tag="PDRPxv_13" + /note="gp13" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12415.1" + /translation="MTTPTPPVLDVVHYEVPPVNPSPNGLFPATTWISDPDNRFFNGV + EVRGPNYGGEDAFGTWEGHYCSVPPTGEDDQRKDGTRPDILDAFDPITVWAYDECDPT + APSRAEVLARAAQILRLEEQVAIEREFANRMLTDAGTPAAAATLRLAVGYIEGVLALT + NTLGFIHIGAQWVATDPDLFIGNGGVKRSPMGHTWVIGGGYVDALGDTIVATSQPYGW + RNEPVTRGAIDAFENTYAAIAERTVLIGYEALIAAATITDTDEPETP" + gene 11958..12800 + /locus_tag="PDRPxv_14" + CDS 11958..12800 + /locus_tag="PDRPxv_14" + /note="gp14" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12416.1" + /translation="MPEGIIATVEDGFATIDFVNPDLRGPALAALLELGGPGTIETIS + RRGPRRLYRVPEGNAEEVGLLDGDEGPAKWSAGADTGRAAALKAADPNVVGGDDWHTP + ILEHSSRNAYVGTVANEDVLDRTPVYTGSAHSFGGHAVAPTSTDLIAGLDAIKYPQQP + EEVPPGEGEGDGTGEGEGDGIEGMSAQRVNLGLAEQTSGLASDPGATPEVGGEALGEF + TTVQSTRVEDAAAYPEGEPSEDWKRAELDAYAAAHGLDTTKLANKGEVLAAINEAADG + EDDA" + gene 12800..13189 + /locus_tag="PDRPxv_15" + CDS 12800..13189 + /locus_tag="PDRPxv_15" + /note="gp15" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12417.1" + /translation="MTGNDLSPRARLQALIPAKARETWYRVASAAVMFLLAFGILDAN + EAALWAQFAVGLVTLVFALIYAYTPARVALYAFVGVVGSVLLYYGITTEETWALITAA + VAQAFGIATAAAKTTPVVVARGQTSLN" + gene 13248..13547 + /locus_tag="PDRPxv_16" + CDS 13248..13547 + /locus_tag="PDRPxv_16" + /note="gp16" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12418.1" + /translation="MLTFLLLVVCIIATVVTDYFGEPPNYLVGLLGTAAGAFFAAIGS + DQQKKAADLAQTATEASTSAARAETTAKRAEHKIDEVLHERLDDDDASTKDGAGT" + gene 13544..14158 + /locus_tag="PDRPxv_17" + CDS 13544..14158 + /locus_tag="PDRPxv_17" + /note="gp17" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12516.1" + /translation="MSSVLEVVYSLPFAVGLLCGILGQRAYCYGRAWYKDRNDPLPNG + RHRTVAGISKVWVGGLIAVGSLGYVLYQAEATRLDTVSLAEHTQECTSDLIASVSRGR + QISTENDRLSIAHRDKLTELAQVQSVWLGRILDPPPHIAAMPADDPRRDGYFKTITQF + YKVRTDELRADIDKIREEQAKLIGDRERNPLPDPRCWPAGPEVK" + gene 14246..15046 + /locus_tag="PDRPxv_18" + CDS 14246..15046 + /locus_tag="PDRPxv_18" + /note="gp18" + /codon_start=1 + /transl_table=11 + /product="major tail subunit" + /protein_id="AKF12419.1" + /translation="MPGIQPVKGRRLRATKINSCGMPIAGPRNRLVTSGYVSLTLTPV + MRDATDLTQDNAEGKECFADRTAPERRWWTPALELCNVNTGLLTMFTGWETLVDANDL + PVGFRDQKEIESDFGVALELWSAGKMEEDCDEIPTSDDVLTDTSSGRSYGYFLFGGTE + WTPGDITISGAVTTFTLTGRTIAMPAWGLGPYNVQEAANGTARRLLTPTSKKEHLTVF + RTRIAPPEPTPGTEPVPLATSTLFTDPNFYYGGPDDEPAAVVAPAQSA" + gene complement(15106..15336) + /locus_tag="PDRPxv_19" + CDS complement(15106..15336) + /locus_tag="PDRPxv_19" + /note="gp19" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12420.1" + /translation="MTISRYAEADLAAAARRGVPGDQIRVARALLEVRAADTVAAAVA + GAAKIDLSPYQIRSLYPAGGLTYDYVRARLDG" + gene complement(15414..16280) + /locus_tag="PDRPxv_20" + CDS complement(15414..16280) + /locus_tag="PDRPxv_20" + /note="gp20" + /codon_start=1 + /transl_table=11 + /product="queuine t-RNA-ribosyltransferase" + /protein_id="AKF12506.1" + /translation="MRAYLYLGTHITSWLGHAGVRGAGQTRTVPMRAYLYLGTHITSW + LGHAGVPLFVSHRRLKRLRSLPRAIEVWALDSGGFSELSMFGGWQTTAREYVEAVNRY + DQQIGKLEWAAPQDWMCEPDMVHRTGLSVAEHQRRTVANYVELRRLWGEVSDEHCPFM + PVLQGYEVSDYAKCMSMYADAGIDLHNIELVGVGSVCRRQHTDEIRRVFEGILDTDPE + LPVHGFGVKSLGLREYGHLLTTADSMAWSLNARKHTPLPGCIGKHVHCGNCLDWALRW + RRGIVTSREIAA" + gene complement(16337..16600) + /locus_tag="PDRPxv_21" + CDS complement(16337..16600) + /locus_tag="PDRPxv_21" + /note="gp21" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12421.1" + /translation="MTTRLITDADLDRMHERQAAALRGVAVEDRDAVLRHAYFLMNVS + GGNLSGNLRTAVRHLAEAGSAEALMQQDRADNARLMGTAFRAR" + gene 16710..17447 + /locus_tag="PDRPxv_22" + CDS 16710..17447 + /locus_tag="PDRPxv_22" + /note="gp22" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12504.1" + /translation="MTFEWPINRAGLPVLPELTDPPSPEYLKALTERNAAESVAVAIM + WALSGRQFGTYETIARPCPTRPRGNPFAYRSDDLVWTGEGWLTCGCVGTGCRIVGPNV + VHLPGPVHEVTRVEIGGVDLSPAVWVAEGNKLYRREAPWPAQDLNRPLGDVNTWGVHY + TRGVPVPPGVSELTGILVKEILGALEDVGRCRLPRTVTTASRNGVTYRAYDPAVIYRN + GKTGLPEIDLWLATVNPNALMAAPTVI" + gene 17447..17965 + /locus_tag="PDRPxv_23" + CDS 17447..17965 + /locus_tag="PDRPxv_23" + /note="gp23" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12422.1" + /translation="MACRTDPALVIVGHATEALLDWFKDGACPPLVGSTTNVRFFAGD + STPLAAWDSHASQGCNEPFVWVRLMRRYRTQRFPDPTIGTDCNLPRVAPVEIGVGWCA + LTEQEPRWSDYQREAEVSSDTAWRLEEALCQASTRLKRDDEQRLVGTDALVPYGPEGG + VIAWTAVLYSTY" + gene 17982..18311 + /locus_tag="PDRPxv_24" + CDS 17982..18311 + /locus_tag="PDRPxv_24" + /note="gp24" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12423.1" + /translation="MTTVVIEGSILPCGFLARGERVEVQRTAHIDRLIEQGFVNVISE + IGDPEPAPLPEPVKAPARSASRDTWAEFLAEHTDIVTEGKGRDELIAEYDEWQSVHDV + PAADDSE" + gene 18315..18737 + /locus_tag="PDRPxv_25" + CDS 18315..18737 + /locus_tag="PDRPxv_25" + /note="gp25" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12425.1" + /translation="MATIRARARIEIDEAALERESGEHLRAFHRSLTRRIANQSRVAV + PVRTGNLGRTIGELPQVYTPFRVRGGVEATADYAAPVHEGSRPHAIRARNAQYLHFWW + HGREMFRKSVWHPGTRARPFMRNSAQRVVTNDPRVRMT" + gene 18788..19351 + /locus_tag="PDRPxv_26" + CDS 18788..19351 + /locus_tag="PDRPxv_26" + /note="gp26" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12515.1" + /translation="MATFNADGKTVTDEQLSPPAEYLPDLTDSEREADYQAEQARIEA + AIADAAAADDDDALPEVEVLTPPAGLATTDEQPGTEVVTFERFNVEEVENWSYDKLEF + KGDMLGIRLPTKAALAGFSLASSKYVSLGVKNDLTGLFIARHLSPESYGRVFSRLMDP + DDVDYDVDTVGELFNAIVTAAVESDDE" + gene complement(19416..19757) + /locus_tag="PDRPxv_27" + CDS complement(19416..19757) + /locus_tag="PDRPxv_27" + /note="gp27" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12426.1" + /translation="MTTITRTPEEIARNYEMHMGFLKDSVEELNELLGLTDAEHKATI + GYIGNARMGRDGWDYSDCRWSVFLPHPGRVGKDDDVIGGWREGSFEGVMAARRDLKTL + ITGVKLAKQIG" + gene 19877..25855 + /locus_tag="PDRPxv_28" + CDS 19877..25855 + /locus_tag="PDRPxv_28" + /note="gp28" + /codon_start=1 + /transl_table=11 + /product="tapemeasure protein" + /protein_id="AKF12513.1" + /translation="MTDVGKISLGVEINGDDLAARLGEAVRTAVAPALDKLNRKLQET + QRAYNKTGDSAEKSAVRQVAALKRVEEQADATRRAVERAAAASRLGGGGRGDSTAGVG + GNDNRRVDNRRIDNRRYDYRTYNDNRKIDQRRYDNRKVDNRKYDYSTKNDQSITYDYR + SYHYDGVPGPAPGGGGGGFGRGGGRGGGGGRGGVLGFLTGPVGLNTIAQGAAALPAVA + TGVVNIVGAVQQLTQAGLALPGIFAGGAAAIGTAVIGFQGLGDAIKALNEADADPAKL + EEANKALEKLAPSAAAVAREVSRLTVGPLRDFQKAIAQPLFEGLDKDIGTFADKALPR + IQPGMQKVAKAINGTFKEGLAALGDDKNLSLADQIFGNTAEGQQRANNAIRPLISAFG + TLAKEGSVFLPRLGDAITSVAERFNRFIQTNTNNGNIFRWIDEGLNGARAFGNTILNI + LKTITGLTKAAGQAKGSLSGDGGLLGALERVTERWSTFTNSVEGQQKLGKFFADGTAD + LHRYGDLLRTLWPTIKEIIAGFQTWGEIVLPIVSALASLTNTLAQIPGLLEAVVVGFL + AWRTLGPIIGGVANKLNAVGGAAGKLGGARGLLGGSLIAGGGAIQASTDSGLGQLAGA + LTQVAGGALAGSVFGPAGAAVGAGVGAAIAGITYVLNENARASRDAAAAQAELAENLN + RSRMAMELTSQATKTANDALLASGGAVDAATIGAVGDQISAIPDRLAGSYDEDTIKGV + ATALKDLSLTEQQLATIVTGAQPGFDALTARLREMGPGGAIAAQNLQAVRDATLGAAN + NAATAAPLLQNLAQTLGVDVPQAAAELTNAFSAVPKEVPINIDAPGGQAVFDMLVAIG + EKVSVDNNKNIVVEAPLAPAVLEQLKAIGYEVTQNNDKTITVRVDPQMYADTLAKLGN + VGNVLRDLHGQSLGLPAVPAGPNNTAGGLFPGMPGPTGPGGADGMVVPGYAPKQDTVN + AVLAPGEGVLIPEAVRGLGGAQAIYAINSRFRKGLSKRYYADGGVHGGTGALPGPAGI + ETELSVLVQIRDILAGKGGPASNPLAATAANTGAVATATKATGGQKLGPFGTPIKDRD + PAYEAAAAAISALGGDPEKFLGQNPVDYAAAQAAQATQFGGGLGVTGAVNMSAYIEAL + TAFAMTGDLSKVQGIGLNANSPVITALTSARNKAKGGLSDQQIADLVGTTLGPTGYTG + TLDSTNSSLIKSLTRYREQLMKQGGAAPTVGGTSAVAPALGIPMTALPAGAMDPISAY + AAAHSGGKYSWGASDLAAGLSDCSGAVSDLVELLTKGQATGERLFSTADAGSVLKSLG + AVEGALPGALQIGWDAGHMRATLPNGVAFESGGGTGQGATYGGNAKGAAGMPNIMSLP + VGALPAGLGMSGLPGGGVGGQGTPVYVTNWPGQGGGIPGLKNILGAGIGAAGQAGTDV + LGDIMGGVVGLGNEPWNTPGSYAALNTLVKEGNPLALAKAFGLDVADFSRAGGAAGEL + EKNEGRGYDASGRLFSDTGALLDRTFTSLNAQLQAMREQMVDVIEQVSQKLNDSALEP + VVKAGVQSALETLKDSVSASIGTALGQAAAPPIADAVSSAVSSLPIDQSGAGNVGGNA + AGVVTGALGMASGGPVSGGTPGKDSVPALLMPGEYVLNTSDVARLGGMAAIDSMRSRG + FKRFATGGGVIGNDTVGADFFGVSQVPIIGAIVNLLVRVLLKVIGVDIEVRDTLMEMT + DDFRQFRGDAFKAFDAQGRLLNDTSGLIERSQSSEETAAEERIRILKIVIQALIKYII + EKVIVPIAKAVANAAIQAGASAAGAAVNSQAPGAGGIVSSLISSAGQAGVDIAAEVGT + DFALAISETLIDVVGEGLKSLLPDLTTGLFGGNGLAFLTDPVGSLLGGLLGGIAGLFS + SLFGGAATMIPGIPFDNGGLAHGEGLMLKATSDPELVLNPTETDVFTRFVKALENGGF + GGGSNTTVHAPITVIGGGPETAEQIENRLLKHMP" + gene 25865..27298 + /locus_tag="PDRPxv_29" + CDS 25865..27298 + /locus_tag="PDRPxv_29" + /note="gp29" + /codon_start=1 + /transl_table=11 + /product="minor tail protein" + /protein_id="AKF12427.1" + /translation="MAFRGYFALNGVEIANSSRVAAHIGAEVPTRDIGLMTADVDCSL + TPIDDDRLLAELPATSVPIGAGRLLATPPDGSRLYGPGLAVVGDCWTPNTLCFGCRTA + IEYDDSWTGLPAFLNDHVYRPELAPWFTTRVPESAEFAGVWVMDVKGLDVTTSQREVI + EMAGDGGAAGIHRDGAQRIQFDVLLVACTNAGATYGLDWLTTQLRRTNDRTDSVLRYL + AAHPEDSAVDPTTLVRDRHGVVLTAGPEITGQVNASGRQHNQATFYRVTFELTALIPH + AYRPATVLPVEWDTVEVEPIQWVHSSECKPPADCSPMPVLFAQGCEVERVEAVSSPPP + VCGGCMPVCAVATHVVQIPLTERPQTGTTTVVSLAIRNTDARPLTLNGYFRRCNARDD + CDDELFPVQIHGLPATAEVVLDGVTGRFWVYYAGRKWRPVHIVGTPSGAPWVPAKLDR + SLCWEFIVVSDGTAMFEVDLALTDRDE" + gene 27295..28407 + /locus_tag="PDRPxv_30" + CDS 27295..28407 + /locus_tag="PDRPxv_30" + /note="gp30" + /codon_start=1 + /transl_table=11 + /product="minor tail protein" + /protein_id="AKF12428.1" + /translation="MSAPTRVLDRGGEAHRIVTAEQGVTIHTDNGTTIDQFTPRQYTS + CTWGLRLRDAGTADIVIPPTADYDRLRDIEPWAHSVTIWDVDSGTTLWTGPIQKARAS + RRGMTISAKDHSAYLSRTRNPLTKRWDAADPATVAGELWAAMVEAQGINTRPIVRVDP + EGDRYDFQVVADEQMLDQTLSDMNSQGLRWTVVAGTPIIGPVSTKALALLGEHDFLGD + GIEFVRDGSQTYNDALVRAADSNTARARVDYHGKNLQTIVNLDSMFGVSNVNRAARQY + VNHTGRPHTRLELTGGTELHPNAPVSMEDLMPSARFIIEARGVRQLFELTSVDVERRQ + GAVSVRVTMGTVEDDIELLDAANGKQQNMTLGGQRL" + gene 28404..30659 + /locus_tag="PDRPxv_31" + CDS 28404..30659 + /locus_tag="PDRPxv_31" + /note="gp31" + /codon_start=1 + /transl_table=11 + /product="minor tail protein" + /protein_id="AKF12429.1" + /translation="MTVELPGRAPTTDGELARQFHERIRRLENPRSVRVGPWVIATDP + ITGDLKASRPGQTLMLGGDQPVEVVEQSLSLSKFVTKDDLDAALDGIEAGGGDNDSVW + AQLYEKLTGILSPTNALNALANFFRLELGAPITSSRLPLLPLSHIRPISPNLLTDGSF + DYEETLSGFPDWDWDDTVGRNKPGSAYTVADGTTHTIHSNSIEVGTDDRLNVEVYARW + AGLVASGAAIKLDISCYRDDGSPVFSGPITLDSRTVSGTASGWTKLSVTNWDVPDEAR + HVVVELTVTSGATAGVVHFDDAGCFKIGTMPQSYISGLVEGLNALWQGIQARIDDFLD + LLDVFGGFNVGDHIGQLTDVVTRLQHLNPLNGLFDASKLGNIANIPMIAQERISGLVT + ALNEAGQGIRDAIVQALGGSGTGHSNIDVLNALMNIPASVVNTAIAGASNIENALQQA + IDSVIAGAGDLIGSGFGFADMIAQLTGLRRATAGTAAAVVNLQSQVANLDPAANSEVV + DFGEFANSASPPSMFTKVSDTGAGSIILTNGVLAWNSADAVGREFYLYNGGPLQTDLF + EVQVVLPSMPSHGIFGLDSTNYVYLIGRSDATGNNMVVARLGWDEVRVYSRNSGTMTQ + IGPTISADDILTSGSSVSFKGGTIADPRYFTVSINGDKVYEYSDEAPITVIGASNRFC + GLGLEKGNNYATGRISTWAMLDGGSSAGSGVVAGYTNAGLTNLILWKGTQAQYDALPT + KSPNGIYVVEG" + gene 30663..32009 + /locus_tag="PDRPxv_32" + CDS 30663..32009 + /locus_tag="PDRPxv_32" + /note="gp32" + /codon_start=1 + /transl_table=11 + /product="minor tail protein" + /protein_id="AKF12430.1" + /translation="MGVYIGDTPISKIMAGTSPFAGKVYIGSTQVWPEVEFPLVYEDV + NLTDALVPAGATALVIEQLVGGGASGLAGQNQSGAGTYTNRGGTGGGGGAVIGQHIIP + ISELGPTFSLQIGAGGEQSTSDTTRNNGGPTIFSSGSIVLTAGGGSASGGVASQSGQW + YVPMANGTAGGVNNTMGGAAGGAEGVSYAHSGNPQNDNEKWDALSADPAGTALQVLPG + GLKPGNGGSQSGTGQRPGGGGGGGKGRSSSSRGVGGRGGDGYARIYFINTAYKRDITR + VYTTAGAWTWTPPPWAVAGTKIDIVMFPGGRAGKNGATFGGAGRGGLAGTPVSATLTV + GTDIPVSGALTGVVGAGGASNGAAGGVSTCTTVGLTAPVNNADASGQDGGNAPNVTLN + GITYPGGRGGSSGTSSSAGEDGQDPGGGGEGGGSLIVGLAGGAGGKGRVYVRVYEV" + gene 32013..33173 + /locus_tag="PDRPxv_33" + CDS 32013..33173 + /locus_tag="PDRPxv_33" + /note="gp33" + /codon_start=1 + /transl_table=11 + /product="minor tail protein" + /protein_id="AKF12431.1" + /translation="MPFGWNPTPQITPRVTPRGWFKDTPELPVDREIGWWAVLTLDAI + DLGDAAQTMTLEALKALALANLAASSQSLELQQIAGISFANMIPPSQALALKKIALLN + LSGVALPTQALALAKVLGIALSSMGISTQALDLGKVVTVAMATDSPAAQLLTLTKKGV + LSLNSTATSTQQVVLARVASLIMSTSLATSVQSGLAVGFPPLTATTVTFTNTTNIQSW + TFPRNAEWLAVVCLGGGCGGRGGGPVLAGGGGYAGSYGTVLLRRGVDIPWSTTMFYMN + VGPGSAGSSGGYLVSDPSPGTASLCTTTALATLASGSGGGPNYSTQRGQDAPDLSYNG + ISAVGGKSSGGSATAGGVPGGGGNGANPGSFGLPGGAGGAGGRGQIWARAFQ" + gene 33204..33593 + /locus_tag="PDRPxv_34" + CDS 33204..33593 + /locus_tag="PDRPxv_34" + /note="gp34" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12432.1" + /translation="MAVGPTAYLVNKLLDHALRGVAYTPPSVIYFKAHTGDPGASGAN + NASAQTARVAVTFLAASGGTVLLTGTPEITLNATETITHGSLWDAAGPTGGNCLWTAQ + ATVSKGGVSGDIIRLSGFQVGFTGLAA" + gene 33602..33988 + /locus_tag="PDRPxv_35" + CDS 33602..33988 + /locus_tag="PDRPxv_35" + /note="gp35" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12433.1" + /translation="MTALTPTPDSQWAVQWEAFAPEDSSVMPERPILPEAPRSGTDED + TPENWAAYQEAYGIYVEAMASYESLLAVLLADDANWRTITNGFPDEALARQMVGIIRS + ANAGNPQTRDFRLVWSAPIVWHIADE" + gene 33988..34635 + /locus_tag="PDRPxv_36" + CDS 33988..34635 + /locus_tag="PDRPxv_36" + /note="gp36" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12434.1" + /translation="MSTPRVCLSENLAVSPDGKLRLPRWSVVRNVGDIVIASAGDTAK + LLISDTLPGRKLIEGRLAWTNDSPVEQQLRVEVTRRYRRWVTSNPNAIEFRDRWSWVV + IPEGDPLIDPAEPDVSDTFNGKTGSAGDIQTNTVAEPLPGVFRHWWGTTTSEEWIPDV + LAPGDTFSLWYRAYVWTPPPWSDNANKNAPTHAAEAGYSRLQLTAYPAQGKVVVG" + gene 34632..35231 + /locus_tag="PDRPxv_37" + CDS 34632..35231 + /locus_tag="PDRPxv_37" + /note="gp37" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12435.1" + /translation="MSLKLCTGEYMLSTVAGLGPSRGWFPRVLAEQMLESTKDGEIKL + SPDPVTMIDGDLTWFNNSDDRVRVFVLVHTAPRDIIAQNPSTVVIHDAWSHRVGRAPS + ADFPSAIRNSFGGRLQLDRASVARDAVKFGRFFLTGDDQQAWVDLGIVRPQQSFHFRY + RAAVQTPGTWVTPSELEPRWEAYARWTRLVALGGPVGES" + gene 35228..35851 + /locus_tag="PDRPxv_38" + CDS 35228..35851 + /locus_tag="PDRPxv_38" + /note="gp38" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12436.1" + /translation="MTSPDCIDPDHFRVTADGGIEPQPWMQWRHVRSISAEGRSGTYA + VTGGGNKNVLIHGLQVSYTNDTPVPQAVYGKITRGGCRVALQARSRAYLQVSSGYQKH + ASDPGKLEVSSRMGCGADIGRGGTLGIGTQFCVIEQRQGMVTIPLAPERAGWLTLSPG + EAVTARVEVKFISEFWENTNIDGGASGSTSGYDSGATQLDLFAVPVL" + gene 35885..36253 + /locus_tag="PDRPxv_39" + CDS 35885..36253 + /locus_tag="PDRPxv_39" + /note="gp39" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12437.1" + /translation="MYEPPPGYDDCESDADDFPTPANAAYRELDVEGVGILHARKPLP + NAIPALAGAANSKVKPAARLDYLTVFIQNHLAPGEYETLLAKMMDPETELPEDTVLRV + SRAIATAGSARPTRRSSTSR" + gene 36253..36564 + /locus_tag="PDRPxv_40" + CDS 36253..36564 + /locus_tag="PDRPxv_40" + /note="gp40" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12438.1" + /translation="MAAHNWRALRLKALGQGITNIMACPSMHVVLDLIEQLGAEASVS + EAKSQTEARSELTRYYDKLYKPDITAKVINGESYFPPPPGFSDEEVEASFDAFLSNPL + R" + gene 36604..37011 + /locus_tag="PDRPxv_41" + CDS 36604..37011 + /locus_tag="PDRPxv_41" + /note="gp41" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12439.1" + /translation="MAVTAVLFETSSTRGNRLDPDVRDEIEALAPGLETGEVTTDKIA + NDAVTRAKIDAGAVGSVELDDDAVKAQHIDDGAVGTPALAAGAVTSEKTGIGVVTAED + ASGNPVEVKQVYMTVAQYNALATKDPNTDYYLS" + gene 37011..38132 + /locus_tag="PDRPxv_42" + CDS 37011..38132 + /locus_tag="PDRPxv_42" + /note="gp42" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12440.1" + /translation="MATIRRGAAAPYKAIYHGTTPIKQVRRGETLLWSQGKIRDDFTD + ILDRWLNELLSGDLGALCSDVTGVLRDGLGNIVGHTVAFVEDNINGLGKLVAQTGQDL + ATAYCGVWGGTAAPNGLIGLINGIPIFGGILADWLKGELDIVSIIGSIPIIGDIGRLI + GLIPDALGHLADPINYVVDAFGEVIGTITCGQFRPHGGDNEGICYTIGVLGDIAKMLI + PDGLLTLNRHTSRLRHETLLDGDDGYLEVQVADLGAPDYPTQVFRRYANNGSGATGVG + MQFMNSQVSLVRRVGGNNTLVLPQVASYSPGDVMRLEQFGDIHSITRNGETVGVWEDN + SATAAKGVNNRSVAMIMQGAKELNTSRKVSPGLAYLEAA" + gene complement(38114..38332) + /locus_tag="PDRPxv_43" + CDS complement(38114..38332) + /locus_tag="PDRPxv_43" + /note="gp43" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12441.1" + /translation="MHRASSVELYGGPLDGERRVVPDTQHELTVWTPICPLPGQKPGL + YRSQPHKYARRRDEPWLFGYVTDQAASR" + gene complement(38334..38582) + /locus_tag="PDRPxv_44" + CDS complement(38334..38582) + /locus_tag="PDRPxv_44" + /note="gp44" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12442.1" + /translation="MTTAGAVGLFMLFISYLPDNPHPMSTALVGAVCIGIAVRLRYLG + QLDKAAYRPPQPPAQHPVGARRERITHERRTRERIWFD" + gene complement(38621..38836) + /locus_tag="PDRPxv_45" + CDS complement(38621..38836) + /locus_tag="PDRPxv_45" + /note="gp45" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12443.1" + /translation="MTDQTHEAPPVPRYLSRDEVAKRLGMKSVRSLSGIELPPPDVEV + GKHKGWLPETIDAWHATRPGRGWWGGR" + gene complement(38833..39258) + /locus_tag="PDRPxv_47" + CDS complement(38833..39258) + /locus_tag="PDRPxv_47" + /note="gp47" + /codon_start=1 + /transl_table=11 + /product="HTH binding protein" + /protein_id="AKF12444.1" + /translation="MTTTETAAEAPVEVYPPTYTEGLGELVRAHRMYLGLSQRDLAAR + LGMDRRDYQHIETGRDKCPPGLISKLDALSDEFANQVDKVIAEAIASGGITIAVSNSG + EPQDEWDRLVAGRAAIETDADAPITLTIVGKQTTGRFAR" + gene complement(39255..39491) + /locus_tag="PDRPxv_48" + CDS complement(39255..39491) + /locus_tag="PDRPxv_48" + /note="gp48" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12445.1" + /translation="MRTLLVSAAVFASLTALGWTVYYDDPNPWGGWFAPAWFASLGAF + LLILSIVTIVRAVDEIANPSHCFTTTTTPRRYTP" + gene complement(39496..40056) + /locus_tag="PDRPxv_49" + CDS complement(39496..40056) + /locus_tag="PDRPxv_49" + /note="gp49" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12446.1" + /translation="MATPFDTIAPATASDAAIALLTKLMMAKAKATGADTAEAAVKIM + AWLETHNDSATVSANIDRLKAEGYAPEREIVAGADEVPAGRYAIDTTIHAINGTAFYK + VDRPTEGRWAGYVFVKQIVGDEERRLSQKQGGTILRRIAEVGAEAASARYGHEIGECG + VCGRTLTNDESRERGIGPICAAKAGW" + gene complement(40106..40183) + /locus_tag="PDRPxv_50" + CDS complement(40106..40183) + /locus_tag="PDRPxv_50" + /note="gp50" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12447.1" + /translation="MGMYLGIRVTVDVPDERVDKSDKGG" + gene 40182..41471 + /locus_tag="PDRPxv_51" + CDS 40182..41471 + /locus_tag="PDRPxv_51" + /note="gp51" + /codon_start=1 + /transl_table=11 + /product="lysin A" + /protein_id="AKF12448.1" + /translation="MRAGTYTLSSGFGPRWGSQHRGLDFAAKDGTPIYAAQGGTVAYI + GRADGFGQWIVIDHPAADGGGTTVYGHMWDAFATGLRQGQRVEAGQLIAYVGTNGQST + GPHLHFEVHPTVWRQGSQIDPKPWLANARNPGDPAPAPAPPKGGTLAKLTDPFTGELW + SPNRYHPRGLGDPRWIVVHTQEGGRTARDLAAYLAQKSSQVSYHVVVDDREVLKVVAE + GDAPWAAAGANKYAFHICMAGSYASWSRNKWLDVDTSDGKNEDLQLTKTAHVIAWWCD + KYGIPPVWIGGRNIPPWGLDGVCGHVDLGAWGGGHTDPGPNFPRDELMRRVNQFLAGT + ELPPLPTPPPVTVPGTKPDQYGDWMLYRGNPRNDADRVRRVQRRLKAAYRSYAGHLEI + DGDFGPLTELAVREFQRRSLLIADGIVGPNTAAALKP" + gene 41481..42836 + /locus_tag="PDRPxv_52" + CDS 41481..42836 + /locus_tag="PDRPxv_52" + /note="gp52" + /codon_start=1 + /transl_table=11 + /product="lysin B" + /protein_id="AKF12449.1" + /translation="MTQPTAWQPPQNVGDVAVTVAQAKAKLKVFSYGAAFKTETSNVY + TAEFGTALRTFQQRRNAEIHEGKKPGPVMNTDGVLDWATKKQLGILPEQTAPAPPPIP + ANRAAALVFRGTGGIIGQDYVSQVCQQVGPMVEEINPEFPASMGGLPPGAPNLPSARQ + AIDIGYRSGAAWIKANPSRKFVLGGYSLGEIVVAKLLTALFSPGGELAAFRDNYVCSF + HIGPPARPLGGAFYGGTAAPGVGIASNRLAADIYAQLGPRACYLCDPEDMYGSIPVPV + EGGTGDIMETVYDMVTTLALNDFLNTAAAMLPHILEIAQDAGIFGLLGLGGVGPATGG + GGLLGGLLGGGLGGLLGGGMANPAALLTNPLAAIPLLLPLFTSALPGLIAGVGGPGTG + GAITGPAAAAQAAILGMKFLFAGTRPHIEYHIREVWPGQTYIGLAVQHVRDWVGRELP + A" + gene complement(42938..43558) + /locus_tag="PDRPxv_53" + CDS complement(42938..43558) + /locus_tag="PDRPxv_53" + /note="gp53" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12450.1" + /translation="MAGSPFSGKGGAATAAAPAPSKGKVDPADLPDVTNVGEDKPVGS + DPYAAADPTGVSGYKPAFFLGQLVLMHPTEYGTMATVHDKDGKESEFVRFDIIPLTVP + EPGTPNAQTAVRLDNGNFGFLNRDGEVEECEPYEVGERLDDIMVFNKALAREGKKALD + RGIAWVLGRIVKGVKKPNQSAPFILVAGSEEDKALYQQWRQSLAKA" + gene complement(43616..45019) + /locus_tag="PDRPxv_54" + CDS complement(43616..45019) + /locus_tag="PDRPxv_54" + /note="gp54" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12451.1" + /translation="MVSVARQFPAWRQPDGSVWFRPADGDGWTADPEQAHADYRVVIA + PDGSVLTWDGDPDAPPTGPTAPQTHETQQPRQETATMTAIDENGDFYPRHTEWMRYPL + PPEPPRPPSKYNGRHQYMLPHPETGRLTSYPRATTVAKTLEDTTGLAKWNRRNVVGAV + LNLVHREITGKANEPMGPGGDYTAAALLAELRDVYANHTKVGEIDRVIETIDNVTGGA + EAREFGECVHAWLEALDLGMVLMRDLPDMVRPHALAYRRVLAHRGLVALPEYVERTVL + NDLGEEATAGKIDRIFQIVSTGELVLGDVKTTKVDSMQYSWLAFAVQIGGVYSWAPHV + LNATGDGWERMPKLIGIPHPDDETDDERDPFAILVHVPSDHPEKASAITIDAVWGAEA + LIESSTTRRMRSRAKTEVPRHAVPAPTKQSVRYAEARLALTAIESAEDGERVYTEYQD + VWDDTLTDFATTVAKLL" + gene complement(45022..46818) + /locus_tag="PDRPxv_55" + CDS complement(45022..46818) + /locus_tag="PDRPxv_55" + /note="gp55" + /codon_start=1 + /transl_table=11 + /product="helicase type III subunit" + /protein_id="AKF12505.1" + /translation="MKPKPAQVSRSPHQHQQHTSNTPTPHRKHPNMTSPAPRELRPYQ + REAVEAIESHWSQGVTRVGVVLPTGTGKSTVIGRTAVNGYQNREPVLMVAHRGELIDQ + MAGTIFEVDPSIPRSHVGIVRAEMDDHSAPIVVATLQTLATAHRREAVGFRRRILWDE + VHHAGAEGFHTTFTELGGYTDALFAGFTATMRRDDKGKSPVGLGDVIEKVVYEKDILW + AIDSGYLVRPRGLTVRINNLNALDDVRTVAGDFQQSDLAEVMEAATEYVVDAIKLHAA + DRRPIIFAASVDAAHHIADALTAADFPAVAVTGSMSYAERQPVYEAYRNGTAKALVTV + QVLTEGADFPMCDCVVLARPTRSRNLYSQMIGRALRLYDGKQDALVLDLAGSSRSMKL + VNLTQLVPGVDAAEVTEDGSVIEIEPDDELPGEGSDPTPKLVRQGPVEMVVIDLLSGS + DVTWCETTLGVPFIPLMDDEIVFVWPKDGYRPLDANATSWAVATMSTKTGRGGWVSGS + GAVGVAEPDYIGLEAACEAAEAWIVNSGKRLPSKKDSWRRKQPATAKQIAFARGLGIV + GADAMTKAELSDEISTVKISRRLDRNITRIEA" + gene complement(46843..47202) + /locus_tag="PDRPxv_56" + CDS complement(46843..47202) + /locus_tag="PDRPxv_56" + /note="gp56" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12507.1" + /translation="MLLDAAGLTPPEDLARLHRIIACERFVSALADSGCTITRISAIS + DRSGLAEGESVTGNPRFVAFEALMGAAEDRAVQCVRCGNAASRHGERPQDSPPGCGWT + YSMVVRAAGVDNPEQFG" + gene complement(47244..47633) + /locus_tag="PDRPxv_57" + CDS complement(47244..47633) + /locus_tag="PDRPxv_57" + /note="gp57" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12508.1" + /translation="MHDYPRPNHDLITAAREALKPIRELHRPRWSNCINACCSGEDCR + LRDRVCEHCEVDWPCDTAKLIYTTEAVTMQDCAGSGKPFKPGTLSRDGEVAKCSACGT + NRYVRDDGSIEPHQVPVVAVVAEGEDK" + gene complement(47686..47901) + /locus_tag="PDRPxv_58" + CDS complement(47686..47901) + /locus_tag="PDRPxv_58" + /note="gp58" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12512.1" + /translation="MSDIYVVTDGSRVIGASTKMQGAELIRAEYASAARGFWKAHGRR + PDEQADEAERETYARVKIQNTELTDDG" + gene complement(47898..48059) + /locus_tag="PDRPxv_59" + CDS complement(47898..48059) + /locus_tag="PDRPxv_59" + /note="gp59" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12511.1" + /translation="MTAVDLDTARQLIGDGVPSTGALPAAWWTRMPLSTLAKLMEEKA + ERLKTGTEE" + gene complement(48056..48427) + /locus_tag="PDRPxv_60" + CDS complement(48056..48427) + /locus_tag="PDRPxv_60" + /note="gp60" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12510.1" + /translation="MVSRRLKAALAAVAMTAAGALAAAPAASADEIAGCKTEMWILWG + SNTRTLCDTPRRSDGSWTRYREFWSPSYYKPFTCTTYGSRYYASTSCDGGYVVPRRSK + GIEVYPVTDATVLHDEPGWLG" + gene complement(48421..48711) + /locus_tag="PDRPxv_61" + CDS complement(48421..48711) + /locus_tag="PDRPxv_61" + /note="gp61" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12509.1" + /translation="MKTLIIAAAAALGVALAPAGIADAAPKMCDNHGFGKGQIYKHAC + ASGNGGSAVWVTVYNEDGTPKKVMTEDGLKTVKRCNIRCGGGRHHVETTDPW" + gene complement(48788..51535) + /locus_tag="PDRPxv_62" + CDS complement(48788..51535) + /locus_tag="PDRPxv_62" + /note="gp62" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12452.1" + /translation="MLGSTPITAILGAGIDNRDPEAVRAFVRQAADIGLSIMFIAPDS + KAPVDLRTPQKKKADDKAAQEAAREAGRRDWARVKSPAGLTLATSDKAVLDRYLKRYL + TLYAERYPEGVPVNVAVEVGGSALVVVDCDTSAQLQRFYEAALPEDIDIDDVPPPTVV + TPGHAGKDGDLADPSSWAHSDGGHFYFTVPDEILPTLPRNLGAMTWGGEDGFAVLWDK + RYVLIPPSTRPEGSYEVVGRDYPITDAPWLLDAIREAAARRAERTERVHRDSEAGELS + EAIDGWAEATSWTAILEPLGWTPAPRADACGCPVWTAPGVHASPKSATAHDTGCALGR + YTEVNAPLHIWTDNPGEPFEAWISDKGTKTLSKLQAVALIDYGGNIGKAMDEIGLTPD + LAVEVDPDIAPKQVDPDHAMTGDGDFELPAAPEPETTSTPDDPPFCPVCDTEAGAFVV + DGDGDMWHMPEGGSAGDEHLAEPAPATYAHDASCNTAHTAGPEPCPTPTSYADDLSDL + PADFGKSEPADEPSPYAAADPEQTDPDVYDSPHSGVPRIAPFSHWRDMPPPEYVIEGL + LEHGGLTSIIGPPGIGKSSVVLDMACHIAVGKRWQGRRTLKTKVLYLPGEGLAGAVQR + LRAWEANHDVDLGNDLMLGDSIILIKASNEAWGDLAAYIARHEIGLVVFDTFARQSSG + LEENSATDVGVAVRRYDKLRELTGAGVCVVHHTSKAHTDQGRGSSALNGALDSELVVR + AATWDTEAAMIDGRLPGRPMEVMTTKQKNAEALEEAIALMMVSYEDEDAGIKAPLITG + PNGSVDPMQGEIVLARPRPEAVIETAVRIRQFVNDLPTQGATRAEILTAVQADAYARS + RRDSAAYWKLHIQRAVDMGLRFNLIETLTGTASGSRYIPGQVGIEQARTLAAAEVLDG + D" + gene complement(51655..51984) + /locus_tag="PDRPxv_63" + CDS complement(51655..51984) + /locus_tag="PDRPxv_63" + /note="gp63" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12453.1" + /translation="MTESCQHHYGNTGDGWLTCVKCGAAPAVPAGHLSSRFPVLLPAR + PSEYDEDEPVWNHYPFIRVKSDSIEIGPRSEDTYTLTVTEAEVFAADLLAAVRTVRSR + QQTSERP" + gene complement(51981..52502) + /locus_tag="PDRPxv_64" + CDS complement(51981..52502) + /locus_tag="PDRPxv_64" + /note="gp64" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12454.1" + /translation="MAEGMTIKPCTCDVGGYCPMHGCPHKTDDRACRCADELHTIRGQ + QPTEVPVERPKPGTVGIGTPPQLKGAPERPEIPPTGQIAEDIAREAIGLFNGPRQATY + GDAADNFGVTASMWSAILGTEVTAEQVALCLATLKIARLRETPDHHDSWVDAVAYIAL + GAGVMIRHKAGRP" + gene complement(52487..54346) + /locus_tag="PDRPxv_65" + CDS complement(52487..54346) + /locus_tag="PDRPxv_65" + /note="gp65" + /codon_start=1 + /transl_table=11 + /product="DNA polymerase I" + /protein_id="AKF12455.1" + /translation="MTDIIDAPVRVPSRSWSDPLLGARMHTGEHAVRFGAEFVRQATR + GPANTIALATDIETPGLDNSFTINCVTMSWAQPDSGRVHSILLDPRRDEQHAALVAEM + YALAGPVILHNAPFDIPPLFHAGLMTGEVIKKRIVDTVVLARFALPEPPPYGQAKTLT + ALAIRYLGLSEFKGGMERAFKAAGYKTQQAGYEGMDIDSPIYRQGAMADTVATLALEP + VLRRMCIAWTLDHPFEVYGATTVAEAEAILDTQEIVHRMMLWRSALGINVDREYLDRY + LNQVADEQAIYTADLAAHGLDGGTGKGAKLVQYLADLGELPQPWPRTPKGALRATKAD + LEHLAEINPLAKAQLSLANIEKTLGYLTKVSTQAAVTGRCHPQVATLGASATGRMSYG + SPELQQFPKDARPILCDDGDGLTSIDWSQIEPVTMANMARDIDFLAAYEAGEDLYGPI + MLAAGIDRPTSKVVLLSSMYGSGIAKLAALIGHTEESTAQIKRQMFAAMKRSARWMVK + VEEVAAQFGRVITAGGRILPVDEGGIFKAVNYTVQGSAYDVLANTIVEMYRRGMTDEL + YLAMHDEVVVATTVSAEVQEIMSTPPEFLIRWAERTPVLRTDRADMGSQWLKV" + gene complement(54343..54807) + /locus_tag="PDRPxv_66" + CDS complement(54343..54807) + /locus_tag="PDRPxv_66" + /note="gp66" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12456.1" + /translation="MLEIWDQDHLESLPDGTIIEWQRIIGDETSTAVAYVRSEIEGAE + THAIACKFNECLCERVVWISPGGWQPLTPEQAGINYPARVVRIGPGNDDRVVTPSAPP + AQSPMSVRMAALNAAARVRAADAININSRVPADLVTELADTFVTWLQGGATA" + gene complement(54800..54925) + /locus_tag="PDRPxv_67" + CDS complement(54800..54925) + /locus_tag="PDRPxv_67" + /note="gp67" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12457.1" + /translation="MRLRDHVVTRDPDNQQPQEGTTTMTAKPTQDHPCITEARDA" + gene complement(54901..55053) + /locus_tag="PDRPxv_68" + CDS complement(54901..55053) + /locus_tag="PDRPxv_68" + /note="gp68" + /codon_start=1 + /transl_table=11 + /product="RDF protein" + /protein_id="AKF12458.1" + /translation="MAREICQDLADDVFDGDEQWCDITGEHHVCVMTDGHIARQAHEC + DCGITW" + gene complement(55053..55409) + /locus_tag="PDRPxv_69" + CDS complement(55053..55409) + /locus_tag="PDRPxv_69" + /note="gp69" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12459.1" + /translation="MFTDPRIRSAFLAGVATGAALGVTVGAALVLTGLPADVSIPHPG + LSVTETVATKQFAMWPADPAPTPEDDPAFDCRYHGNHICGPLNVDGYAPGLYIDGVLV + DPWPVDPASTPTIEVP" + gene complement(55522..55881) + /locus_tag="PDRPxv_70" + CDS complement(55522..55881) + /locus_tag="PDRPxv_70" + /note="gp70" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12460.1" + /translation="MTDNLPAPTGPRLRNAASTPLSQAPTSVLAFALGYAAARPQHAN + TFGPFCAMVEELMTRPSGATDPGPLGGYDQRTVFGSIMATIPPETARQLAMLYQAQRG + QRWASTGHREGRAPGGK" + gene 56336..57100 + /locus_tag="PDRPxv_71" + CDS 56336..57100 + /locus_tag="PDRPxv_71" + /note="gp71" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12461.1" + /translation="MGTKTSPVRLDMDLDEFTGHVNDLLGYDTQLGENIRIWRRSRNV + INTLGSGSAFAKELNAAMDAAVGSVGVAEGLCHGTNKRVYRRHMAAHTKTVVRSEAVK + KRCPELWLAARKPSFRMGCDTKTSPVAVPKIRPGYVGKLIEAKTEAAQRLKYAREHEL + RVKDHLYTTTFGIEAEDGWTGNPPYALSDGWVIGWTKTQRYSEALAKEKAPEFGVDLT + EITEYVTVPPRVFYVVGDLTGTEGSLEDYEGDGYAD" + gene 57235..57414 + /locus_tag="PDRPxv_72" + CDS 57235..57414 + /locus_tag="PDRPxv_72" + /note="gp72" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12462.1" + /translation="MLWPIRGPITIRQRMKIACAEEGLTYAGLIEKFLDERDEKIRRQ + LAAQKSPLHRPKREI" + gene 57417..57593 + /locus_tag="PDRPxv_73" + CDS 57417..57593 + /locus_tag="PDRPxv_73" + /note="gp73" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12463.1" + /translation="MTELNGMTDDELRARLGLTPVDRPQTMEERIEAHVVRAMPEPED + CGVCDGKGCGDCYA" + gene 57577..57801 + /locus_tag="PDRPxv_74" + CDS 57577..57801 + /locus_tag="PDRPxv_74" + /note="gp74" + /codon_start=1 + /transl_table=11 + /product="putative HNH protein" + /protein_id="AKF12464.1" + /translation="MTAMHEALICKHCRTEVLPAGDGSHRHRRTNKNTCEVEPYGFHA + EPIGTSCGDHPANSCLGASGAVPLRGQSFT" + gene 57798..58505 + /locus_tag="PDRPxv_75" + CDS 57798..58505 + /locus_tag="PDRPxv_75" + /note="gp75" + /codon_start=1 + /transl_table=11 + /product="Rnase E" + /protein_id="AKF12465.1" + /translation="MTDTPQTDTDEVDTEFAELVAASIEERRGRVLLMRLAGSTFKTI + AQAVGVSIGTVRKDYDIALAAHLDDPPDMMIARQRAVITDIMRANYPSMLNGDKEAAQ + TILKALDREAKLFGLDAPTRVLAQVNNDDFAVEAARLIEHIQKIDPNELKELARAGQP + QQPRPQDRPAAQDTAVGPLDVELAEDGAQGPERDPAGSIDTDTSEPDVEPAAAEPVAE + PDDDYDDWSNLDDDDIA" + gene 58498..59079 + /locus_tag="PDRPxv_76" + CDS 58498..59079 + /locus_tag="PDRPxv_76" + /note="gp76" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12466.1" + /translation="MPETGPLIKHTAVLSERHLVVVELPGRHHDVLPSEFPEIVSACE + RRARAWADAHGLELGGLGVYSSASCEGKPARVGVSFEVVVGKGEIDADQSPAGHLDRW + RWSGGRLDWWRNVFNRSDQREGRKLVAREEGDNIVAFPRLASVDWASAAPGGAGGSGA + SGHTGQKSSQVSYHAVVDDRNIGELDRPEDGAQ" + gene 59076..59498 + /locus_tag="PDRPxv_77" + CDS 59076..59498 + /locus_tag="PDRPxv_77" + /note="gp77" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12467.1" + /translation="MSGDLDRQRMWNNGHTDDEVIEGTGAVFDATVLYGQTDDVDEHL + ADAVEAGQLSGDPAAVDARDERAFLASMRDNTDVTVRTLGVLGVWLLDNGVAHPYRGA + RALLSELAEYDLQTVIGADLDTFIGEIQRLRDERGGTA" + gene 59482..59667 + /locus_tag="PDRPxv_78" + CDS 59482..59667 + /locus_tag="PDRPxv_78" + /note="gp78" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12468.1" + /translation="MAAPHNSPFRDRFGRYCGPLKIKTAAVLDPAPYDDLDHCIGCHR + HAVDGHEHGCPYRGAWD" + gene 59702..60046 + /locus_tag="PDRPxv_79" + CDS 59702..60046 + /locus_tag="PDRPxv_79" + /note="gp79" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12469.1" + /translation="MAMTPEPVTAPHMIEHRVRLTRDEAAAVAAGTYVPGTVPVHGAP + TVIDKRAQNAARAQAERDAQAAWVRQAAGTLWWSAQRVNSPARREKILAKRSELITGA + LAKGLDLGGLTV" + gene 60048..60650 + /locus_tag="PDRPxv_80" + CDS 60048..60650 + /locus_tag="PDRPxv_80" + /note="gp80" + /codon_start=1 + /transl_table=11 + /product="HTH binding domain protein" + /protein_id="AKF12470.1" + /translation="MTDTLDRDDLDSDIEAAPLPAWQPDHSLVPAILAGSPVTMKQLR + PPDRAWAVAVLRRMGYTAELIADWLSCSLRLVHTISADSAVLVQLYLDHTETTGNEHR + MLASEVARLAEALADAEAAAERYRQQRDRLIAVGSSDKATFPCGCPRTRYNTYVAPKT + GKAGCRHHRTLAVARHRARKRQSTGVASGHGQSNEGAGHP" + gene 60709..60945 + /locus_tag="PDRPxv_81" + CDS 60709..60945 + /locus_tag="PDRPxv_81" + /note="gp81" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12471.1" + /translation="MSAATGGVGQFAKRGGRRGGGYRGFKSKKQWRWAWATKQPWARK + KAHETKGGPKVRYRRLPESKHSGHRGRRGPSRKG" + gene complement(61020..61229) + /locus_tag="PDRPxv_82" + CDS complement(61020..61229) + /locus_tag="PDRPxv_82" + /note="gp82" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12472.1" + /translation="MMSFDATVRAAAADLKVGDTLTHEGRTYTVAEFDRRRRERPASW + GGPYICTTIVWAEGYLTRLEDVVTV" + gene complement(61310..61666) + /locus_tag="PDRPxv_83" + CDS complement(61310..61666) + /locus_tag="PDRPxv_83" + /note="gp83" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12473.1" + /translation="MNANTATAAKLELVKVEDHEGTGECPACGRTGLRWICVMNDGSR + AGVECAKKLLGYRPAPKHYRWVPEFTAVAEYVEAGQTFVLWQHNTSGQTRETRNGILV + AVGGARAAWQRRGWIA" + gene complement(61765..61908) + /locus_tag="PDRPxv_84" + CDS complement(61765..61908) + /locus_tag="PDRPxv_84" + /note="gp84" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12474.1" + /translation="MAHLNDFVARNARAAAIENRRRLEAELAADGIHTLDDLNDEIDA + YFA" + gene complement(61963..62226) + /locus_tag="PDRPxv_85" + CDS complement(61963..62226) + /locus_tag="PDRPxv_85" + /note="gp85" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12475.1" + /translation="MKLCAACQSASDKWLDYRITPSIKIASGAARDDTVEGVRDARAA + RFQEWRDTINRQQALIRKQCVSAGHVTIVDVELPGLDNTDIGG" + gene complement(62223..62504) + /locus_tag="PDRPxv_86" + CDS complement(62223..62504) + /locus_tag="PDRPxv_86" + /note="gp86" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12476.1" + /translation="MWPDLADVGTVALVGSVAGALYPLTRVLLAHIDRRRARDNRPLP + YSRAPEAREWCAGCGRTPVRRVMAWDAEGRYLCLDCRHPEDEADLRGER" + gene complement(62504..62734) + /locus_tag="PDRPxv_87" + CDS complement(62504..62734) + /locus_tag="PDRPxv_87" + /note="gp87" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12477.1" + /translation="MGDLSSVSGVSRRADTVTVGEWVDIPHRRRAYLVVEWCGTDVDG + MTYLAGHTHDGVPYVRTLSPGAKVNVIFEAVP" + gene complement(62727..63113) + /locus_tag="PDRPxv_88" + CDS complement(62727..63113) + /locus_tag="PDRPxv_88" + /note="gp88" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12478.1" + /translation="MKFNNHTARDQNVRMLVASVLHEHINDHAVDHVEVEVLRTVDRK + GHRHERDFCGPTTNYNGQPRTLIRVEVELDERMFSDTDELVCAVIAAEQDALNAEISE + AEAAATRALQRVEELRKRRTVEVTNG" + gene complement(63157..63459) + /locus_tag="PDRPxv_89" + CDS complement(63157..63459) + /locus_tag="PDRPxv_89" + /note="gp89" + /codon_start=1 + /transl_table=11 + /product="HNH endonuclease" + /protein_id="AKF12479.1" + /translation="MSSTNGRCNSTERGSSYARRARKLWLLSPEAAWGGDGETVPCWE + CGVLCEFEDLIADRIVPGEDGGRYTRDNIAPHCLLCSCRQGQRRTAAILAAKRRAA" + gene complement(63456..63566) + /locus_tag="PDRPxv_90" + CDS complement(63456..63566) + /locus_tag="PDRPxv_90" + /note="gp90" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12480.1" + /translation="MSRPIAPGKCPHITTVVITRAGRHVEVCARCRRQIA" + gene complement(63563..63742) + /locus_tag="PDRPxv_91" + CDS complement(63563..63742) + /locus_tag="PDRPxv_91" + /note="gp91" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12481.1" + /translation="MKLSDAAAKLTALAEQYPDAEIDVWLGGDYGQCPVVDIESPVDS + LDIDNTDDVVVVRFA" + gene complement(63755..63988) + /locus_tag="PDRPxv_92" + CDS complement(63755..63988) + /locus_tag="PDRPxv_92" + /note="gp92" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12482.1" + /translation="MIVRPENGYDDPVSAEFGPMAGHYGKVVEIDPAPHDSATLITVE + IVGRVDGPVSPAICADRFDPQWVFYPHELDHID" + gene complement(64012..64209) + /locus_tag="PDRPxv_93" + CDS complement(64012..64209) + /locus_tag="PDRPxv_93" + /note="gp93" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12483.1" + /translation="MTAPEKARKDRLRPVPRAGLKQIRNGEARYCPSCQTLRRQTDMR + ITKGGVPICIGCRDNSPRVIG" + gene complement(64206..64430) + /locus_tag="PDRPxv_94" + CDS complement(64206..64430) + /locus_tag="PDRPxv_94" + /note="gp94" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12484.1" + /translation="MVIATAPGVALRGTPVDCAQCGHEVTPIVGTFPDNTAVGTLSGR + WREFWIVPAHRAYGHGVCTGSGRPLGPVVR" + gene complement(64424..64609) + /locus_tag="PDRPxv_95" + CDS complement(64424..64609) + /locus_tag="PDRPxv_95" + /note="gp95" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12485.1" + /translation="MSTPNLVPDLVAVMSAGRHARALLIAKQPTPNYEGRHRAPEVEP + PVRVGSVWAKAIGGGPW" + gene complement(64606..64827) + /locus_tag="PDRPxv_96" + CDS complement(64606..64827) + /locus_tag="PDRPxv_96" + /note="gp96" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12486.1" + /translation="MGLMPNLGGLDVAKTVGEFKDAMVTMVSLLRETNRKLDTLIEIE + QAKQAPPTCLDCEDNGRLRCHEHGPAALR" + gene complement(64948..65250) + /locus_tag="PDRPxv_97" + CDS complement(64948..65250) + /locus_tag="PDRPxv_97" + /note="GP97" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12514.1" + /translation="MSQIVTAAQLETGDLFRIRTLTGRTTYQVDSARKVHYQDHYIEI + AATKVTTHGTTTMSLAPSTPVERLEPTPQRDIVAMLPYLNPGETVSVTYWDGQFWT" + gene complement(65282..65479) + /locus_tag="PDRPxv_98" + CDS complement(65282..65479) + /locus_tag="PDRPxv_98" + /note="gp98" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12517.1" + /translation="MSTSPACDCLTSEDHTLPHATMCAAAFNVHDTDHSQIRELAELA + AYKDIDTALSILRAYRSGSLS" + gene complement(65547..65822) + /locus_tag="PDRPxv_99" + CDS complement(65547..65822) + /locus_tag="PDRPxv_99" + /note="gp99" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12499.1" + /translation="MSGRPLRGTVGSGRHRPWAYTVMPGDVKVVGARTTMWGLKHFVL + RYEERHDEVGILLGWFPVAASPVPRRALRRRERRAALRARHDSTLRC" + gene complement(65819..66064) + /locus_tag="PDRPxv_100" + CDS complement(65819..66064) + /locus_tag="PDRPxv_100" + /note="gp100" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12424.1" + /translation="MGTKAVVQSRVINVNQTITSPTGVAKASKARETLATAREILYAS + GNMRAADAITPALHDLTRQIADYYRRRAEADKRAAQR" + gene complement(66102..66458) + /locus_tag="PDRPxv_101" + CDS complement(66102..66458) + /locus_tag="PDRPxv_101" + /note="gp101" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12413.1" + /translation="MFKFVARLNGRTVVTQHETQADATARIGQIAAVNNLTATQERRD + EDTISGVWHYRTAPNKGGVAGTFKIIDNSPVRIVDKDGFTAELLNTTDAEVAVVQYDH + LPEPTHLDWSHVNRIS" + gene complement(66621..67373) + /locus_tag="PDRPxv_102" + CDS complement(66621..67373) + /locus_tag="PDRPxv_102" + /note="gp102" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12503.1" + /translation="MSAPHTEVEVSAQLAIVSSLLATTCRGWTDISTVRADLRARKRE + LKRRLAEVKRNNQREKENTTMEDPNGADKAPKGVVADPSTPADLLAAAAEAEKAAAAF + RRQAAEIARAAEEARRPKMPTVQTGVSVVVTFRRYTSGREYNYAAIGWRAGTSVRWAV + TGTETRRFNWSGLLDFIGEANWHTIAEVTQTRTVLASSGEPPVAEVMGRYGKVLRTER + PSSPFERPSSPFVGGAQYGGEVRPGPYSEFEF" + gene complement(67370..67549) + /locus_tag="PDRPxv_103" + CDS complement(67370..67549) + /locus_tag="PDRPxv_103" + /note="gp103" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12500.1" + /translation="MSFAYDPSTAYVVLGLALLACFALHFSPRGSLREAVFATLTLSL + TITGIIGAAFGAVTL" + gene complement(67546..67782) + /locus_tag="PDRPxv_104" + CDS complement(67546..67782) + /locus_tag="PDRPxv_104" + /note="gp104" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12498.1" + /translation="MFDPADIVEPPTCYDCMGHITAHEDGCKTGLGIISDHENVLHDP + EVELYAVVSAMVDFEIERNGAYWAARRAAREARA" + gene complement(67836..68237) + /locus_tag="PDRPxv_105" + CDS complement(67836..68237) + /locus_tag="PDRPxv_105" + /note="gp105" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12501.1" + /translation="MRWHREKHTGGAYAAGAYHVHRAEAAALGWVAKGPGVETSHHET + KADAQAECERALNARIDDPTTEVVPGDHLLLRDSGKVGQLAQRLNGDETPADRVVYAI + RMPYGKTLCRVRSEFTLIDPYDGVDKPTLVG" + gene complement(68237..68572) + /locus_tag="PDRPxv_106" + CDS complement(68237..68572) + /locus_tag="PDRPxv_106" + /note="gp106" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12502.1" + /translation="MAIHIITKLGDWNTRTTSTDGGPDHYVTVAGEGTVDILNSDETM + FDGMLTAQNTLGQILWGKLSAHVACGTHYGSEHGTIDRIAVIDMHGATREEVLETVLW + VNGITQGAI" + gene complement(68643..69065) + /locus_tag="PDRPxv_107" + CDS complement(68643..69065) + /locus_tag="PDRPxv_107" + /note="gp107" + /codon_start=1 + /transl_table=11 + /product="hypothetical protein" + /protein_id="AKF12414.1" + /translation="MQITAPAAQTTTDYNKAAFWIEKGLHAEAARMKNNVRVKVVQFF + ATEAEAQEFAAQFPKSNKVVAKKMIGIKDNGGHVGMIEAVFNFSSNGVTGDRNETAIK + RFRSFEKNLDKAGLVVHWTMPYAAHMAMTYADVEAHIA" +ORIGIN + 1 atgctggtca tgaccacaaa ccaccgcctc atctacctca tcggcgcgcc cggcgcgggc + 61 aagtcgacca tgatggccca cctcaccgac tgctttgacc gagagcatgt accgggcacc + 121 gacaccgccc cggcgtacga cctgctcaag tacaccgatg gcgaggtcgc cggggtcgag + 181 ctcggcaagc ggcgacccgc gttctcgggc accgacgcgc tgcccgcctc gatcatcgag + 241 aaggccatcc cgtggctcaa caccaagccg tttcgactgg tgttcgccga gggtgcccgg + 301 ctggccaaca agcggttcct cgacgctgcg gtggccgccg gatacgaggt ccacctcggt + 361 tggctcaacc accccgatgt cgacgcgtgg cgggcagctc gctcggcaga agtcggcaag + 421 gtccagtccg agtcgtgggt caagggccgc gtcacggcgg tgaccaagct ggtcgagaac + 481 tacatcaacc ggcccgagcg aggtgtcaag gtctacgttg gtggtccaga gcagttgacc + 541 tcggatatgg agcacctgtt gggatgagct accccgatga gccgctgagc cccatcatcg + 601 agatcccgtc gccggacgcg cccgatcccg atgtatgggg cgaggacggt gcctacgacg + 661 agcgcaaggc tgcggtcatc cttgagcagg ccaagtcgtg gtccaccgag cagaagcacg + 721 ccatgctggc ttcgctgcgg gctgccaaag cgcgcgcgtc catgaaggtc aaatacgcgc + 781 atcctgccga gctcgccgct gctctcgacc cgaactacgt cgttaccccg gccatcgagc + 841 tgatcagcac atcgattgag cgcgtgctca cctcgcccaa gcagatcaac cttgagatct + 901 cgatgccgcc gcaggagggc aagtcgacgc tcgcagcggt ggccacgccg ctgcgcgcgc + 961 tacagcacaa cccgcaccgc aagatcatcc tcgcgaccta cgcgcttgac ctcgccgaga + 1021 cccacagccg aacgatgcgc gagtggatcg agacctacgg caccgatgtg gtcgacccgc + 1081 tcaccgggct gccggtcgag gacaagatcg ggctcaagct cgcacgcggt gccaacaagg + 1141 tcaccgcgtg gtcggtggcc ggtggccggg gcggcctagt ggccgccggt atcggatcga + 1201 ggctgacggg tatgcctgcc gatctcatga tcattgatga cccgttcaag aacatgatgg + 1261 aagccgacag cgcgctgtac cgcaagcgcg tagacgagtg gttttcgtcg gtggctcgaa + 1321 cacgtctcgc gcccgacgcg tcgatcatca tgatccaaac ccgttggcat cctgaggatc + 1381 tcgcgggcaa ggtgattgcc gccgagaggt cgctgccgag aaacgaacga acatggcgag + 1441 tcatcaatat ccccgccatc gccgaaaagg gcatccccga cgcgctcaag cgtgagcccg + 1501 gcaccccgat ggtgtcggcc cgtgacaccc ctgaggccaa gcgtaatttc cccaagatcc + 1561 ggcgcgaggt cggcgagcgt acgtggtacg cgctctacca gggatcaccg cgcaaccccg + 1621 agggcgggat attccagcag aagtggtttg acgcgacacg gctgcccgag gcacctctca + 1681 acccctacac cgccgtggtc ggcatcgacc ccgccgacag cggcgaggga gacgaggcgg + 1741 gcatcatcgg cggcatggcc gccaccatcg acaagcggct cacagcggtg ctcacgcacg + 1801 accgcagcgg gcagtacacc tcggaccaat gggccaaggt cggcgtcacg ctggcccttg + 1861 agatcggcgc acgagtgctc gccgtcgagg ggtacaccac cgccaagacc tacacgcgtg + 1921 tcgtgcgtga ggcgtacaac gcgattcacc gcgaggcgcg caagaaacag ctcgccggtg + 1981 tcccgctcac cccggtcgag caccgcgcgc tgccggatct accgccgttc acgatcaagc + 2041 cgtggcgcgg tgcgaacaag gccgacgcgg tggcacgtgc gggcggcatg agccagagtt + 2101 tcgagaccgg aagggcacgc accatcgatt acgcgctggc gacgttcgag caacaggcgg + 2161 tcgactggca ggccgggcag catcagcctg accgagtggc tgcgggcatc atcgttcacg + 2221 acaccatttt cgatctcatg ggcggctcga tgagcatcgc agcaccgccg accggcacca + 2281 atccgagcgg tggccaggga cgagaaatac ctcccccacc tgcatggatg cggcgatcca + 2341 tcgggaaata accggaagtg ttgacaacac ctcccaagtc gggtaactta tctcttgtca + 2401 ggcgggaccg ccccgccgac aggaaggaaa tcccatgatc gagaccaccg ccaccagcgt + 2461 caccgagttc gacggcactt tcgagtccgt catcacgtgg cggttcgatg gtctgtggga + 2521 tgtcgcggtt gcccacatgg gcacgtggct gtgccacgcg caggtcgcgg cgtcctttga + 2581 ggacgctcga tttctcgcgg gatacatctg cgggcagcac atgcgtgcag agtcgatggt + 2641 ggcggcgtga gccgccgccc ggcgcgggcc gcgctgcggc gctggctgca cgaccgcgtg + 2701 tgcgtcggcg cgacctcgcc tgacccgtac tgcacaggca gcggggccga cgagcatgca + 2761 cgcacacagg atgccaacgc gcgccgtctg atggcctgct cgacgcgcga ggaagtcgcg + 2821 cgtgagctgc accatcaggc gtgcactgtg tggatacagc aggcacacgg cggcgtctac + 2881 gtcgagctcg cgcgcaacac gtgcggcccc gatggtgcgc accacgtcga gcggctgatc + 2941 ggagcgactt tgatctcgga attgtgcgag atgatcggca tagttgacaa gaccgactta + 3001 ggcgggtaac ttatctcttg tccggcggga ccgtcccgcc gattgaccgc cccgccgggg + 3061 cagatcggag gccaccatgg ctgatcagga tttctacttc gaggtcgacg gccagcgttt + 3121 cgaccagcgc gtcgaggcca tggcgcactg cgatctgtac gggtaccact actcggcgat + 3181 ccaatgggtc gacccgaacg cgccctgggc gtgacctcgt cctactcggc ccgctcgtcg + 3241 ggccgagtag ggacgggcgc agcgatgacc tcgggctgag aaaggtgcag ccgttcatgg + 3301 gccatgcttg ccgggggaaa acgatgcgaa aagcccggtt tgacgcccgt ccctcggtgt + 3361 ctatgggccg attttgtcaa gtacctcggt ctggtacgca gtcggctcac cgagaggttg + 3421 tccgaggtag tgacacccta tggcagccag acacgcggcg tcagcctcgt tgttgttcgc + 3481 aggcgcgagc tcaggccatc gcttggccac cgtgagcatg acggtgtcct tgtctgcgtt + 3541 gcccttaccg gtggcgtatt tcttgacctg cgacgtgccc gcgcacagca gctcgacatc + 3601 gaacccctca cacagctcga tgaccttgcc aaagatccac ggcagcaccc acacaccctc + 3661 gcccttggct gcgaaggcga gggtctccat gacgacgagg tcgggccggt cctcggcgaa + 3721 tacccattgg atctgatcga ccagcgcctt gacccggcga gccatggccc gtttcgactt + 3781 gtcggcggtg gccgccggtg cgccgacggt gacacactca ccgtgccacg cgtcgagccc + 3841 gactgagggc ggcagcgaca caaggcacat gcccgtggcg cgcagtgagg tatcgatccc + 3901 gagaacgact ggcattccac gatcttaccc gacagcaacg ggcaacagta gactgccgtg + 3961 catgaccatc gacaaccctg ccaccctcgc gctggccctc gtcatcctca tgctcgccgt + 4021 cgctcgtgtc acgcggctca tcaactcgga caaaatctcc gacccgatca gggtggccat + 4081 cgccagcagg gtccgtcacc acacgctcat tgccgccgag gccgaggcac acggccagtc + 4141 gcagcgggcc gccgaggcgc acaggcgcga ggcgcggtgg gccggggtct acgagttcgt + 4201 gcagtgcccg tggtgcattg gtatgtgggt atcgttcggc ggcgcggcgg tgctggtgtg + 4261 ggtggtcaaa tactcgtgga cgcttgactg gtgggcgctg ctgcccgtcg cgctcgctgc + 4321 cagtcacctc gtcggcgtgc tggcccgttt cgccgacacc gaggaaatcg agatcgagga + 4381 cgcggctgag gacgagcttg acgactgacc gcacgccgtg cggataacct gacccgtatg + 4441 gctgctactt cactgcgcgt cgttcgccgc cccaagggca gcgccccggc agctcgtcgg + 4501 cgatccctca cggctgcctc gcagctcatc accgacccac aaaagcagat gaagacctcg + 4561 ctgatgggca ccgcccgcaa cgagtggcag tccgaggcgt gggacttttc cgagagcatc + 4621 ggcgagctga gctactacgt ctcgtggcgc gcgaactcgt gctcgcgcac cacattgatc + 4681 ccgtcgtcta tcgaccccga taccgggctg ccgaccggcg aggtcgacat cgagaaagat + 4741 cccgacgcac agatagtcgc cgactacgtc aagggcatcg ccgacgggcc gctcggtcag + 4801 gcggcattga tcaagcgcgc ggtcgagtgc atgaccgtgg tcggtgaggt atggatcgcc + 4861 gtgctgatcc gacaagagaa agatccagtc accggcatcg ccgggcctcg ggcgcggtgg + 4921 tatgccgtca cgcgcgagga aatcaaatcc aaggcggggg agaccgccga gatcttgctg + 4981 ccggacggta agacccacga gttcaatcgc gacctcgact cgctcgtgcg gatctggaat + 5041 ccacgtccac gcaaggccag tcaggccacc tcgccggtac gtgcttgcct cgaaacgctg + 5101 cgcgagatcg agcggaccac acgcaagatc aagaacgcgg ccaagtcgcg cgtgatgaac + 5161 aacggtgtgc tgttcgtgcc cgccgagatg tcgctaccgg ctgcgcaggc cccgatcccc + 5221 gaggggcagg cgcagatccc cggtgcgccg gtgcccgagg tgtccggcgt cccggccagc + 5281 gagcagctcg ccacgatgat ctatcaggcg tcggtggccg ccatggagga cgagaacagc + 5341 caggcggcgt atatcccgct cgtcgcgtcg gtggccgccg agcacctcga aaaggtccag + 5401 cacatcaagt ttggcaacga ggtcaccgag gtcgagatca agacccgtgt cgacgcgatc + 5461 acgcggttgg ccatgggcct cgatgtctct cccgagcggc tgctcggcat gagcaaaggc + 5521 aatcactggt ctgcgtgggc catcggcgac gaggatgtgc agcttcacat caagccagtc + 5581 atggatctga tctgccaggc gatctacagc gacatcctga ccccgctgct cgcacgcgag + 5641 ggaatcgacc cgaccaggta catcctctgg tacgacgcgt ctggtctgac cagcgacccc + 5701 gatctgtccg acgaggctgt cgaggcgcac gaccgtggtg cgatcacctc ggcggcgctg + 5761 cgacggctgc tcaacgtggg cgacgacagc ggctacgacc tcaccaccct cgacggctgc + 5821 cgcgagttcg ctgccgatgt cgtcaccaag aaccccgagc tcatcgcgat gtacgcaccg + 5881 ctgctgtcga gccagctcgc gggtatcgag ttcccgcagc ctgccggtgc catcgagtcg + 5941 acgcgcgagg acgaggaaga cgacgaggac agcggtgccc ggcagcagcg cgagccggaa + 6001 accgaggacg agcgtagtac cgaggaagcg gcgtcgctca atgaccgcgc ggcctacctc + 6061 gtcgccgagc ggctgctcgt caaccgtgcg ctcgacctcg cgggcaagcg gcgattcaag + 6121 gtgaacgacg cggcgctcaa gaccaagctg cgcgacgttc cggcccacga ataccaccgc + 6181 gtgctgccgc cggtccggtc gagcgagatc ccccgcctga tcgccggatg ggacaccgca + 6241 cttgaggacg aggtcgtggc ctcgctcggt ctcgacaacg agaagctgcg cagtgcggtg + 6301 ctggccacgg tgcggcgtca gctcacacag cctctcatcg agggcgaggt cgtctgatgt + 6361 ggccgctgcc cggtgaggct atctctcgga ccatcgaggc cgaggaagtg ctgaccgctt + 6421 tgtattcaaa agcgttgaat tcgtgggttt ccgatgtagt gccctttgta ctgcccacac + 6481 tcactgctgc gtccgaacca ccaccggata cgggtgccgt ggtcgagcag gcgggcggtt + 6541 tggatctgct actcgatgag ctgatcctac ccggtctgtc aatactgttt gcgctgtccc + 6601 tgatagaagc aatgaccggt atttctatac cgataccgga gttgcctgac gttgatgtca + 6661 atattggtgg gaggggtcga cctcagattg atcgtgagac cgaggtcgag gcacctcgtg + 6721 tgagcggtga ccgtgtgcgt gacgagcgtg tcgagatcgc gcaccccgag gtgctgcgtc + 6781 ctgaggtggt cgagacgatc acctcagtga ctgactacga gcgcgacgag atcgttgagg + 6841 cggtcgagcg tgtctatgcc gatccatatc tgcgcgagcg ggccgaggac tacgtcgaca + 6901 cacagcgcga gcgggtacac cgcacggtgc ccatggtgca gagcaaggta gaggcagcgg + 6961 cgcgcgaggc tgcggcggcc ccggcacccg agcgagagac cgagctgccg acgacgaccg + 7021 gcgacctcac ccctgatatc gaggtcgtga tcacgcggca gcgcgaggcg gtggccgagg + 7081 tgctcacccc cggtagcaac atgctgcttg aggtcgcaca gctacagggc tatcaggccg + 7141 ccggtgtgca gaatgcggcg gtgattgccg ccgcacagat ctcgccggac gcacacttgc + 7201 tcgacaaagt gtggatcgcg accatggacg gcaagactcg cgattcacat ttcgccgccg + 7261 acgggcagcg cgttgagctg accggtgagt tcaccgtcgg cggtgccggt ctgctgttcc + 7321 ccgccgaccc caccggccca ccgcacgaga tcctcaactg ccggtgccgt gtcggcattc + 7381 tcgcccgaga cgaggaaatt cccgacgagg tcgaccggca caccgagcgc ctcaacggtc + 7441 gcgacagcgt gcaggtcaat cggcaaggat cgcagcagga tgagatcgac cggcgcgccc + 7501 gtaagggtgt tgtccgtgcc cgcgaggaca ccgacggcat cgggcgtgtg gcagcaggtg + 7561 gttggaccgc cccgagcgaa caggattaca gcatggccaa gacagagctt ttccggacat + 7621 tcaccgatca gccgctcgcg ttcgtcggca tcgagaccag cgacggtcgc atgctcgcac + 7681 ccgagatcga gttctcggtg cgcacaccgc cgcttcccgc gatgtggtgc aagcagaccg + 7741 gcgagggcca taccgaggcg tacaccgtcg gtgtccttga gtcggcacgg atcgagaacg + 7801 gcaccgtgct cggctcgggt tactggctca acaagcctga ggccgacgac gcctatgccg + 7861 atgcccggca taaggtgagc cgcccgtcgg tcgacctcgc cgccaccgag tggaagctca + 7921 ccgtcgacgg gcgcgagctc accgaagagg aaatgttcga tctgccgccg gacgcgcacg + 7981 tggtgcagac gatcaccaaa gccgagctca tcggtttcac gatggtggcc aagcccgcgt + 8041 tcggtgacac cgtcatcgag ttcaacgcga cccgcgagtc tcgcgacctc gcgatggtgg + 8101 ccagcatcgc cgacgatttc cggcctcggg tgtacgcggc tgatctgttc gcgcagccca + 8161 acctcgccga gccgactgca ctgcaaatgg accccgccac gggccggata tttggacaca + 8221 tcgcgtgctt tggtgcgtgc caccgatcgg tgcagaacgc gtgcgtcatg gccccgaaat + 8281 cgccgtcgaa ctacgcacag ttccacactt cgccgccggt gctgctcgat gacggcaccc + 8341 gcctcgccgt gggccgcctg accgtcggca ccgggcacgc agacgagcgg ctgcgtcccg + 8401 gcccggccat ggcgcactac gacaacaccg gcgcgtgctg ggcgctggtg cgtgcctacg + 8461 agaccagcgt cggaatcgag gtgtcgggcg tggccgcccc gtgggccacc cccgagcaga + 8521 tcgagatggg cctcgcctcg ccgctgtccg gcgattggcg tgatttcggg cagggcctcg + 8581 atctgatcgc cgccctgagc gtcaacacgc ccggtttcgc cgtccgtggc cgcaccgacg + 8641 acgatggccg cccggcggcg ttggtggcca gcctcgcacc cgcacgcaag cgcgtcatcg + 8701 agctcagcgc agacgagatc ggcaagatcg tggcgcggtc ggtcgagcag gcattcgctg + 8761 cccgtgaggc cgccgaggcg gcggccaagg ccgcgcagga cgaggccgac aacatcgagg + 8821 cgctggtcga gctcgccatc gagaaggtag gcgagccgcc tgagcctgag cccgagaaga + 8881 ccccgaacga catcgttgcc gaactcatcg aaaaggcggg actgtaatgg catgtggatg + 8941 tggtggcggc gctaaggcca acagcaattc gccgggcggt gacaccctcg gctatcgggc + 9001 catcctgccc aacaagtcgg tcgtgccgcc ccgtaatgag ccgccgttct tcatggccgc + 9061 cgacgccatg cgcgaggtca cgctcgcgcg cggaggcacg gtgcggcgta ttcgcaagag + 9121 cgatcccacc gacgactatg cggtagcgaa acgcgaggct gagctcgccg ctgccgccac + 9181 gacagcgtag gtctgcacac cgctggtcta cgtttccggt tagagcgttt ccgactgcgt + 9241 tgtgtaccgg ggagcgatca ccgagtttga ccgctgttcg atcaagacaa ggagacaagc + 9301 gccatgtata agcgccctga gcagctcccc ggcaccgctg ccgagctcga cgctctgctc + 9361 gatgccgcgc gtgctgacat caacgtcatc actgcccgcc acaaggccgg tgagagcctg + 9421 acccctgagg acgcgcagcg cctcaaggat ctgctctctg aggtcgatga gctcaacggt + 9481 gaaaaggcca agatcgccgt cgccgacgag ctgcccgacc tgctggccaa ggccgacgct + 9541 gccaccgcgc ccgccgacga caagaccgcc gatgagtccg agggtgatga cgacggtgat + 9601 gccgagggcg acgacgacgg cgagcccgag ggtgagaccg agggcggcga gggtgccgag + 9661 cagcgcgagc ccgccgaggc catcaccgcc tcgaccggcg cacagcgccc gcagcgccgg + 9721 tcgccgaact tcgccggtgc cggtgccaac gacacccccg gcgagggtga cggcggcggt + 9781 gacgcgccaa ccccgcgctg gaagctgcac cccggcgcac ccggctatcg cgagggcatg + 9841 ggcactgtcg gtttcgccga catcagccag gcgctcgaaa agatccggcc cggcagccgc + 9901 gccgcgatcc ggcccaaccg cccgagccgt aacctcgacg ggcaggaatt tgcccgtcag + 9961 gtcgtgtcga ctctcgaccg cgaggtcgat gtcgtcggcg acagccacgc gctggtggcc + 10021 gcgatcacca aggccaccga tcagcgcaat ctgcccggtg gatctctgat cgccgccggt + 10081 ggctggtgcg caccgtcgga gcagctctac gacttctgcg acgtgcccga ggccaccgac + 10141 ctgatctcgc tgcccgagat caccatcaac cgtggcggta tccgctggcc tcgcgaacct + 10201 gatctgtcgg gcatcttcga ggaattcgag tggttcttca ccgagcccga gctcgaggcc + 10261 actgaccctg acaccggcaa gcccacggcg gtcaagacgt gcgtcgagat cccgtgcgcc + 10321 gacgagttcg atgagatccg gctcaacgcg gtcggatggt gcgtcgaggc gggcatcctg + 10381 caagagcagg ggtggcccga gctggtcgag tggttcatgc gttcgctgac gcaagaacac + 10441 ttccgcgccc tgtctcgccg gacgatcctc aacatggtcg ctggctcgac cggggtcacg + 10501 atcccggcaa cctcgaccat gggtgccatg gcctcggtgc tcaactcgct ggcactggtc + 10561 gcgaccaaca tccgtctcaa gcggggtctg tcgcgcaccg ccaccatcga gggtgtcgca + 10621 ccgtcgtggt tccacgaggt catccgtgcc gatctggcca tgcgtgcggg cggcgtcgag + 10681 gtcttcaacg tctccgacgc gcagattcag caggcgctcg cggctcgcaa catcgcgttg + 10741 cagtacgtgg gtgactggca gacccgcgag tcgggcaagc ccggcaacct ggccacggtc + 10801 gattggccgg acacggtcga cgtgctgctg tacccggcgg gcacatggtt ccgctcgatg + 10861 agcaacgtca tcgagctggg cgtcatgtac cccaaggaac agttgcagta caaccgcttt + 10921 acgcggatgt tcactgagga cgccatcgcc gtcggcaagc gctgcggtga gtcggtcaag + 10981 gtgacgttga cgctcgacgt gtccggtgcc accggcctgc ctcagcgtcg caccaacctc + 11041 agcgcataag cgagccggta gcagcagact gagagcgggc accgtgggca agaaccttcc + 11101 cgagactgcc cacggtgccc gttctctcat cttgagagga caacgcacat gaccacaccg + 11161 acaccgccgg tgctcgatgt cgtgcactac gaggtgccac cggtcaatcc ctcgcccaac + 11221 ggactgttcc cggcaacgac gtggatctcc gaccccgaca accggttttt caacggcgtc + 11281 gaggttcgcg ggccgaacta cggcggcgag gacgcgttcg gcacgtggga gggtcactac + 11341 tgctcggtcc cgccaacggg cgaggacgat cagcgcaagg acggcactcg acccgacatc + 11401 ctcgacgcgt tcgacccgat cacggtgtgg gcctacgacg agtgcgaccc caccgcgccg + 11461 agccgcgctg aggtgctcgc acgggcagct cagatcctca ggcttgagga acaggttgcc + 11521 atcgagcgcg agttcgccaa ccgcatgctc accgacgcgg gcacgcctgc tgcggcggcc + 11581 acgctgcgat tggcggtcgg ctacatcgag ggtgtgctcg cactgaccaa cacgctcggt + 11641 ttcatccaca taggcgcgca atgggtggcc accgaccccg atctgttcat cggcaacggc + 11701 ggtgtcaagc ggtccccgat gggccacacg tgggtcatcg gtggcggata cgtcgacgcg + 11761 ctcggggaca ccatcgtggc cacctcgcag ccgtatgggt ggcgcaacga gcccgtgacg + 11821 cgcggtgcca tcgatgcttt cgagaacacc tatgcagcaa tcgccgagcg taccgttctc + 11881 attggatacg aggcgctcat cgcagccgcg acgatcaccg acaccgacga gcccgagaca + 11941 ccgtaggaga cataccaatg cctgagggaa tcatcgccac cgtcgaggac ggtttcgcga + 12001 ctatcgattt cgtcaaccct gatctgcgtg gcccggcgct cgctgctctg cttgagctcg + 12061 gcgggccggg caccatcgag acgatcagcc gacgcggccc gcgacggctg taccgcgtgc + 12121 ccgagggcaa tgccgaggaa gtcgggttgc tcgacggcga cgagggtccg gccaagtggt + 12181 ctgctggtgc cgatacgggc cgggcggcgg cgctcaaggc tgccgatccc aacgtggtcg + 12241 gcggcgacga ctggcataca ccgatccttg agcacagctc gcgcaacgcg tatgtcggta + 12301 ccgtggccaa cgaggacgtg ctcgaccgga cgccggttta caccggcagc gcacacagtt + 12361 tcggtggcca cgcggtggcc ccgacgagca ccgatctgat cgccggtctc gacgccatca + 12421 agtacccgca gcagcccgag gaagtcccgc ccggcgaggg tgagggtgac ggcacgggtg + 12481 agggcgaggg tgacggcatc gaggggatgt ccgcacagcg ggtcaacctc ggtctcgccg + 12541 agcagaccag cggtctggcc agcgaccccg gcgcgacccc cgaggtcggc ggcgaggcgc + 12601 tcggcgagtt caccacggtt cagtccacac gcgtcgagga cgcggcggcc taccccgagg + 12661 gtgagccgtc ggaggactgg aagcgggccg agctcgacgc gtacgcggct gcgcacggtc + 12721 tcgacaccac caagcttgcc aacaagggcg aggtgctcgc ggccatcaac gaggctgccg + 12781 acggggagga tgatgcgtaa tgaccggcaa tgacctgagc ccacgggctc gtctacaggc + 12841 cctgatcccg gccaaggcgc gcgagacgtg gtatcgcgtg gccagcgcgg cggtgatgtt + 12901 cctgctcgct ttcggcatcc tcgacgccaa cgaggcggcg ctgtgggcac agttcgctgt + 12961 gggcctcgtg acgctggtgt tcgcgctcat ctacgcgtac acccctgcac gggtggccct + 13021 gtacgcgttc gtcggcgtcg tcggctcggt gctgctgtac tacggcatca ccaccgagga + 13081 aacgtgggca ctgatcacgg ctgccgtcgc tcaggcgttc ggcatcgcca ccgctgccgc + 13141 caagaccacc cctgtggtcg tcgcacgcgg ccaaacgtcc ctcaattgag ggacaggtga + 13201 ctagcccgtg gggtggaccc cgacataggt cgtgggcatc gaacacgatg ctcacgttct + 13261 tgttgctggt cgtgtgcatc atcgcaacgg tcgtgaccga ctatttcggt gagccaccca + 13321 actaccttgt cggcctgctc ggcacggcag ccggtgcatt tttcgccgcc atcggcagcg + 13381 atcagcagaa gaaagccgct gatctcgcgc agactgccac cgaggcaagc acctcggcag + 13441 ctcgggccga gacgaccgcc aagcgagccg agcacaagat cgatgaggtg ctgcacgagc + 13501 gccttgacga cgatgacgcg tcgaccaaag acggggcggg tacatgagct ctgtgcttga + 13561 ggtcgtctat tcgctgccgt tcgcggtggg cctgctgtgc ggaatcctcg ggcagcgggc + 13621 ctactgctac ggtcgggctt ggtacaaaga caggaacgat cccctaccga acggtcggca + 13681 ccgcactgtg gcaggcataa gcaaggtttg ggtcggcggc ctgatcgctg tcggcagcct + 13741 cggctatgtc ctgtaccagg ctgaggccac ccgcctcgac acggtctcgc tcgccgagca + 13801 tacgcaggaa tgcacgagcg acctgattgc ctcggtgtcg cgtggacggc agatcagcac + 13861 cgagaatgac cggctcagca ttgcccaccg cgacaagctc accgagctcg cgcaagttca + 13921 atctgtgtgg ctgggccgga tactcgaccc gccaccccac atcgccgcca tgcccgccga + 13981 tgaccctcgg cgggatggct atttcaagac cattacgcag ttctacaagg tacggaccga + 14041 cgagctgcgc gcggatatcg acaaaatccg cgaggaacag gccaagctca tcggtgatcg + 14101 cgagcgcaat ccgttgcccg accctcgatg ctggcccgct ggccccgagg tgaaatagcc + 14161 gtgcacggtg ctggcctagt gtcattttca gtctcgggaa acggactagg cgcgttcacg + 14221 cgcccgactt aggaggaccg accctatgcc cggtatccag cccgtcaagg gccgccgcct + 14281 gagggccacc aagatcaaca gttgcggtat gcccatcgcc gggcctcgaa accgtctggt + 14341 cacgagcggc tatgtcagcc tcacgttgac cccggtgatg cgcgacgcca ccgacctcac + 14401 gcaggacaac gctgagggca aggaatgctt cgcagaccgc accgcgcccg agcgtcgctg + 14461 gtggaccccg gcgttggagc tgtgcaacgt caataccggc ctgctgacca tgttcaccgg + 14521 gtgggaaacg ctggtcgacg ccaacgatct gcccgtcggt ttccgcgacc aaaaggaaat + 14581 cgagtccgat ttcggcgtag cgcttgagct gtggtcggcg ggcaagatgg aagaggattg + 14641 cgacgagatc ccgacttccg acgatgtcct caccgacacg agctcgggcc ggtcgtacgg + 14701 ctatttcctg ttcggcggca ccgagtggac acccggcgac atcaccatca gcggcgcggt + 14761 gaccacgttc acgttgaccg gtcgcaccat cgcgatgccc gcgtggggcc tcggcccgta + 14821 caacgtgcag gaagcggcca acggcaccgc tcgacgcctg ctgaccccga cgagcaaaaa + 14881 ggaacacctc acggtgttcc gtactcggat cgcaccgccc gagccgacac cgggcaccga + 14941 gcccgtgccg ctggctacga gcacgttgtt caccgatccg aatttctact acggcgggcc + 15001 ggacgacgag ccagccgccg tggtggcacc tgctcagtct gcgtaagtcg cgctacgcag + 15061 aaccgccccg gtcccctttg ggccggggcg gtttgctgtc tcggtttagc cgtcgaggcg + 15121 ggcgcgaacg taatcgtagg tgaggccacc ggcagggtag agggaacgaa tctggtaggg + 15181 gctgagatcg atcttggcgg caccggcgac agcggcggcg acggtgtcgg cggcgcgaac + 15241 ctcaagcagg gcgcgggcga cgcgaatctg atcgccgggg acaccgcgac gggcggcggc + 15301 ggcgagatcg gcctcggcgt agcggctgat ggtcatttct tcctctctgt gagcgggtcc + 15361 gtcccgctga caagaactaa gttacccgcc taagtcggtc ttgtcaaccc ccgtcacgca + 15421 gcaatttccc gcgaggtcac gatgcctcga cgccaccgca gcgcccagtc gaggcaattg + 15481 ccgcaatgca catgcttgcc gatgcagccg ggcagcggcg tgtgtttacg ggcgttgagc + 15541 gaccaggcca tcgagtcggc ggtcgtgagc aggtgcccgt attcgcgcag cccgagggac + 15601 ttgaccccga aaccgtgcac aggcagctcg gggtccgtgt cgaggatgcc ctcgaaaacc + 15661 cgccggatct cgtcggtgtg ctgtcgacgg cacacgctgc cgacaccgac gagctcgatg + 15721 ttgtgcagat cgatcccggc gtcggcgtac atcgacatgc atttcgcgta gtcagagacc + 15781 tcgtaacctt gcaggaccgg catgaacggg cagtgctcgt cgctcacctc gccccacagc + 15841 cgacgcagct cgacgtagtt ggccaccgtg cggcgctggt gctcggcgac gctcaggccg + 15901 gtccggtgga ccatgtcggg ctcgcacatc cagtcttggg gcgcggccca ttcaagtttg + 15961 ccgatctgct ggtcgtatcg gttgaccgcc tcgacgtact cgcgggcggt ggtctgccag + 16021 ccgccgaaca tcgagagctc ggagaaaccg cccgagtcga gcgcccatac ctcgatggcc + 16081 cgaggcaggc tgcgcagccg cttgaggcgg cgatgagaga cgaacagcgg caccccggcg + 16141 tggccgagcc acgaggtgat gtgggtaccg aggtagagat aggcgcgcat cgggacggtc + 16201 cttgtctgcc cggccccccg caccccggcg tggccgagcc acgaggtgat gtgggtaccg + 16261 aggtagagat aggcgcgcat cgggacggtc cttgtctgcc cggcccccct gaggggccgg + 16321 gcggtaagcg gggtgattag cgggcgcgga acgcggtgcc catgagccgt gcgttgtcgg + 16381 cgcggtcctg ctgcatgagc gcttcggcgc tgcctgcctc ggcgaggtgc cggacggcgg + 16441 tgcggagatt gccgctcagg ttgccgcctg agacgttcat gaggaagtac gcatgtcgga + 16501 ggacggcgtc gcgatcctcg acggcgacac cgcgcagtgc ggcggcctga cgctcgtgca + 16561 tgcggtcgag atcggcgtcg gtgatgagtc gggtggtcat ggtttcctct ctcggcgggg + 16621 ccatcccgcc tgacaagaga taagttaccc tcctaagtcg gtcttgtcaa ctccctcggt + 16681 tcatcaccac ctcgcggtag cctgagctcg tgactttcga gtggccgatc aaccgggcgg + 16741 gtctgcccgt cctgcctgag ctgaccgacc cgccgagccc cgagtacctc aaggcgctca + 16801 ctgagcgcaa cgctgccgag agcgtggcgg tggccatcat gtgggccttg tccggtcgac + 16861 agttcggcac ctatgagacc atcgcgcgcc cgtgcccgac gcgcccgcgc ggcaacccgt + 16921 tcgcctaccg ctccgacgac ctcgtgtgga ccggcgaggg gtggctcacg tgcggctgtg + 16981 tcggcaccgg ctgccggatc gtcggcccca acgtcgtgca cttgcccggc cccgtgcacg + 17041 aggtcacgcg cgtcgagatc ggcggcgtcg acctatcccc ggcggtgtgg gtcgccgagg + 17101 gcaacaagct ctatcggcgc gaggccccgt ggcccgccca agacctcaat cggcccctcg + 17161 gcgatgtgaa cacgtgggga gtccactaca cgcgcggtgt gccggtgccg ccgggcgtat + 17221 ccgagctcac cggcatcctc gtcaaagaga ttctcggcgc actggaagat gtcgggcgct + 17281 gccgcttgcc gcgcaccgtg accaccgcga gccgcaacgg tgtgacgtac cgggcctacg + 17341 atccggcggt gatctaccgc aacggtaaga ccggactgcc cgagatcgac ctatggctcg + 17401 ccacggtcaa cccgaacgcg ctcatggccg ccccgacggt gatctgatgg cctgccggac + 17461 agatcccgcc ctcgtcatcg tgggccatgc caccgaggcg ctgctcgatt ggttcaagga + 17521 cggcgcgtgc ccgccgctcg tcggcagcac gaccaacgtc cggtttttcg ccggtgactc + 17581 gacaccgctc gccgcgtggg acagccacgc gagccaaggc tgcaatgagc cgttcgtgtg + 17641 ggtgcggctc atgcggcggt accgaacgca gcggtttccc gatccgacca tcggcaccga + 17701 ttgcaatcta cctcgcgtgg cccctgtcga gatcggcgtc gggtggtgcg cactgaccga + 17761 acaagaacct cgatggtccg actaccagcg cgaggccgag gtgtcgtccg acaccgcgtg + 17821 gcggcttgag gaagcgctgt gtcaggccag cacccggctc aagcgcgacg acgagcagcg + 17881 cctcgttggc accgacgcgc tggtgcccta tggccccgag ggcggcgtaa tcgcatggac + 17941 cgctgtgctg tattcgacct actgagatag gagcatgaca aatgacgacc gtggtgattg + 18001 agggcagcat tctgccgtgc ggtttcctcg cccgaggcga gcgcgtcgag gtgcagcgca + 18061 ccgcccacat cgaccggctc atcgagcagg gtttcgtcaa cgtgatcagc gagatcggcg + 18121 accccgagcc cgcaccgctg cccgagccgg tcaaggcacc ggcgcgctcg gcgagccgcg + 18181 acacgtgggc cgagttcctc gccgagcaca ccgacatcgt gaccgagggc aagggccgcg + 18241 acgagctcat tgccgagtac gacgagtggc agagcgtgca cgacgttccc gccgccgacg + 18301 acagcgagta gcccgtggcc acgatccgcg cccgcgctcg gatcgagatt gacgaggcgg + 18361 cgcttgagcg ggagtcgggc gagcatctgc gggcattcca ccgctcgctc acgcgccgga + 18421 tcgccaacca atcgcgcgtg gcggtgccag tgcgtacggg caacctcggg cgcaccatcg + 18481 gcgagctgcc gcaggtatac accccgtttc gcgtgcgggg cggtgtcgag gccacagcgg + 18541 attacgcagc gcccgtgcac gagggcagcc gtccgcacgc gatccgtgcg cgcaatgcgc + 18601 agtatctgca cttctggtgg catggccgcg agatgttccg caaatcggtc tggcaccccg + 18661 gtacgcgggc acgaccgttc atgcgcaact cggcgcaacg cgtcgtgacc aacgatcctc + 18721 gggtgcggat gacttagccg acctcgctgg taaagtcggg ctcacctatc tcgggaggaa + 18781 aacaaccatg gccacattca acgcagacgg caagaccgtc actgacgagc agctctcgcc + 18841 gcccgccgaa tatctgcccg atctcaccga cagcgagcgc gaggccgact accaggccga + 18901 gcaggcgcgt atcgaggccg ccatcgccga cgcggctgcc gccgatgacg atgatgcgct + 18961 gcccgaggtc gaggtgctga ccccgcccgc cggtctggcc acgaccgacg agcagcccgg + 19021 caccgaggtg gtcacgttcg agcggttcaa cgtcgaggaa gtcgagaact ggtcctacga + 19081 caaactcgag ttcaaaggcg acatgctggg catccggctg ccgaccaagg cggcgctcgc + 19141 cggtttctcg ctggcatcga gcaagtacgt ctcgctcggc gtcaagaacg atctcaccgg + 19201 cctcttcatc gcgcggcacc tctcgcctga gtcgtacgga cgcgtattct cgcgcctgat + 19261 ggaccccgac gatgtcgact acgacgtaga caccgttgga gagctgttca acgcaatcgt + 19321 gacggcagcg gtcgagtccg acgacgagta gaccccgcac aacggaaacc gccccggtcc + 19381 tttcggatcg gggcggtttc tgtgcggcgg gtaggtcagc cgatctgctt ggccagcttg + 19441 accccggtga tcagcgtctt gagatcccgg cgggcggcca tgacaccctc gaacgatccc + 19501 tctcgccaac cgccgatcac gtcatcgtcc ttgccgactc gaccggggtg ggggaggaag + 19561 acggaccagc ggcaatcgct gtaatcccag ccgtcacgac ccatgcgggc gttgccgatg + 19621 taaccgatgg tggccttgtg ctcggcgtcg gtcagtccca gcagctcgtt gagctcttcg + 19681 acgctgtcct tcagaaaacc catgtgcatt tcgtagttgc gggcgatctc ttccggggtg + 19741 cgggtgatgg tggtcatttc taccttcctc ggcgggacgt tcccgcctga catgaactaa + 19801 gttacccgcc taagtcggtg ttgtcaagcc cattgcgtaa acgagtttcc gcgcgcccgt + 19861 gcgatagcct gaccaggtga cagatgtcgg caaaatcagc ctcggggttg agatcaatgg + 19921 cgacgatctg gcagcccgtc tcggcgaggc agtgcgcacg gcggtagcgc ccgcgctcga + 19981 caagctcaat cgtaagctgc aagagacgca gcgcgcgtac aacaagaccg gcgacagcgc + 20041 cgagaagtcg gcagtgcggc aagtcgcggc cctcaagcgc gtcgaggaac aggccgacgc + 20101 gacccggcgc gcggtggaac gcgctgccgc tgcctcgcgc ctcggtggtg gcggtcgcgg + 20161 tgactcgacg gcgggcgtcg gcggcaacga caacagacgt gtcgacaacc gacgtatcga + 20221 caatcgccgg tacgactatc gcacgtacaa cgacaaccgc aagatcgatc aacgacggta + 20281 cgacaaccgc aaggtcgaca accgtaagta cgactacagc accaagaacg accagagcat + 20341 cacctatgac tatcggtcgt accactacga cggcgtgccc ggtccggcac ccggcggcgg + 20401 cgggggcggt ttcgggcgag gtggtggccg gggcggcggc ggtggtcgag gcggtgtgct + 20461 cggtttcctg accggacccg tcgggctcaa caccatcgct caaggcgcgg cggcgctgcc + 20521 cgccgtggcc accggagtgg tcaacatcgt gggcgcggtg cagcagctca cgcaggccgg + 20581 tctcgcgctg cccggcatat tcgcgggcgg cgcagcggcc atcggcaccg ccgtcatcgg + 20641 cttccagggc ctcggcgacg ccatcaaggc gctcaacgag gccgacgctg acccggccaa + 20701 gctggaagag gccaacaagg cgctcgaaaa gctcgcaccg tcagcggcag cggtggcccg + 20761 cgaggtctcg cgactcaccg tcggaccgct gcgcgatttc caaaaggcca tcgcgcagcc + 20821 gctattcgag ggcctcgaca aggacatcgg gacgttcgcc gacaaggcat tgccgcgcat + 20881 ccaaccgggc atgcaaaagg tggccaaggc catcaacggc acgttcaaag agggcctcgc + 20941 tgccctcggc gacgacaaga acctcagcct cgccgaccag atattcggca acaccgccga + 21001 gggacagcag cgcgcgaaca acgcaattcg accgctgatc tccgcattcg gcacgctggc + 21061 caaagagggc agcgtgttcc tgccgcgcct cggtgacgcg atcacctcgg tggccgaacg + 21121 gttcaatcgg ttcattcaga ccaacaccaa taacggcaac atcttccgat ggattgacga + 21181 gggtctcaac ggcgcgcgtg cgttcggcaa cacgatcctc aacatcctca aaacgatcac + 21241 ggggctgacc aaggccgccg gtcaggccaa ggggtcgctg tccggtgacg gcggtctgct + 21301 cggtgcgctg gaacgcgtca ccgagcgctg gtcgacgttc accaacagcg tcgagggaca + 21361 gcagaagctc ggcaagttct tcgccgacgg caccgccgac ctccaccgct acggtgatct + 21421 gctgcgcacg ttgtggccca cgatcaagga aatcatcgcc gggttccaga cgtggggcga + 21481 gatcgtgctg cccatcgtct cggcgctcgc ctcgctcacc aacacgctcg cgcagatccc + 21541 cggcctgctg gaggcggtcg tggtcggctt cctcgcgtgg cgcaccctcg gcccgatcat + 21601 cggcggcgtg gccaacaagc tcaacgcggt cggcggcgcg gcgggcaagc tcggcggcgc + 21661 acgcggactg ctcggcgggt cgctcatcgc gggcggcggc gcgattcaag cctcgaccga + 21721 cagcggactc gggcagctcg ccggggcgct cactcaggtg gccggtggtg cgctcgcagg + 21781 ctcggtgttc ggcccggcgg gcgcggctgt cggcgcaggt gtcggcgcag ccatcgcagg + 21841 catcacctac gtgcttaacg agaatgcacg cgcgtcaagg gacgcggctg cggctcaggc + 21901 cgagctcgcc gagaacctca accgcagccg catggccatg gagctgacca gtcaagccac + 21961 caagaccgcc aacgacgcgc tgctcgcctc gggcggcgcg gtcgacgccg cgaccatagg + 22021 cgcggtcggc gatcagatct cggcgatccc cgaccggctc gcgggatcgt acgacgagga + 22081 cacgatcaag ggcgtggcca ccgcgctcaa ggatctgagc ctcaccgagc agcagcttgc + 22141 caccatcgtc acgggtgcac agcccggttt cgatgccctc accgcgcggc tgcgtgagat + 22201 ggggccgggc ggtgccatcg cggcccagaa cctgcaagcg gtgcgcgacg cgaccctcgg + 22261 ggccgcgaac aacgcggcca cggcggcccc gctgttgcag aacctcgcgc agaccctcgg + 22321 ggtcgacgtg cctcaggcgg cagccgagct gaccaatgcg ttcagtgcgg tacccaaaga + 22381 ggtgcccatc aacatcgacg cgccgggcgg acaagcggta ttcgacatgc tcgtcgctat + 22441 cggtgaaaag gtcagtgtcg acaacaacaa gaacatcgtg gttgaggcac cgctcgcccc + 22501 ggcggtgctt gagcagctca aggccatcgg ctacgaggtc acgcagaaca acgacaagac + 22561 catcaccgtt cgcgtcgacc cgcagatgta cgcggacaca ctcgccaagc tcggcaacgt + 22621 cggcaacgtg ctgagggatc tgcacgggca gagcctcggt ctgccagcgg tgcccgccgg + 22681 accgaacaac acagcgggcg gactgttccc cggcatgccc ggtcccaccg gccccggtgg + 22741 tgcggacggc atggtcgtcc ccggctacgc acccaagcag gacaccgtca acgcggtgct + 22801 ggcacccggt gagggcgtgc tcatccccga ggcggtgcgc ggcctcggtg gtgcgcaggc + 22861 gatctacgcg atcaactcgc gcttccgtaa gggcctgagc aagcggtact acgctgacgg + 22921 cggtgtccac ggcggcaccg gcgcgctgcc cggcccggct ggcatcgaga ccgaactgtc + 22981 ggtgctggtg cagattcgtg acattctcgc gggcaagggt ggtccggcga gcaacccgct + 23041 cgcagccacg gcggccaaca ccggcgcggt ggccacagcg accaaggcca ccggcggcca + 23101 gaagctcggc ccgttcggca ccccgatcaa ggaccgagat ccggcctacg aggcagcggc + 23161 ggcggccatc agcgcgctcg gcggcgaccc cgagaaattc ctcgggcaaa accccgtcga + 23221 ctacgcggcg gcccaggcgg cgcaggccac acagttcggc ggcggcctcg gcgtcaccgg + 23281 cgcggtgaac atgtctgcgt acattgaggc gttgacggcg ttcgcgatga ccggcgacct + 23341 gagcaaggtg cagggtatcg ggctcaacgc gaactcgccc gtcatcaccg cgctgacctc + 23401 ggcgcgcaac aaggccaagg gcggcctgag cgatcagcag attgccgacc tcgtcggcac + 23461 caccctcggc ccgaccggat acacgggcac gctagacagc accaacagtt cgctgatcaa + 23521 gtcgctgacc cggtaccgcg agcagctcat gaaacagggc ggcgcggccc cgacggtcgg + 23581 cggcacctcg gcagtcgcac cggccctcgg catcccgatg accgcactgc ccgctggtgc + 23641 catggacccg atcagcgcgt atgcggcggc ccacagcggc ggaaaatact cgtggggcgc + 23701 gtcggatctc gccgccggtc tgagcgactg ttcgggcgcg gtgtcggatc tcgtggagct + 23761 gctgactaag ggtcaggcca ccggcgagcg tctgttttcg accgccgatg ccggatcggt + 23821 gctcaagagc ctcggcgcgg tcgagggcgc gctgccgggt gcgttgcaga tcgggtggga + 23881 cgcgggccat atgcgtgcca cgctgcccaa cggtgtggca ttcgagtcgg gcggcggcac + 23941 cggtcaaggc gcaacctacg gcggcaacgc taagggcgct gcgggcatgc cgaacatcat + 24001 gagcctgccg gtcggcgcgc tgcccgccgg tctcggcatg agcggactgc cgggcggcgg + 24061 cgtgggtggc cagggcaccc cggtctacgt caccaactgg cccggccagg gcggcggcat + 24121 ccccggtctc aagaacatcc tcggggccgg tatcggcgct gccggtcagg cgggcaccga + 24181 cgtgctcggc gacatcatgg gcggtgtggt gggcctcggc aatgagccgt ggaacactcc + 24241 cggcagctac gcggcgctca acacgctcgt caaagagggc aacccgctcg cgctggccaa + 24301 ggcgttcggc ctcgatgtcg ccgacttctc gcgcgcgggc ggcgcggcgg gcgagctcga + 24361 aaagaacgag ggtcgcggat acgacgcgag cggtcgactg ttctccgaca ccggcgcgct + 24421 gctcgaccgc acgttcacct cgctcaacgc acagctacag gcgatgcgcg agcagatggt + 24481 cgacgtgatc gagcaggtca gccagaagct caacgacagc gcactcgagc cggtcgtcaa + 24541 ggccggtgtt cagtccgcgc tcgaaacgct caaggacagt gtttcagcgt ccatcggcac + 24601 cgccctcggt caggcggcgg ccccgcccat cgccgacgct gtcagtagtg cggtgtcgtc + 24661 gctgcccatc gatcagtccg gcgcgggcaa tgtcggcggc aacgctgccg gtgtcgtcac + 24721 gggtgcgctc ggcatggcga gcggcggccc tgtgtccggc ggtacgcccg gcaaggactc + 24781 ggtgcccgcg ctgctcatgc ccggcgagta cgtgctcaac acttccgatg ttgcccggct + 24841 cggcggcatg gccgccatcg actcgatgcg gtcgcgcggt ttcaagaggt tcgccaccgg + 24901 cggcggtgtg atcggcaacg acaccgtcgg cgcggatttc ttcggtgtgt ctcaggttcc + 24961 gatcatcggg gccatcgtca acttgctcgt ccgagtgctg ctcaaggtca tcggtgtcga + 25021 catcgaggtg cgcgacaccc tgatggaaat gaccgacgat ttccggcagt tccgagggga + 25081 cgcgttcaag gcgttcgacg ctcagggccg gttgctcaac gacacctcgg gtctcatcga + 25141 gcgcagccag tcgagcgagg aaaccgccgc cgaggaacgt attcgcatcc tcaagatcgt + 25201 gattcaggcg ctcatcaagt acatcatcga aaaggtgatc gtgcccatcg ccaaggcggt + 25261 ggccaacgca gccattcagg cgggcgcgtc ggcggcgggc gctgcggtca actctcaggc + 25321 acccggcgcg ggcggcatcg tcagctcgct catcagctcg gcgggtcagg cgggcgtcga + 25381 catcgccgcc gaggtcggta ccgacttcgc actcgcgatc agcgagacac tcatcgatgt + 25441 cgtcggagaa ggtctcaaat cgctgctgcc agatctgacc acgggactgt tcggcggaaa + 25501 cgggctcgcg ttcctcaccg accccgtcgg ctcgctgctc ggcggcctgc ttggcggcat + 25561 cgccggtctg ttctcgtccc tgttcggtgg ggcggcaacg atgatccccg gcatcccgtt + 25621 cgacaacggc ggcctcgccc acggtgaggg cctcatgctc aaggccacca gcgatcctga + 25681 gctcgtgctc aaccccaccg agaccgacgt gttcacgagg ttcgtcaagg cactcgagaa + 25741 tggcggtttc ggcggcggca gcaataccac agtgcacgcg ccgattacag tgatcggtgg + 25801 cggccccgag accgccgagc agatcgagaa ccgcctgctc aagcacatgc cttaggagac + 25861 ggtcgtggca tttcgtggct acttcgccct caacggggtc gagatcgcca acagcagccg + 25921 cgtggccgcc cacatcgggg ccgaggtgcc tacgcgcgac atcgggctca tgaccgccga + 25981 tgtcgactgc tcgctcacgc ctatcgatga cgaccggctg ctcgccgagc tgcccgccac + 26041 ctcggtgccc atcggggccg gacggctgct ggccacaccg ccggacgggt cgcggctcta + 26101 cggtccaggt ctcgccgttg tcggcgactg ttggacaccg aacacgctgt gtttcggctg + 26161 ccgcaccgcc atcgagtacg acgattcatg gaccggcctg cccgccttcc tcaacgatca + 26221 tgtctaccgg cccgagctcg ccccgtggtt cacgacgagg gttcccgagt cggcagagtt + 26281 cgcgggtgtg tgggtcatgg atgtcaaggg cctcgacgtg accacctcac agcgcgaggt + 26341 gatcgagatg gccggggacg gcggcgcggc gggcatccat cgtgacggcg cgcagcggat + 26401 acagttcgac gtgctgctcg tcgcgtgcac caacgcgggt gcgacgtacg gtctcgactg + 26461 gctgaccaca cagctccgca ggaccaatga ccggaccgac tcggtgttgc gatacctcgc + 26521 cgcccacccc gaggacagcg cagtcgaccc gacaaccctc gtgcgtgatc gccacggtgt + 26581 ggtcctcacc gccggtcctg agatcaccgg acaagtcaac gcgagcggac ggcagcacaa + 26641 ccaagcgacg ttctaccggg tcactttcga gctcaccgcg ctcatcccgc acgcgtaccg + 26701 gcctgccacg gtgctgcccg tcgagtggga caccgtcgag gtcgagccga tccaatgggt + 26761 gcactcgtcg gagtgcaaac cgcccgccga ctgctcgccg atgccggtgt tgttcgcgca + 26821 gggctgcgag gtcgagcgcg tcgaggcggt gtcctcccca ccccctgtgt gcggcggctg + 26881 catgccggtg tgcgcggtgg ccacacacgt ggtgcagatc cctctcaccg agcgcccgca + 26941 gaccggcacc actaccgtgg tcagcctcgc gatccgaaac accgacgcgc gcccgctcac + 27001 gctcaacggg tatttccggc ggtgtaacgc tcgcgacgac tgcgacgatg agcttttccc + 27061 cgtgcagatc cacggtctgc ccgccaccgc tgaggtcgtg ctcgacggcg tcacgggccg + 27121 cttctgggtc tactatgcgg ggcgcaaatg gcggcccgtg cacatcgtgg gcaccccgag + 27181 tggggcaccg tgggtgcctg ccaagctcga ccgatcgctg tgctgggagt tcatcgtggt + 27241 ctccgacggc accgcgatgt tcgaggtcga cctcgcgctc acggatcgtg acgaatgagt + 27301 gcgccgacgc gggtgctcga ccggggcggt gaggcgcaca ggatcgtcac cgccgagcag + 27361 ggcgtgacga tccacaccga caacggcacc accatcgatc aattcacacc tcgccagtac + 27421 acctcgtgca cgtggggctt gaggctgcgc gacgcgggca ccgccgacat cgtcataccg + 27481 ccgaccgccg actatgaccg gctgcgtgac atcgagccgt gggcacactc ggtgacgatc + 27541 tgggacgtag acagcggcac cacattgtgg accggcccga ttcagaaggc ccgagcctca + 27601 cgcaggggca tgacgatcag cgccaaggat cactcggcat acctcagtcg cacccgcaat + 27661 ccgctcacca aacggtggga cgctgccgac cccgccacgg tggccggtga gctgtgggcg + 27721 gccatggtcg aggcgcaggg catcaacacg cggcccatcg tgcgcgtcga ccccgagggt + 27781 gaccgctacg actttcaggt cgtcgccgac gagcagatgc tcgaccagac gttgagcgac + 27841 atgaacagtc aaggactgcg gtggaccgtg gtggcgggca ccccgatcat cgggccggtg + 27901 agcaccaagg cgctcgcgct gctcggcgag cacgactttc tcggcgacgg catcgagttc + 27961 gtgcgcgacg gcagccagac ctacaacgat gcgctggtgc gggccgccga cagcaacacc + 28021 gcgcgggcgc gcgtcgacta ccacggcaag aacctacaga ccatcgtcaa cctcgactcg + 28081 atgttcgggg tgtccaacgt caaccgcgcg gcccggcagt acgtcaacca caccggcagg + 28141 ccacacacca gactcgagtt gaccggaggc accgagctac atccgaacgc gcccgtgtcg + 28201 atggaagacc tcatgccctc ggcgcggttc atcatcgagg cgcgcggtgt ccgtcagctt + 28261 ttcgagctca cgagtgtcga cgttgagcgt cggcagggtg cggtgagtgt tcgtgtcact + 28321 atgggcactg tggaggacga tatcgagttg ctcgacgcgg ccaacggcaa gcagcagaac + 28381 atgactctcg gaggtcaacg gctgtgacgg tcgagctgcc ggggcgggca ccgacaaccg + 28441 acggcgagct cgcccgccaa ttccacgagc gcattcgccg actcgagaat ccccgctcgg + 28501 tgcgcgtcgg cccgtgggtc atcgcgaccg acccgatcac cggcgacctc aaagcatcac + 28561 gtcccggtca gaccctcatg ctcggcggcg atcagcccgt cgaggtggtc gagcagtcgc + 28621 tcagcctgtc caagttcgtc accaaggacg acctcgacgc ggccctcgac gggatcgagg + 28681 cgggcggcgg cgacaacgat tcggtgtggg cgcagctcta tgagaagctc accggcatcc + 28741 tgtcgccgac caacgcgctc aatgcgctgg ccaatttctt tcggcttgag ctcggcgcac + 28801 cgatcaccag cagcaggctg ccgctgctgc cgctgtcgca catccggccc atatctccga + 28861 acttgctcac cgacggatcg ttcgactacg aggaaacgct ctccggtttc cccgattggg + 28921 actgggacga caccgtagga cggaacaaac cgggcagcgc gtacaccgtc gctgatggca + 28981 ccacccacac gatccacagc aacagcatcg aggtcgggac ggacgaccgg ctcaacgtcg + 29041 aggtgtacgc acgttgggcc gggctggtgg ccagcggcgc ggccatcaag ctcgatatct + 29101 cgtgctaccg cgacgacggc tcgccagttt tctccggtcc gatcacgctc gactcgcgga + 29161 cggtgtccgg caccgcctcg gggtggacca agctcagcgt gacgaactgg gacgttcccg + 29221 acgaggcccg gcacgtcgtg gtcgagttga cggtcaccag cggcgcgaca gcgggcgtcg + 29281 tgcacttcga tgacgcgggg tgcttcaaga tcggcacgat gccgcaaagc tatatctcgg + 29341 gccttgtcga gggcctcaat gcgttgtggc agggcatcca agcacggatc gatgactttc + 29401 tcgacctgct cgacgtgttc ggcggtttca acgtcggcga tcacatcggg cagctcaccg + 29461 atgtcgtgac gcgattgcag cacctcaacc cgctcaacgg actgttcgac gccagcaagc + 29521 tcggcaacat cgccaacatc ccgatgatcg cgcaggaacg gatctcgggt ctggtcaccg + 29581 cgctcaacga ggccgggcag ggtatccgtg atgccatcgt gcaggctctc ggcggcagcg + 29641 gtaccgggca cagcaacatc gacgtgctca acgcgctgat gaacattccg gccagcgtcg + 29701 tcaacaccgc cattgcgggc gcgtcgaaca tcgagaacgc actacagcag gccatcgact + 29761 cggtcatcgc gggcgcgggc gatctcatag gcagcggttt cggtttcgcc gacatgatcg + 29821 cgcagctcac cggcctgcgg cgggccaccg ccggtacggc ggcagcggtg gtcaacctac + 29881 agtctcaggt ggccaatctc gacccggcag ccaacagcga ggtggtcgat ttcggggagt + 29941 tcgccaactc ggcgtcaccg ccctcgatgt tcaccaaggt cagtgacacc ggcgcgggca + 30001 gcatcatcct gaccaacggt gtgctggcgt ggaactcggc tgatgcggtg gggcgcgagt + 30061 tctatctcta caacggcggc ccgctacaga ccgatctttt cgaggtgcaa gttgttctgc + 30121 cctcgatgcc ctcacacggc attttcggtc tcgactcgac caactacgtg tatctcatcg + 30181 gacgctcgga tgccaccggc aacaacatgg tcgtcgcacg gctcgggtgg gacgaagtac + 30241 gggtctactc gcgcaactcg ggcaccatga cacagatcgg tccgacgatc agcgccgatg + 30301 acattctcac ctcgggctcg tcggtatcgt tcaaaggcgg caccatcgcc gatccacgct + 30361 acttcaccgt ctcgatcaac ggcgacaagg tctacgagta cagcgacgaa gcaccaatca + 30421 ccgtcatcgg cgcgagcaac cgtttctgcg gtctcggact ggaaaagggc aacaactacg + 30481 cgacgggccg gatctcgacg tgggccatgc tcgatggcgg atcgtcggca ggatcgggtg + 30541 tcgtggccgg atacaccaac gcgggcctga caaacttgat cctgtggaag ggtacgcagg + 30601 ctcagtacga cgcactgccg accaagagcc cgaacgggat ctacgtggtc gaggggtgag + 30661 cggtgggcgt ctacatcggt gacaccccga tcagcaagat catggcgggg acctcgccgt + 30721 tcgctggcaa ggtctacatc ggatcgacgc aggtctggcc cgaggtcgag ttcccgctcg + 30781 tctacgagga tgtcaacctc acggatgcgc tcgtgcccgc aggggccacc gcgctggtca + 30841 tagagcagct cgtcggcggc ggtgcctcgg gtctggcagg gcaaaatcag tccggcgcgg + 30901 gcacctacac caaccgtggc ggcaccggcg gcggcggcgg tgcggtcatc gggcagcaca + 30961 tcatccccat cagcgagctc ggcccgacgt tcagcttgca gatcggcgcg ggcggcgagc + 31021 agtcgacatc ggacaccaca cgcaacaacg gcggcccgac gatcttctcg tcaggcagca + 31081 tcgtgctcac cgccgggggt gggtcagcca gcggcggcgt ggccagccaa tcgggccaat + 31141 ggtatgtccc gatggccaat ggcacggcgg gcggcgtcaa caacaccatg ggcggcgcgg + 31201 cgggcggtgc cgagggcgtg agctacgcgc acagcggcaa cccgcagaac gacaacgaga + 31261 aatgggacgc gctgtctgct gacccggcgg gcaccgccct gcaagtcctg ccgggcggcc + 31321 tcaagccggg caacggcggc agccagtccg gcacgggcca gcgtcccggc ggtggcggcg + 31381 gtggcggcaa gggccgcagc tcgtcctcgc gcggcgtcgg cgggcgcggc ggtgacggct + 31441 atgcgcgcat ctacttcatc aacaccgcct acaagcgcga catcacacgc gtctacacca + 31501 ccgccggtgc gtggacgtgg acaccgccgc cgtgggcggt ggcgggcacc aagatcgaca + 31561 ttgtcatgtt ccccggcggg cgcgcgggca agaacggtgc gacgttcggc ggcgcaggcc + 31621 gaggcggtct cgcgggcacc cctgtgtcag caaccctcac cgtcggcacc gatatccccg + 31681 tcagcggggc gctcaccggc gtcgtcggcg cgggcggtgc cagcaacggc gcagcgggcg + 31741 gcgtcagtac gtgcacaacc gtgggtctca ccgcgccggt caacaatgcc gacgcgagcg + 31801 gtcaggacgg cggcaacgcg cctaacgtca cgctcaacgg gatcacgtat cccggtgggc + 31861 gcggcgggtc gagcggcacg agctcatcgg cgggcgagga cggtcaagac cccggcggcg + 31921 gcggcgaggg cggcggctcg ctgattgtgg ggctcgcggg cggcgcgggc ggcaagggtc + 31981 gcgtttacgt acgcgtctac gaggtgtaag ccgtgccgtt tggctggaat cccacaccgc + 32041 agatcacccc gcgcgtcaca ccgcgcgggt ggttcaagga cacgccagag ctgcccgtcg + 32101 accgtgagat cgggtggtgg gcggtgctca ccctcgacgc gatcgacctc ggcgacgctg + 32161 cgcagaccat gacccttgag gcgctcaagg cgctcgcgct ggccaacctc gccgcctcgt + 32221 cgcaatccct tgagctgcaa cagatcgcgg gcatctcgtt cgccaacatg atcccgccct + 32281 cccaagcgct cgcgctcaag aaaatcgcac tgctcaatct gtccggcgtg gcgctgccga + 32341 cgcaggcgct cgcgctggcc aaggtgctcg gcatcgcgct gtcgagcatg ggcatcagta + 32401 cgcaggccct cgacctcggc aaggtcgtca ccgtggccat ggcgacggac agcccggcgg + 32461 cccaactact cacgctcacc aaaaagggcg tgctgtcgct caacagcacc gcgacgagca + 32521 cacagcaggt ggtgctcgcg cgggtggcct cgctgatcat gtcgacctcg ctggccacct + 32581 cggtgcagtc cggcctcgcc gtcggtttcc cgccgttgac agcgaccacg gtcacgttca + 32641 ccaacaccac caacatccaa tcgtggacgt tcccgcgcaa cgcagagtgg ctcgccgtgg + 32701 tctgcctcgg cggcggctgc ggtggacgcg gcggcggccc tgtgctcgcg ggcggtggcg + 32761 gctatgcggg gtcgtacggc accgtcctgt tgaggcgcgg cgtcgacatc ccgtggtcga + 32821 ccacgatgtt ctacatgaac gtggggccgg gaagcgccgg gtcgtcgggc ggatacctcg + 32881 tcagcgaccc ctcgccgggc acggcgtcgc tgtgcaccac caccgcgctg gccaccctcg + 32941 ccagcggcag cggcggcggc ccgaactact cgacgcagcg tggccaggac gcacccgacc + 33001 tcagctacaa cgggatctcg gcggtcggcg gcaagtcatc gggcggatcg gcgacggcgg + 33061 gcggtgtgcc cggcggcggc ggcaacgggg ccaacccagg atcgttcggc ctaccgggcg + 33121 gtgctggcgg tgccggtggg cgcggacaga tatgggcgcg agcattccag tgaccgccat + 33181 cctcgacaga tagtatcgac cccatggccg tcggacctac cgcctacttg gtgaacaagc + 33241 tgctcgatca cgcgctacgc ggggtcgcgt acacaccgcc ctcggtcatc tacttcaagg + 33301 cgcacaccgg cgaccccggc gcgtcgggcg cgaacaacgc gagtgcgcag actgcacgcg + 33361 tggcggtcac gttcctcgcc gcctcgggcg gcacggtgct gctcaccggc acgccagaga + 33421 tcacgctcaa cgcaaccgag acgatcactc acggatcact gtgggacgcc gccgggccaa + 33481 cgggcggcaa ctgtctgtgg actgcgcagg ccaccgtcag caagggcggc gtgtccggcg + 33541 acatcatccg gctatcgggc ttccaagtcg gcttcaccgg cctcgcagcg taagggagaa + 33601 catgaccgca ctgacaccga cccctgattc acaatgggct gtgcaatggg aggcgttcgc + 33661 gcccgaggac agcagtgtga tgcccgagcg gcccatcctg cccgaggcac cccggtccgg + 33721 caccgacgag gacacgcctg agaactgggc ggcctatcag gaagcgtatg ggatctacgt + 33781 cgaggccatg gcttcctacg agagtctgct tgcagtgctg ctcgccgacg atgccaattg + 33841 gcgcacgatc accaacggtt tccccgacga ggcgctcgcg cggcagatgg tcggcatcat + 33901 ccgcagtgcc aatgcgggca accctcagac gcgtgatttc cggctagtgt ggtccgcgcc + 33961 catcgtctgg cacatcgcgg acgagtgatg tcgacaccgc gcgtctgcct atccgagaac + 34021 ctcgccgtca gccccgacgg caagctgagg ctgcctcggt ggtcggtcgt gcgcaatgtc + 34081 ggcgacatcg tcatcgcgtc ggcgggggac accgccaagc tgctcatcag cgacacgctt + 34141 cccggtcgga agctcatcga ggggcggctc gcgtggacca acgacagccc ggtcgagcag + 34201 caattgcgcg tcgaggttac gcgtcgatac cggcgatggg tcacgtctaa ccccaatgcc + 34261 atcgagttcc gtgaccgatg gtcgtgggtc gtgatccccg agggcgatcc actcatcgat + 34321 ccggctgagc ccgatgtgtc ggacacgttc aacggcaaga ccggaagcgc cggggacatt + 34381 cagaccaaca ccgttgccga gccgttgccc ggtgtgttcc ggcattggtg gggcaccacg + 34441 acctcggaag agtggatacc ggacgtgctc gcgcccggcg acacattctc gctgtggtac + 34501 agggcctacg tgtggacacc gccgccgtgg tcggacaatg ccaacaagaa cgcgccgacg + 34561 cacgccgccg aggccggata cagccggttg cagctcactg cctacccggc tcagggcaag + 34621 gtggtcgtgg gatgagcctc aagctgtgta ctggcgaata catgctcagc accgtggccg + 34681 gtctcggccc gtcgcgcggg tggttcccgc gtgtgctcgc cgagcagatg cttgagtcga + 34741 ccaaggacgg cgagattaag ctctcgccgg accccgtcac gatgatcgac ggcgacctca + 34801 cgtggttcaa caacagtgat gaccgcgtgc gggtgttcgt gctcgtgcac accgcgccac + 34861 gcgacatcat cgcgcagaat ccgtccaccg tggtgatcca cgatgcgtgg tcgcaccggg + 34921 tgggcagggc accctcggct gatttcccga gcgcgattcg caacagtttc ggtggccggt + 34981 tgcagctcga ccgcgcctcg gtggcccgcg acgcggtcaa gttcggtcgg ttcttcctga + 35041 ccggcgacga tcagcaggcg tgggtggacc tcggtatcgt gcgtccacag cagtcttttc + 35101 acttccgcta ccgtgcggca gtccagacac ccggcacgtg ggtcacaccg agcgagcttg + 35161 aaccgcgctg ggaggcgtac gcgcggtgga cacggctcgt cgcgctcggc ggcccggtag + 35221 gagagtcatg acctcgcccg attgcatcga ccccgatcac ttccgcgtga ccgccgacgg + 35281 cgggatcgag ccgcagccat ggatgcaatg gaggcacgtg cgcagcatca gcgccgaggg + 35341 cagatcgggc acgtacgcgg tgaccggcgg tggcaacaag aacgtgctga ttcatggtct + 35401 gcaagtcagc tacaccaatg acaccccggt gcctcaggcg gtctacggca agatcacgcg + 35461 cggcggctgc cgcgtggccc tacaggcacg ctcgcgggcc tacctacagg tgtccagcgg + 35521 ataccagaag cacgcgagcg accccggcaa gcttgaggtg tcgagccgga tgggctgtgg + 35581 tgccgacatc gggcgtggcg gcaccctcgg catcggcact cagttctgcg tcatcgagca + 35641 gcgacagggg atggtgacga taccgctcgc gcccgagcgg gccgggtggc tcaccctcag + 35701 ccccggtgaa gcggtcaccg cgcgcgtcga ggtcaagttc atctcagagt tctgggagaa + 35761 caccaatatc gacggcggtg cgtcgggctc gacctcgggc tacgactcgg gtgccacaca + 35821 actcgatctg ttcgcggtcc cggtactgta ggggtaattc cccacctcac tcgggagagt + 35881 gccagtgtac gaaccaccgc ccggctacga cgattgcgag tcggatgccg acgatttccc + 35941 caccccggcc aatgcggcgt accgcgagct cgatgtcgag ggtgtcggca tcctgcacgc + 36001 gcgtaaaccg ctgcccaacg ctatccccgc gctcgccggg gcggccaaca gcaaggtcaa + 36061 gcccgccgcc cgcctcgact acctcacggt gttcatacag aaccacctcg cgcccggcga + 36121 gtacgaaact ctgctggcca agatgatgga ccccgagacc gagctgcccg aggacacggt + 36181 gctacgtgtg agccgagcta tcgcgacggc gggcagtgcc cgccctacac ggcggtcatc + 36241 aacctcgcgt tgatggccgc gcacaattgg cgggcactgc ggctcaaggc cctgggccaa + 36301 ggcatcacca acatcatggc gtgcccatcg atgcacgtcg tgctggatct catcgagcag + 36361 ctcggtgccg aggcgtcggt gtctgaggcc aagtcgcaga ctgaggcgcg cagcgagctc + 36421 acgcgttact acgacaagct ctataagccc gacatcaccg ccaaggtgat caacggcgag + 36481 agctatttcc cgccgccgcc cggtttctcg gacgaggaag tcgaggcatc gttcgacgcg + 36541 ttcctcagca acccgctgcg atgagtcggg gaacttgccg cccgccgcga tactctgtgt + 36601 gctatggccg tcaccgctgt cctgtttgaa acctcaagca cccgaggtaa ccgcctcgac + 36661 cccgatgtgc gtgacgagat tgaggcgctc gcgccgggcc ttgagaccgg ggaggtcacg + 36721 accgacaaga tcgccaatga cgcggtcacg cgcgcgaaga tcgacgcggg cgcggtgggg + 36781 tcggtcgagc tcgatgacga cgcggtcaag gcgcagcaca tcgatgatgg ggcggtgggg + 36841 acaccagcgc ttgcggcggg cgcggtcacc tcggagaaga ccggtatcgg tgtggtcacc + 36901 gccgaagatg cctcgggcaa ccccgtcgag gtcaagcagg tctacatgac cgtggcgcag + 36961 tacaacgcgc tggcgacaaa agacccgaac acggattact acctgagctg atggcgacga + 37021 tcaggcgcgg tgcagcggca ccctacaagg cgatctatca cggcaccacc ccgatcaagc + 37081 aagtccggcg tggcgagacc ctgttgtggt cgcagggcaa gatccgagac gatttcaccg + 37141 acattctgga ccggtggctc aacgagttgc tcagcggcga cctcggggcg ctgtgctccg + 37201 atgtcaccgg tgtgctgcga gacggtctcg gcaacatcgt cggtcacacc gtggctttcg + 37261 ttgaggacaa catcaacggt ctcggcaagc tcgttgcgca gaccgggcaa gatctcgcca + 37321 ccgcctactg cggcgtgtgg ggcggcaccg ccgcgcccaa tggtctcatc ggactgatca + 37381 acggcatccc gattttcggc ggcatcctcg ccgactggct caagggcgag ctcgatatcg + 37441 tctcgatcat cggcagcatc ccgatcatcg gcgacatcgg gcgactcatc ggactgatcc + 37501 ccgacgcgct cgggcacctc gccgacccga tcaactatgt ggtcgacgcg ttcggcgagg + 37561 tgataggcac gatcacgtgt ggacagttcc ggccccacgg cggggacaac gagggaatct + 37621 gctacaccat cggtgtgctc ggcgacatcg ccaagatgct cattcccgat ggcctgctca + 37681 cgctcaaccg gcacacttcg cgtctgcgtc acgagacctt gctcgacggt gatgacggct + 37741 accttgaggt gcaggttgcc gacctcggcg cgccggacta cccgactcag gtgttccggc + 37801 ggtacgcgaa caacggcagc ggtgccaccg gagtcggcat gcagttcatg aactctcagg + 37861 tctcgctcgt gcggcgtgtc ggcggaaata acacgctcgt gctgcctcag gtggccagct + 37921 actcgccggg cgatgtgatg cggctggaac agttcggcga cattcactcg atcaccagaa + 37981 acggcgagac cgtgggcgtg tgggaggaca atagcgctac tgcggccaag ggcgtcaaca + 38041 accgctcggt ggccatgatc atgcagggcg ctaaagagct caacacatcg cgcaaggtct + 38101 cgccgggtct ggcctacctc gaggctgctt gatcggtcac gtagccgaac aaccacggct + 38161 catcacgccg cctcgcgtac ttgtgcggct ggctgcggta gaggcccggt ttctggccgg + 38221 gcagggggca gatcggcgtc cacacggtga gctcgtgctg cgtgtcgggt acaacacggc + 38281 gctcgccgtc tagtgggccg ccgtagagct cgacgctgct ggccctgtgc atgtcagtcg + 38341 aaccagatcc gctcgcgcgt gcggcgctcg tgggtaattc gctcgcggcg ggcaccgacg + 38401 ggatgctgtg cgggcggctg tggcggccta taggcagcct tgtcgagctg cccgaggtat + 38461 cgcagccgga cggcaatgcc gatgcacaca gcgccgacga gggcggtcga catcgggtgt + 38521 gggttgtccg gcagatagct gatgaacaac atgaacagtc ccaccgcccc ggcggtggtc + 38581 accttatcgt ggtgaccacc gtgcggggtt cgttggacgg tcaccgcccg ccccaccagc + 38641 cgcgaccggg ccgcgtggcg tgccacgcgt cgatggtctc gggcagccat cccttgtgct + 38701 tgccgacctc gacatcgggc ggtggcaatt cgatgcccga cagcgatcga acggatttca + 38761 tgccgaggcg cttggcgacc tcgtctcgac tgaggtaccg aggcaccggg ggtgcctcgt + 38821 gggtctgatc agtcatcgtg caaacctccc cgttgtctgt ttcccgacaa tggtaagcgt + 38881 gatcggtgca tcggcatcgg tctcaattgc ggcgcgtccg gcaacgaggc ggtcccattc + 38941 gtcttgaggt tcgccagagt tgctgacagc gatggtaatg ccaccggatg caattgcctc + 39001 ggcaatgacc ttatcgacct gattagcgaa ctcatcgcta agcgcgtcaa gtttgctgat + 39061 gagaccggga gggcatttat cacgtcctgt ctcgatgtgc tgatagtcac gtcggtccat + 39121 accgagccgg gcggcaaggt cacgctggct gaggccgagg tacatcctgt gtgcgcgcac + 39181 gagctcgccg aggccctcgg tgtaggtcgg cgggtagacc tcgacgggtg cctcggcggc + 39241 ggtctcggtg gtggtcatgg ggtgtatctc cttggtgttg ttgtggtggt gaaacagtgc + 39301 gatgggttgg cgatctcgtc cacggcgcgg acgatggtga cgatggacag gatgagcagg + 39361 aacgcgccga ggctggcgaa ccaagcgggg gcgaaccatc cgccccacgg gttcgggtcg + 39421 tcgtagtaga cggtccagcc gagcgcggtg agcgaggcga ataccgccgc gctcaccagc + 39481 agggtccgca tcagctcacc acccggcctt ggcagcgcag atcgggccga taccgcgctc + 39541 gcggctctcg tcgttggtca gggttcgacc gcagacaccg cattcgccga tctcgtggcc + 39601 gtagcgggcg ctcgcggcct cggcaccgac ctcggcgatg cggcgcagga tcgtgccgcc + 39661 ctgcttctgg ctgaggcggc gttcctcgtc gccgacaatc tgcttgacga acacgtaccc + 39721 ggcccaacgt ccttcggtgg ggcggtcgac tttgtagaac gcggtgccgt tgatcgcgtg + 39781 gatcgtggtg tcgatggcgt agcgaccggc gggtacctcg tcggcaccgg cgacgatctc + 39841 gcgctcgggc gcgtaaccct cggccttgag ccggtcgatg ttcgcgctga ccgtggcgga + 39901 atcgttgtgg gtttcgagcc acgccatgat cttgacggcg gcctcggcgg tgtcggcacc + 39961 ggtggccttg gccttggcca tcatcaattt cgtcagcagc gcgatggcgg cgtcagaggc + 40021 ggtggcgggg gcgatggtgt cgaacggggt ggccatgatc ttgaatcctt cctcggcggg + 40081 tcgatcccgc ctgacaagag ataagttacc cgcccttgtc ggatttgtca acacgttcat + 40141 cgggcacgtc taccgtgaca cgtatgccga ggtacatgcc catgcgcgcg ggcacctaca + 40201 cactgtcgtc cggtttcggc ccgaggtggg gcagccagca ccgtggcctc gatttcgccg + 40261 ccaaggacgg cacaccgatc tacgccgcgc agggcggcac cgtggcctac atcggcagag + 40321 ccgacggttt cgggcaatgg atcgtgatcg accaccccgc cgccgacggt ggcggcacga + 40381 ccgtctacgg acacatgtgg gacgcgttcg ccaccggact acgtcaaggg cagcgcgtcg + 40441 aggccgggca gctcatcgcc tacgtcggca cgaacggtca atcgaccggc ccacatctgc + 40501 acttcgaggt gcaccccacc gtgtggcggc aggggtcgca gatcgatccc aagccgtggc + 40561 tggccaacgc gcgcaacccc ggcgaccccg ccccggcacc cgcaccaccc aagggaggaa + 40621 cattggccaa gctgacagat ccgttcaccg gcgagctgtg gtcgccgaac cgctaccacc + 40681 cgcgcggtct cggcgacccc aggtggatcg tggtgcacac ccaagagggc ggtcgtacgg + 40741 cgcgcgacct cgcggcctac ctcgcgcaga agtcgagcca ggtctcgtat cacgtcgtgg + 40801 tcgatgaccg cgaggtgctc aaggtcgtcg ccgagggcga tgcaccgtgg gcggcagcgg + 40861 gcgcgaacaa gtacgcgttc cacatctgca tggctggtag ctacgcgtcg tggtcgcgca + 40921 acaaatggct cgatgtcgac acgtccgacg gcaagaacga ggacttgcag ctcaccaaga + 40981 ccgcgcacgt catcgcatgg tggtgcgaca agtacggcat cccgccggta tggatcggtg + 41041 gccgcaacat cccgccgtgg ggcctcgacg gcgtgtgcgg acacgtcgac ctcggcgcgt + 41101 ggggcggtgg ccacaccgac cccggcccca atttcccgcg cgacgagctc atgcgacgcg + 41161 tgaaccaatt cctcgcgggc accgagctgc cgccgctgcc gacaccgccg ccggtcaccg + 41221 tgccgggcac caagcccgat cagtacggcg attggatgct ctaccgaggc aacccgcgca + 41281 acgatgccga ccgtgtccgg cgcgtgcagc ggcggctcaa ggcggcgtat cggtcctacg + 41341 cgggtcacct cgaaatcgac ggcgatttcg gcccgctcac cgagctcgcc gtgcgcgagt + 41401 tccagcgtcg cagcctgctc atcgccgacg gcatcgttgg ccccaacacc gcagccgccc + 41461 tcaaaccgta ggagatccgc atgacacagc ccaccgcatg gcagccgccg cagaatgtcg + 41521 gcgatgtcgc cgtgactgtc gctcaggcca aggccaagct caaggtgttc agctacggcg + 41581 cggcgttcaa gaccgaaacg tcgaacgtct acaccgccga gttcggcacg gcgctgcgca + 41641 cgttccagca gcggcgtaat gccgagatcc acgagggcaa gaaacccggc cccgtgatga + 41701 acaccgatgg cgtgctcgac tgggccacca agaaacagct cggcatcctg cccgagcaga + 41761 ccgccccggc accaccgccg ataccggcca accgggcggc ggcgctggtg ttccgaggca + 41821 ccggcggcat catcggtcag gattacgtga gccaggtctg ccagcaggtc ggcccgatgg + 41881 tcgaggaaat caaccccgag ttccccgcct cgatgggcgg gctgccgccg ggcgcgccga + 41941 acctaccgag cgcacgccag gccatcgata tcggctaccg gtccggcgcg gcgtggatca + 42001 aggcaaaccc ctcgcgcaag ttcgtgctcg gcggctacag cctcggcgag atcgtggtgg + 42061 ccaagctgct caccgcgctc ttctcgccgg gcggcgagct cgcggcgttc cgcgacaact + 42121 acgtgtgttc gttccacatc ggcccaccgg cccgcccgct cggcggtgcg ttctacggcg + 42181 gcaccgccgc gcccggcgtc ggaatcgcga gcaacagact cgccgccgac atctacgcgc + 42241 agctcggccc gagggcgtgc tatctgtgcg accccgaaga catgtacggg tcgatccccg + 42301 tacccgtcga gggcggcacc ggcgacatca tggaaaccgt ctacgacatg gtcacgacac + 42361 tggccctcaa cgacttcctc aacacggcgg cggccatgct gccgcacatt ctggagatcg + 42421 cacaggacgc gggcattttc gggctgctcg gcctcggcgg cgtcggaccg gcgaccggcg + 42481 gcggcggtct gctcgggggt ctgctcggcg gcggcctcgg cggtctgctc ggcggcggca + 42541 tggccaaccc ggcggcgctg ctgaccaacc cgctcgcggc catcccgctg ctgctgccgt + 42601 tgttcacctc ggcgctgccc ggtctcatcg caggcgtcgg cgggccgggc accggagggg + 42661 cgatcaccgg accggcggcg gcggctcagg cggccatcct cggaatgaaa ttcctgttcg + 42721 ctggcacgcg accgcacatc gagtaccaca tccgcgaggt atggcccggt cagacctata + 42781 tcggtctcgc cgtgcagcac gtccgagatt gggtagggcg agagctgccc gcgtagcggt + 42841 atagtgtgcg cggcggtccc ttcgtggttg ttggtaccca acagcaaggc cggtgtcctt + 42901 tggggtggga caccggcctt gactgcgtta cagggcgtta cgccttggcg agcgactgcc + 42961 gccattgctg gtagagcgcc ttgtcttcct cgctgcccgc aacgaggatg aacggtgccg + 43021 actgattcgg cttcttgaca cccttgacga tccgtcccag cacccacgcg atgccacggt + 43081 cgagggcctt tttgccctca cgcgcgagcg ccttgttgaa caccatgatg tcgtcaagac + 43141 gctcgccgac ctcatacggc tcgcattcct cgacctcgcc gtcccggttg aggaaaccga + 43201 agttgccgtt gtccagccga acagcggtct gtgcgttcgg cgttcccggc tcgggcacag + 43261 tgagcgggat gatgtcgaac cgcacgaact cggattcttt gccgtccttg tcgtgcacgg + 43321 tggccatcgt gccgtactcg gtggggtgca tgagcaccaa ctgcccgagg aagaacgcgg + 43381 gcttgtagcc gctgaccccg gtgggatcgg cggcggcgta cgggtcggac ccaacgggct + 43441 tgtcctcacc gacgttggtc acgtcgggga ggtcggcggg gtcgaccttg cccttgctcg + 43501 gggcgggcgc agcggcggtg gcagcgccac ccttgcctga aaacgggcta ccggccatgg + 43561 tggtgatctc ctttgtgttt gtggtgatgg tggtgttgtg gactgctctg aggtctcaga + 43621 gcagctttgc aaccgtggtc gcaaagtcag tgagggtgtc gtcccagaca tcctgatatt + 43681 cggtgtagac ccgctcgccg tcctcggctg actcgatagc ggtcagagcg aggcgtgcct + 43741 cggcgtagcg cacggactgc ttggtcggtg cgggcacggc gtgccggggc acctcggtct + 43801 tggcccggct gcgcatacgg cgggtggtcg aggattcgat gagcgcctcg gcaccccaca + 43861 ccgcatcaat cgtgatcgcc gaggctttct cggggtgatc gctcggcacg tgcacgagga + 43921 tggcgaacgg gtcgcgctcg tcgtcagtct cgtcgtcggg gtgcgggatg ccgatgagct + 43981 tgggcatacg ttcccaaccg tcaccggtag cgttgagcac gtgcggtgcc cacgagtaca + 44041 ccccgccgat ctggactgcg aacgcgagcc acgaatactg catgctgtcg accttggtgg + 44101 tcttgacatc gccgaggacg agctctccgg tcgagacgat ctggaaaatt cggtcgatct + 44161 tgcccgctgt cgcttcctcg ccgaggtcat tgagcacggt gcgttcgacg tactccggta + 44221 gcgccaccag acctcggtgt gcgagcaccc ggcggtaggc cagcgcgtga ggccggacca + 44281 tgtcgggtag atcgcgcatc agcaccatcc cgaggtcgag cgcttccagc cacgcgtgca + 44341 cgcactcgcc gaactcgcgt gcctcggccc cgccggtcac gttgtcgatg gtctcgatga + 44401 cccggtcgat ctcgcccacc ttggtgtggt tggcgtagac atcgcgcaac tcggcgagca + 44461 gggcggcggc ggtgtagtcc ccgcccggtc ccatcggctc gttggccttg ccggtgatct + 44521 cgcggtgcac gaggttgagc acggcaccga cgacgttgcg gcggttccac ttggccagcc + 44581 cggtggtgtc ttcgagtgtc ttggccaccg tggtggcgcg cgggtacgag gtcagtcggc + 44641 ccgtctcggg gtgcggcaac atgtactggt gccgaccgtt gtatttcgac ggcgggcgcg + 44701 gcggttcggg cggcagcgga tagcgcatcc actcggtgtg ccgggggtag aaatcgccat + 44761 tctcatcgat tgccgtcatg gtggctgtct cctgtcgtgg ttgttgggtt tcgtgcgtct + 44821 gcggggctgt ggggccggtc ggcggggcat cggggtcgcc gtcccacgtg agcaccgaac + 44881 cgtccggcgc gatcacgacg cggtagtcgg cgtgcgcctg ctcggggtca gcggtccatc + 44941 cgtcgccgtc ggcgggacga aaccacaccg agccgtctgg ctgccgccac gcggggaact + 45001 gccgtgccac gctcaccacg gttacgcctc gatccttgtg atgttgcggt cgaggcggcg + 45061 gctgatcttc accgtcgaga tctcgtcgct gagctcggcc ttggtcatgg cgtcggcacc + 45121 gacgatgccg aggccccgtg cgaaagcaat ctgcttggcg gtggcgggct gcttgcgccg + 45181 ccatgagtct ttcttgctcg gcagccgttt accgctgttg acgatccacg cctcggcggc + 45241 ctcgcaggca gcctcgagcc cgatgtaatc cggctcggcg acaccgaccg cgccggaccc + 45301 cgatacccaa ccaccgcgcc cggtcttggt gctcatggtg gccaccgccc acgacgtggc + 45361 attggcgtcg agcggacggt acccgtcctt gggccacacg aacacgatct cgtcatccat + 45421 gagcgggatg aacgggacac cgagggtggt ctcgcaccac gtcacatcgc tgccggacag + 45481 cagatcaatc acgaccatct cgaccggtcc ctgccgaacg agcttgggtg tcgggtcgct + 45541 gccctcgccg ggcagctcgt cgtcgggttc gatctcgatg accgagccgt cctcggtgac + 45601 ctcggcggcg tcgacaccgg gcacgagttg cgtgaggttg acgagtttca tcgagcgcga + 45661 tgacccggcg aggtcgagca cgagggcatc ctgcttgccg tcgtacagac gcagcgcgcg + 45721 accgatcatc tgcgagtaca ggttgcggct gcgggtgggc cgggccagca cgacgcagtc + 45781 acacatcggg aaatcggcac cctcggtgag cacctgcaca gtgaccagcg ccttggcggt + 45841 gccgtttcgg tacgcctcgt atacgggctg gcgctcggcg tagctcatcg agccggtgac + 45901 ggcgacggcg gggaaatcgg cggcggtcag tgcgtcggcg atgtggtggg cggcgtcgac + 45961 cgacgcggcg aagatgatcg ggcggcggtc ggcggcgtgc agcttgatcg cgtcgaccac + 46021 gtactcggtg gcggcttcca tgacctcggc gaggtcggac tgctggaaat caccggcgac + 46081 ggtgcgcacg tcatcgaggg cgttgaggtt attgatgcgg acggtcagac cgcgcgggcg + 46141 cacgaggtag ccagagtcga tggcccagag aatgtctttc tcgtaaacga ctttctcaat + 46201 cacgtcgccg aggccgaccg ggcttttgcc cttgtcgtcg cgacgcatcg tggcggtaaa + 46261 tcctgcgaac agtgcgtcgg tgtatccgcc gagctctgtg aacgtcgtgt ggaaaccctc + 46321 ggccccggcg tgatgcacct cgtcccagag aatgcgtcga cggaaaccga cagcctcgcg + 46381 gcggtgcgcg gtggccaacg tctgcaaggt cgcaaccacg atgggcgcgc tgtggtcatc + 46441 catttcggcg cgcacgatgc cgacgtggct gcgcgggatc gaggggtcga cctcgaaaat + 46501 ggtacccgcc atctggtcga tgagctcacc acggtgagcg accatgagga cgggctcgcg + 46561 gttctggtat ccgttgacgg cagtgcgacc gatgaccgtc gacttgccgg tgccggtggg + 46621 gagaacgaca cccacgcggg tcacgccttg cgaccagtga gactcgatgg cctcgactgc + 46681 ttcgcgctgg tacgggcgca attcccgagg cgctggtgat gtcatgttgg ggtgctttcg + 46741 gtgtggtgtt ggtgtgttgc tggtgtgttg ctggtgttgg tgtggtgacc ggctgacctg + 46801 agcgggcttg ggtttcactc aggtcaaccg gtccatcaga acttacccga actgttcggg + 46861 gttgtctacc cctgcggcgc ggacgaccat tgaatacgtc catccgcaac cgggtggact + 46921 gtcctgcgga cgctcaccat gccgcgaggc agcgttaccg cagcgaacgc actgaacggc + 46981 acggtcttcc gcagcaccca ttaacgcttc gaatgccaca aacctcggat tgccagtcac + 47041 gctttccccc tcggccagtc cgctgcgatc actgatcgcg gaaatgcggg tgattgtgca + 47101 gcccgagtcc gccaacgcgc tcacgaatcg ctcgcaggcg atgatcctgt ggaggcgcgc + 47161 taaatcctcg ggcggcgtaa gtcccgccgc gtccagcagc atcgccccta aaagctcctt + 47221 gttttcatac ggatctcgcc cactcacttg tcttccccct cggctacaac agcaacaacg + 47281 ggcacctgat ggggctcgat actgccgtcg tcacggacgt agcggttggt cccgcacgca + 47341 gagcacttgg cgacctcgcc gtcgcggctt agagtgccgg gcttgaacgg tttccccgat + 47401 cccgcgcagt cctgcatcgt cacagcctca gtggtgtaga tgagcttggc ggtgtcgcat + 47461 ggccaatcga cctcgcagtg ttcgcatacg cggtctctga gtcggcagtc ctctccgctg + 47521 cagcaggcgt tgatgcagtt gctccaccga gggcggtgca gttcgcggat cggcttcaac + 47581 gcctcacggg cggcggtgat caggtcatgg ttgggccttg ggtagtcgtg cactttcctc + 47641 atcgcccgtt gtgcggcttc tactgctgga tcgttcattg ttcccctacc cgtcgtcggt + 47701 gagctcggtg ttctggatct tgacacgggc ataggtctcc cgttcggcct cgtcggcctg + 47761 ctcgtcaggc cgacgaccat gggctttcca gaacccgcgc gctgccgagg cgtactcggc + 47821 tcggatgagc tcggcacctt gcatcttggt gctcgcgccg atgacccgac tgccgtcggt + 47881 gacgacgtag atgtcgctca ttcctcggtt cccgtcttga gtcgctcggc tttttcctcc + 47941 atgagcttgg ccagcgtcga cagcggcata cgcgtccacc acgctgcggg cagcgcaccc + 48001 gtgctcggga caccgtcgcc gatgagctgc cgggcggtgt cgaggtcgac ggcggtcatc + 48061 cgagccaccc cggctcgtcg tggagcaccg tggcgtcggt gaccgggtag acctcgatgc + 48121 ccttgctgcg tcgaggcacc acatatccgc catcgcacga ggtggacgcg tagtaccgac + 48181 tgccataggt cgtgcacgtg aacggtttgt agtagctcgg cgaccagaac tctcgatagc + 48241 gcgtccatga gccgtctgac cggcgcggtg tgtcgcacag ggtccgagtg ttcgatcccc + 48301 acaagatcca catctcggtc ttgcaaccgg cgatctcgtc agccgaggcg gcgggcgcgg + 48361 cggccaaggc accggctgcc gtcatcgcga cggcagccag tgcggccttg agacggcggc + 48421 tcaccacggg tcggtggttt cgacgtggtg acgaccgcca ccgcagcgga tattgcagcg + 48481 cttgacggtc ttgagaccgt cctcggtcat gactttcttg ggggtgccgt cctcgttgta + 48541 gacggtcacc cacaccgccg agccgccgtt gccggacgca cacgcgtgct tgtagatctg + 48601 gcccttgccg aaaccgtggt tgtcgcacat cttgggcgcg gcgtcggcga tcccggcagg + 48661 cgcgagcgcc acaccgagcg cggcggctgc cgcgatgatc agggttttca tgggtggtct + 48721 cccttgtggt tgttggtggt tggttactgc tggtgatgaa cttacccgca tgtgtcgggt + 48781 aagtcaacta gtccccgtcg agcacctcgg cggcagccag cgtgcgagcc tgttcgatcc + 48841 cgacctgacc ggggatgtaa cggctgcccg aggcggtgcc ggtgagcgtc tcaatgaggt + 48901 tgaaccgcag acccatatcc accgcacgct gaatgtgcag cttccagtat gcggcgctgt + 48961 cccgacggct gcgggcatac gcgtcggcct gcacggcggt gaggatctcc gcgcgcgtgg + 49021 caccctgtgt cggcagatcg ttgacgaact gccggatgcg cacggcggtc tcgatgacag + 49081 cctcggggcg gggtcgagcg agcacgattt ctccctgcat cgggtcgacg ctgccgttgg + 49141 ggccggtgat cagcggggcc ttgatcccgg cgtcctcgtc ctcgtagctg accatcatca + 49201 gtgcgatggc ttcctcgagt gcctcggcgt tcttctgctt ggtggtcatg acttccatcg + 49261 ggcggcccgg caggcgtcca tcgatcatcg ccgcctcggt gtcccacgtc gccgcccgca + 49321 ccacgagctc gctgtcgagg gcaccgttga gcgccgacga acctcgaccc tgatcggtgt + 49381 gcgccttact cgtgtggtgc actacgcaca caccagcgcc ggtgagctca cgcagcttgt + 49441 cgtagcgccg gaccgcgaca ccgacatcgg tggcgctgtt ctcttcaaga cccgacgact + 49501 gacgcgcgaa cgtatcgaag actacgaggc cgatctcgtg ccgggcgatg tacgcggcca + 49561 ggtcacccca tgcctcgtta ctggccttga tcaggatgat cgagtcgccg agcatcagat + 49621 cgttgccaag gtcgacatcg tgattggctt cccacgcgcg caaccgttgc acagcaccgg + 49681 cgagaccctc accgggcaga tacagcacct tggtcttgag ggtgcggcgg ccctgccacc + 49741 gcttgcccac cgcgatgtgg caggccatgt cgagcaccac cgacgactta ccgatgccgg + 49801 gcggcccgat gatcgaggtc agaccgccgt gctcaagcag accctcgatg acgtactcgg + 49861 gcggcggcat atcgcgccaa tgcgagaacg gggcgatgcg cggcacaccg ctgtgcggac + 49921 tgtcgtagac atcggggtcg gtctgctcgg ggtcggcggc ggcgtacggc gatggttcgt + 49981 cggcgggctc ggacttgccg aaatcggcgg gcagatcgct gaggtcgtcg gcgtagctcg + 50041 tcggcgtcgg gcacggctca ggaccggcgg tgtgcgcggt gttgcacgag gcgtcgtgcg + 50101 cgtaggtggc gggcgcgggc tcggcgaggt gctcatctcc tgcgctgccg ccctcgggca + 50161 tgtgccacat gtcgccgtcg ccgtcgacca cgaacgcacc ggcctcggtg tcgcacaccg + 50221 ggcagaacgg tggatcgtca ggcgtcgagg tcgtctcagg ctcgggcgcg gcgggcagct + 50281 cgaaatcacc gtcgccggtc atcgcgtgat cggggtcgac ctgcttgggt gcgatgtcgg + 50341 ggtcgacctc gactgccaaa tccggcgtca gcccgatctc gtccatggcc ttgccgatgt + 50401 tgcccccgta gtcgatgagc gcaacggctt gcagcttgct gagggtcttg gtgcccttgt + 50461 cggagatcca cgcctcaaat ggctcgccgg ggttgtcagt ccagatgtgc agcggtgcgt + 50521 tgacctcggt gtaccggccc agtgcgcagc ccgtgtcgtg tgcggtggcg ctcttgggtg + 50581 aggcgtggac accgggtgcg gtccagaccg ggcatccaca cgcatcagcg cgcggtgcgg + 50641 gggtccaacc gagcggctca agaatcgctg tccacgaggt ggcctcggcc cacccgtcga + 50701 tggcctcgct gagctcaccg gcctcgctgt cgcggtgcac acgctcggtg cgctcggccc + 50761 ggcgcgcggc ggcctcacgg atcgcgtcga gcagccacgg tgcgtcggtg atcgggtagt + 50821 cgcgaccgac cacctcatag gaaccctcgg ggcgggtcga cggcgggatg agcacatacc + 50881 gcttgtccca caggactgcg aaaccgtcct cgccgcccca cgtcatcgcg ccgaggttgc + 50941 gcggcagcgt cgggaggatc tcgtcgggca cggtgaagta gaaatgcccg ccgtcgctgt + 51001 gtgcccacga gctcggatcg gcgaggtcgc cgtccttgcc cgcgtggccg ggcgtcacca + 51061 cggtgggcgg tggcacgtcg tcaatgtcga tgtcctcggg cagtgcggcc tcgtagaacc + 51121 tctgtagctg ggcagaagtg tcgcagtcga ccacgaccag cgccgagccg ccgacctcga + 51181 cggcgacgtt gaccggcaca ccctcggggt accgctcggc gtacagggtc aggtaccgct + 51241 tcagataacg gtcgagcact gccttgtccg aggtggccag cgtgagaccg gctggcgatt + 51301 tgactcgcgc ccagtcccgt ctaccggcct cacgcgcggc ttcctgcgcc gccttgtcgt + 51361 cggctttctt cttctgcggg gtgcgcaggt cgacgggtgc cttggaatcg ggcgcgatga + 51421 acatgatcga cagcccgata tcggcggcct gacgcacaaa cgcgcgcacc gcctcggggt + 51481 cgcggttgtc gatgccagcg ccgaggatgg cggtgatggg ggttgatccg agcacggttg + 51541 gttcctgttc gtttcgtggt tgttggtgtg cggaatggcg agcgtagcgg gtcatttcga + 51601 gcccgtcgag gcaccgctat gggtggggtg cgacgactcg acgggaccct acatctaggg + 51661 tctctcgctg gtctgctggc gagatctgac ggtgcggacg gcggcgagaa gatcggctgc + 51721 aaacacctcg gcctcggtga cggtgagcgt ataggtgtcc tcgctgcggg ggccgatctc + 51781 gatggagtca cttttgaccc gaatgaaggg gtagtggttc cacaccggct catcctcgtc + 51841 gtactccgag ggccgggcag gcaacagcac ggggaatcga gacgacaagt gccccgccgg + 51901 taccgccggg gcggccccgc acttgacgca cgtgagccac ccgtctccgg tgttgccgta + 51961 gtggtgctgg cagctctcag tcatggccgc cccgccttgt gccggatcat gaccccggcc + 52021 ccgagcgcga tgtaggcgac cgcgtcgacc catgagtcgt gatggtccgg tgtctcgcgc + 52081 agccgggcga tcttgagcgt ggcgaggcag agcgcgacct gctcggcggt cacctcggtg + 52141 cccaggattg ccgaccacat cgaggcggtc accccgaaat tgtcggcggc gtcgccgtag + 52201 gtggcctgcc tcggcccgtt gaacagtccg atggcctcac gggcgatgtc ctcggcgatc + 52261 tgcccggtgg gcgggatctc gggtcgctcg ggagcgccct tgagctgcgg cggggtgccg + 52321 atgccgacgg tgcccggttt gggccgctcg acgggcacct cggtgggctg ctggccccgt + 52381 atggtgtgga gctcgtcggc gcagcggcac gcccggtcat cggtcttgtg cgggcatccg + 52441 tgcatcgggc agtatccgcc cacatcacac gtgcacggtt tgatggtcat accttcagcc + 52501 attgcgatcc catatctgct cggtcggtgc gcagcaccgg ggtgcgttcg gcccacctga + 52561 tcaagaactc aggcggggtg gacatgatct cttggacctc ggcggacacc gtggtggcca + 52621 ccacaacctc gtcatgcatg gcgaggtaga gctcgtcggt cattccgcgc cggtacatct + 52681 ccacgatggt gttcgcgagc acgtcatagg cgctgccctg aaccgtgtag ttgaccgcct + 52741 tgaaaatccc accctcgtca accggcagga tgcggccacc ggcggtgatc actcgaccga + 52801 actgggcggc gacttcctcg accttgacca tccaccgcgc cgagcgtttc atcgcagcga + 52861 acatctgccg tttgatctgc gcagtcgact cttcggtgtg cccgatgagt gcggcgagct + 52921 tggcgatgcc cgagccgtac atcgagctca gaagcaccac cttggatgtc gggcggtcga + 52981 tcccggcggc cagcatgatc gggccgtaga gatcctcccc ggcctcgtag gcggcgagga + 53041 aatcgatgtc gcgcgccatg ttggccatcg tgaccggctc gatctgtgac cagtcaatcg + 53101 aggtgagacc gtcgccgtcg tcgcacagga tcggacgggc gtccttgggg aactgctgca + 53161 actcggggct gccgtagctc atgcggcccg tcgcgctggc cccgagggtg gccacctgag + 53221 ggtggcaccg gccagtgacg gcggcctgag tgctgacctt ggtgaggtag ccgagggtct + 53281 tctcgatgtt ggccagcgag agttgcgcct tggccaacgg gttgatctcg gccagatgct + 53341 cgaggtcggc cttggtggca cgcagcgcac ccttgggagt gcgcggccac ggctgcggca + 53401 gctcgcccag atcggcgaga tactggacca gcttggcccc tttgccggta ccgccgtcga + 53461 gaccgtgggc ggcgaggtcg gcggtgtaga tcgcttgctc gtcggcgacc tgattgaggt + 53521 agcggtccag atattcgcgg tcgacgttga tgccgagcgc cgagcgccac aacatcatgc + 53581 ggtgcacgat ttcctgcgtg tcgaggatgg cctcggcctc ggccaccgtg gtggccccgt + 53641 acacctcaaa cggatggtcc aaggtccacg caatgcacat ccggcgtagc accggttcga + 53701 gggcgagggt ggccacggtg tcggccattg caccctgccg gtagatcgga ctgtcgatgt + 53761 ccatgccctc gtacccggcc tgctgtgtct tgtacccggc ggccttgaat gcgcgctcca + 53821 tgccgccctt gaactcggac agtccgagat agcggatggc cagtgcggtg agggtcttgg + 53881 cctgaccata tggcggcggc tcgggcagcg cgaacctcgc gagcacgacc gtgtccacga + 53941 tgcgtttctt gatcacctca ccggtcatca gcccggcgtg gaacagcgga gggatgtcga + 54001 acggcgcgtt gtgcaggatc accggcccgg ccagcgcgta catctcggcg acgagcgcgg + 54061 cgtgctgttc gtcgcggcgg gggtcgagca gaatcgagtg cacgcggccc gagtcgggct + 54121 gagcccacga catcgtcacg caattgatcg tgaacgagtt gtcgagaccg ggggtctcga + 54181 tgtcggtggc cagcgcgatg gtgttcgccg gtccccgcgt ggcctgccgg acgaactcgg + 54241 caccgaaacg caccgcgtgc tcgccggtgt gcatgcgggc accgagcagc ggatcggacc + 54301 acgagcgtga aggcacccgc acaggggcgt cgatgatgtc ggtcatgctg tggcaccgcc + 54361 ctgtagccat gtgacgaacg tgtcggcgag ctcggtgacg agatcagcgg ggacacgtga + 54421 gttgatgttg atggcgtcgg cggcccgcac gcgggcggcg gcgttgaggg cggccatacg + 54481 caccgacatc ggtgattggg caggcggtgc cgacggggtg acgacacggt cgtcgttgcc + 54541 cggcccaatg cgcacgacgc gcgccgggta gttgatcccg gcctgctcgg gggtgagcgg + 54601 ctgccacccg cccggcgaga tccacacgac gcgctcgcac aggcactcgt tgaacttgca + 54661 cgcgatggca tgcgtttccg caccctcgat ctcgctgcgc acgtaggcca ccgccgtgct + 54721 ggtctcgtcg ccgatgatgc gctgccactc gatgatcgtg ccgtcgggca gcgactcgag + 54781 gtggtcctgg tcccagatct caagcatcgc gggcctcggt gatgcaggga tggtcctgcg + 54841 tcggtttggc ggtcatggtg gttgttcctt cctgtggttg ttggttgtcg gggtctcggg + 54901 tcaccacgtg atcccgcagt cgcactcgtg cgcttgccgg gcaatgtgcc cgtcggtcat + 54961 cacgcacacg tggtgctcgc cggtgatgtc gcaccactgc tcgtcgccgt cgaacacgtc + 55021 gtcggcgagg tcttggcata tctcgcgggc catcacggca cctcgattgt cggtgtcgag + 55081 gcggggtcga ccggccacgg gtccacgagc acaccgtcga tgtagagacc gggcgcgtag + 55141 ccgtcgacgt tgagcggccc gcagatgtgg tttccgtggt atcggcagtc gaacgcgggg + 55201 tcgtcctcgg gtgtcggggc ggggtcggct ggccacatcg cgaactgctt ggtggccaca + 55261 gtctcggtga ccgacaagcc ggggtgtggg atcgagacat cggcgggcag tccggtgagc + 55321 accagcgccg cgccgacggt cacgcctagc gcggccccgg tggcgacccc ggccaggaag + 55381 gccgagcgaa tgcgtgggtc ggtgaacatg gggtggatct cccttgtgat cggtgcggcg + 55441 gggcggtccc gccgtgggtt accaagatat accccgccgc cccggctcac gtcaatactt + 55501 gcccgacata tgtaggtgtt ctcatttacc gccgggtgct cggccctcac ggtggccggt + 55561 cgaggcccac cgctggcccc gctgcgcttg gtagagcatg gcgagctgcc gggcggtctc + 55621 gggcgggatg gtggccatga tcgagccgaa caccgtgcgc tggtcgtagc cgccgagcgg + 55681 gccggggtcg gtggcaccgg acggtcgggt catgagttcc tcgaccatcg cgcagaacgg + 55741 gccgaacgtg ttggcgtgct ggggccgggc ggcggcgtag ccgagtgcga acgcgagcac + 55801 cgaggtgggc gcttgcgaca acggggtcga ggcggcgttg cgcagccggg gtccggtggg + 55861 ggcggggagg ttgtcggtca tgatccgacg gtaggtgacc gatgtcgggt aagtcgagcg + 55921 ggggacgaaa catattcatg tatacgcatg tcatatcccc gccctcacgc gcgcgtgcgc + 55981 gcgacagcgt accggctgaa cacccgtttt gtcaagggtc aatttcaaga tcaattacca + 56041 ctacatggtc cctcgaccat gggcttgaca aggtgggtag gatcaaagcc agcaagcttg + 56101 gcgttttcct aaaccccccg tgtccccgcc ctcgatggcc tcggtgacca cgtgatggat + 56161 gagtgggtga aaacgcgtaa tcgactcaat gcaatgtcaa ccgcattgac atgccgcact + 56221 cgcaaaagcg cctctgggcg ctactttgct cgcggtacgt cggcgcgccc gaccgagctg + 56281 ccgtcagata cgcagtgtgt tgcaagatat acttttattg ggtacggtcc gactcatggg + 56341 aacgaaaacg tcaccggtga gactcgatat ggacctcgat gagttcaccg gacatgtgaa + 56401 tgatctgctc gggtacgaca cgcagcttgg cgagaacatc cgcatatggc ggcgtagtcg + 56461 caatgtcatc aacacgctcg gcagcggcag cgcgttcgcc aaggaactca acgccgctat + 56521 ggatgcggcg gtgggcagcg tcggggtcgc cgagggtctg tgccatggaa cgaacaaacg + 56581 ggtgtaccgc agacacatgg cggcgcacac caagaccgtg gtgcgcagcg aggcggtgaa + 56641 gaagcggtgc cccgagctgt ggctcgccgc acgcaaaccc agctttcgca tggggtgcga + 56701 caccaagacc tcaccggtgg cggtgcctaa gatccggccc ggctatgtcg gcaagctcat + 56761 cgaggccaag accgaggcgg cgcagcggct caagtacgcg cgcgagcacg agctgcgcgt + 56821 caaggaccac ctctacacca cgacgttcgg tatcgaggct gaggatgggt ggaccggaaa + 56881 ccctccgtat gcgctcagcg acgggtgggt gatcgggtgg accaagacgc agcgttacag + 56941 cgaggcgctc gcgaaagaaa aggcacccga gttcggtgtc gacctcaccg agatcactga + 57001 gtatgtgacc gtcccgcctc gggtgttcta cgtggtcggc gacctcaccg gcaccgaggg + 57061 cagccttgag gattacgagg gagatggcta cgcagactga gcagtgaggc ggtggctcgg + 57121 tgcccttgtg gcccgcctga gcaattctgg tatatcttcc taaccatgag cgcaccgagc + 57181 cgacacacac gcagacagcc cgcccgtgga cgcccggcgg gagaggttcc caacgtgctg + 57241 tggcccatcc gagggccgat caccatccgg cagcgcatga agatcgcatg tgccgaggaa + 57301 gggctcacct acgcggggct gatcgagaag tttctagacg agcgcgacga gaagatccgg + 57361 cggcagcttg ccgcacaaaa atccccacta catcgtccga aacgagagat atgaccatga + 57421 ccgagctcaa cggcatgacc gatgacgagc tgcgcgcccg tctcggcctc acgcccgtcg + 57481 accgccctca gaccatggaa gagcgcatag aggcccacgt ggtgcgcgcg atgcccgagc + 57541 ccgaggactg cggcgtgtgc gacggcaaag ggtgcggtga ctgctatgca tgaggcgctg + 57601 atctgcaagc actgccgcac cgaagtgtta cccgcaggcg acggctcgca ccgtcaccgt + 57661 cgcaccaaca agaacacctg cgaggtcgag ccgtacggct tccacgctga gccgatcggc + 57721 acgtcatgcg gtgaccaccc ggcgaactcc tgcctcggcg cgtccggcgc ggtgccgttg + 57781 cgaggacaat cgttcacatg accgacaccc ctcagaccga caccgacgag gtcgacaccg + 57841 agttcgccga gctcgtcgca gccagcatcg aggaacgacg gggccgggtg ctgctcatgc + 57901 gcttggccgg atcgacgttc aagaccattg ctcaggcggt gggcgtgtcc atcggcaccg + 57961 tgcgcaagga ctacgacatc gcgctcgcgg cccacctcga tgacccgccg gacatgatga + 58021 tcgcccggca gcgcgcggtg atcaccgaca tcatgcgtgc caactacccg tccatgctca + 58081 acggcgacaa agaggctgcg cagaccatcc tcaaggcgct cgaccgcgag gccaaactgt + 58141 tcggcctcga cgcacccaca cgtgtgctcg ctcaggtcaa caacgacgat ttcgccgtcg + 58201 aggcggcgcg attgatcgag cacatccaga agattgaccc caacgaactc aaggagctgg + 58261 cccgtgccgg acaaccccaa caaccacgac cccaagatcg tcccgctgcg caggacaccg + 58321 ccgtcgggcc actcgatgtc gagctcgccg aggacggtgc tcaagggcct gagcgagatc + 58381 ctgcgggatc tatcgacacg gatacttccg agcccgatgt cgagcccgct gccgccgagc + 58441 ccgtcgccga gcccgatgac gactacgacg attggtccaa cctcgatgac gacgacattg + 58501 cctgagaccg gcccgctcat caagcacacc gccgtgctca gcgagcgcca tctcgtggtg + 58561 gtcgagctgc ccggtcgtca tcacgatgtg ctgccctcag agttccccga gatcgtcagt + 58621 gcgtgcgaac ggcgggcgcg cgcgtgggcc gatgcgcacg ggcttgagct cggcggcctc + 58681 ggggtctact cgtcggcgtc ctgcgagggc aaaccggcgc gcgtcggcgt ttcattcgag + 58741 gtcgtggtcg gcaagggcga gatcgacgcg gaccagtcac cggcgggaca cctcgaccgt + 58801 tggcggtggt cgggcggtcg gctcgactgg tggcgcaacg tgttcaaccg cagcgatcag + 58861 cgcgagggcc gaaagctcgt cgcgcgcgag gaaggcgaca acatcgtcgc gttcccgagg + 58921 ctggccagtg tcgattgggc cagcgccgca cccggcggtg cgggcggtag tggcgcgagc + 58981 gggcacaccg ggcagaagtc gagccaggtc tcgtatcacg ctgtggtgga tgaccggaac + 59041 atcggcgagc tcgaccggcc cgaggacggt gcccagtgag cggcgacctc gaccggcagc + 59101 gcatgtggaa caacgggcat accgacgacg aggtgatcga gggcaccggc gcggtgttcg + 59161 atgccacggt gctctacggc cagaccgacg acgttgacga gcacctcgcc gacgcggtcg + 59221 aggccgggca gctctccggt gacccggcgg cggtcgacgc gcgcgacgag cgcgcgttcc + 59281 tcgcctcgat gcgcgacaac actgacgtga ccgtgcggac cctcggtgtg ctcggcgtgt + 59341 ggctgctcga caacggtgtg gcgcacccgt accggggcgc gcgtgcgttg ctctccgagc + 59401 tcgccgagta cgacctacag acggtcatcg gtgccgacct cgacacgttc atcggtgaga + 59461 tccaacggct acgggacgag cgtggcggca ccgcataact cgccgtttcg ggaccggttc + 59521 gggcgctact gcggcccctt gaagatcaag accgcagcgg tgctcgatcc ggccccgtac + 59581 gacgacctcg accattgcat cggctgccac cggcatgccg tcgacgggca cgagcacggc + 59641 tgcccgtacc ggggcgcgtg ggattgagtt ccccgccctc ggcgggcaac gggcctatgc + 59701 tgtggccatg acccccgagc cggttaccgc cccgcacatg atcgagcatc gagtacgtct + 59761 gacccgtgac gaggccgcag cggtcgccgc tggcacctac gtgcccggca cggtgcccgt + 59821 ccacggcgcg cccaccgtca tcgacaagcg cgcgcagaac gcagcccgcg ctcaggccga + 59881 gcgtgacgct caggcggcgt gggtgcggca ggcggcggga acgctgtggt ggtcggcgca + 59941 gcgcgtcaac tcaccggccc ggcgcgagaa gatcctcgcc aagcggtccg agctcatcac + 60001 cggcgcgctg gccaagggcc tagacctcgg cgggctgact gtctgacatg accgacactc + 60061 tcgaccgcga cgacctcgac agcgacatcg aggcggcacc gttgcccgcg tggcagccag + 60121 accatagcct cgtgcccgcc attctcgccg gatctccggt gaccatgaaa cagctcaggc + 60181 cgccggatcg tgcgtgggcg gtggcggtgc tgcggcgcat gggctacacc gccgagctca + 60241 tcgccgattg gctgtcgtgc tcgctgcgtc tggtgcacac catcagtgcc gacagcgcgg + 60301 tgctcgtgca gctctacctc gaccacaccg agaccaccgg caacgagcat cgcatgctcg + 60361 ccagcgaggt ggcccggctc gccgaggcgc tcgctgatgc cgaggccgcc gccgagcgct + 60421 acaggcagca gcgtgaccgg ctgatcgccg tcggcagctc cgacaaggcg acgttcccat + 60481 gcgggtgccc gcgcacccgg tacaacacct acgtcgcgcc caagaccggt aaggcagggt + 60541 gtcggcacca ccgcacgctc gctgttgccc gacatcgggc gcgtaaacgg cagtccacgg + 60601 gggtagcctc aggccatggc cagagcaacg agggcgcggg gcacccgtag ccgactaggc + 60661 cgggggtctg ccaggatcgc acggtcacgc cagcgcggca cgatctcgat gtcggcggcg + 60721 accggcggtg tcggacagtt cgcgaaacgg ggtggccgcc ggggcggggg ctaccggggg + 60781 ttcaagagca agaaacaatg gcggtgggcg tgggccacca agcagccgtg ggcgcgcaag + 60841 aaggctcacg agaccaaggg cgggccgaaa gtaaggtatc ggcggctgcc cgagtccaag + 60901 cactcgggcc accgtggtcg acgcggtccg tcccgtaagg ggtgaccgac gtcgggtcat + 60961 taccggtcat tacaaacgag aaaaccccgc cggggcgctc ggcggggttt ctctgtgtct + 61021 cagacggtga ccacgtcctc gaggcgggtg aggtatccct cggcccagac gatggtggtg + 61081 cagatgtacg ggccgcccca cgaggcgggg cgctcacgac gacggcggtc gaactcggcg + 61141 acggtgtagg tccggccctc gtgggtgagg gtgtcaccga ccttgaggtc ggcggcggcg + 61201 gcgcgaacgg tggcgtcaaa gctcatcatg atcgttcttc tctctgtagt tgtgtggtgg + 61261 cgggtctccc gcccgacaag aattaactta cccgcctatg tcggcattgt caagcaatcc + 61321 atcctcggcg ctgccatgca gctcgggcac cgccgacggc gacgaggatg ccgttgcggg + 61381 tctcgcgggt ctggcccgag gtgttgtgct gccacagcac gaacgtctgc ccggcctcga + 61441 cgtactcggc gacggcggtg aactcgggca cccacctgta gtgcttgggg gcggggcggt + 61501 agccgagaag tttcttggcg cactcgacac cggcgcggct gccgtcgttc atcacgcaga + 61561 tccaccgcag accggtgcgt ccgcaggccg ggcactcgcc ggtgccctcg tggtcctcga + 61621 ccttgacgag ttcgagcttg gcggcggtgg cggtgttcgc gttcatgtgg acaacaatac + 61681 cgtaaaagtc ggtattgtca agcccgcccg tcgacaggac ctcgcgcaac gaaaaacccc + 61741 gccggtgagg gcggggtttt cggtttaggc gaagtaggcg tcaatctcgt cgttgaggtc + 61801 atcgagggtg tggatgccgt cggcggcgag ctcggcctca aggcggcggc ggttctcgat + 61861 ggcggcggcg cgagcgttgc gagcgacgaa gtcgttgagg tgggccattt cgatcttctc + 61921 tctgtagttg tttggcgggt ctcccgcctg acaagaacta acttacccgc ctatgtcggt + 61981 gttgtcaagt cctggcaact caacgtcgac aatcgtgacg tgaccagcgg aaacgcactg + 62041 cttgcggatg agcgcttgct gccggttgat cgtgtcgcgc cactcttgga atcgggcagc + 62101 tcgcgcgtca cggacaccct cgaccgtgtc gtcacgggcg gccccgctgg caatcttgat + 62161 cgacggggtg atccggtagt cgagccattt atctgaggcg gactgacacg ccgcgcatag + 62221 cttcaccgct cgcccctgag atccgcctcg tcctcggggt ggcggcagtc gaggcacaga + 62281 tagcgcccct cggcgtccca cgccatcacc cggcgcaccg gcgtacgacc acacccggcg + 62341 caccactcac gggcctcggg tgcgcggctg tagggcaggg gccggttgtc gcgcgcacgg + 62401 cgtcggtcga tgtgggccag cagcacccgc gtgagcgggt acagggcacc ggcgacgctg + 62461 ccgacgagtg ccacggtgcc gacatcggcg aggtcgggcc acatcaggga accgcctcga + 62521 aaatgacgtt gaccttcgcg cccggcgaca acgtgcgcac gtacggcacc ccgtcgtggg + 62581 tgtgcccggc aaggtaggtc atgccgtcga catcggtacc gcaccactcg accacgaggt + 62641 aggcgcggcg gcggtgcggg atatcgaccc actcgcccac ggtcacggtg tcggcgcggc + 62701 ggctcacccc gctcacgctg ctcagatcac ccattggtca cctcgaccgt tcggcgtttc + 62761 ctgagttctt ctacgcgttg cagtgcccgc gtagcggcgg cctcggcctc ggagatctcg + 62821 gcgttgaggg cgtcctgttc ggcggcgatc accgcgcaca cgagctcgtc ggtgtcgctg + 62881 aacatccgct catcgagctc aacctcgacg cggatgagcg ttctcggctg cccgttgtag + 62941 ttggtggtcg gcccgcagaa gtcccgctcg tggcggtgtc ccttgcggtc gacggtgcgc + 63001 agcacctcga cctcgacgtg gtcgacggca tgatcgttga tgtgctcgtg gagcaccgag + 63061 gcgacgagca tgcgcacgtt ctggtctcgg gcggtgtggt tgttgaattt catggtcgct + 63121 aaattaccct cacttgtcgg ggaacacaac ggtgtttcag gcagctcgcc gcttggccgc + 63181 gagaatggcc gccgtccggc gctggccttg gcggcacgag cacagcaggc agtgcggggc + 63241 gatgttgtcg cgcgtgtagc gcccgccgtc ctcgccgggc acgatccggt cggctatgag + 63301 gtcttcaaac tcgcacagca ctccgcattc ccagcacggc acggtctcgc cgtcaccacc + 63361 ccacgcggcc tcgggcgaga gcagccacaa cttgcgggcg cggcgcgcgt agctcgatcc + 63421 tcgctcggtg ctgttgcacc ggccattggt gctgctcatg cgatctgtcg gcggcagcgg + 63481 gcgcagacct cgacgtggcg gccagcgcgg gtgatgacga cggtggtgat gtgcgggcac + 63541 ttgcccggcg cgatggggcg gctcatgcga atctcaccac cacaacgtca tcggtgttgt + 63601 cgatgtcgag tgagtccacc ggtgactcga tgtcaacgac gggacactgg ccgtagtccc + 63661 cgccgagcca cacatcgatc tcagcgtcgg ggtattgctc ggcgagcgcg gtgagcttgg + 63721 cggcagcgtc ggagagtttc attggtcggt cccctcagtc gatgtggtcg agctcgtgtg + 63781 gatagaagac ccattgcggg tcaaagcggt ctgcgcagat cgccggtgac accggcccgt + 63841 cgacccggcc cacgatctcg accgtgatca acgtggcgct gtcgtgtggg gccggatcga + 63901 tctcgaccac cttgccgtag tgcccggcca tcgggccgaa ctcggctgat accggatcgt + 63961 cgtagccgtt ttcgggccgg acgatcacgc ggtgccctac ggcaaagcag ctcatccgat + 64021 caccctcggt gagttgtcgc ggcacccgat acagattggg acaccaccct tggtgattct + 64081 catgtcggtc tgtcggcgca aggtctggca gctcgggcag taccgagctt ccccgttgcg + 64141 gatctgcttg agcccggccc gagggaccgg cctgagccgg tccttgcggg ctttctcggg + 64201 cgcggtcatc gcaccacggg gccgagcggt cggcccgagc cagtgcagac accgtgcccg + 64261 taggcgcggt gcgcgggcac gatccagaac tcgcgccacc ggcccgacag cgtgcctacg + 64321 gcggtgttgt cggggaacgt gccgacgatc ggcgtgacct cgtggccgca ctgtgcacaa + 64381 tcgaccgggg tgccgcgcag cgccacgccc ggcgcggtgg cgatcaccat ggtccaccgc + 64441 cgatggcctt ggcccatacg ctgcccactc gcacgggcgg ctcgacctcg ggcgcgcggt + 64501 ggcggccctc gtagttcggt gtcggttgct tggcgatgag cagggcacgg gcgtgccgtc + 64561 cggcgctcat caccgcgacg aggtccggca cgaggttggg cgtgctcatc gcagtgccgc + 64621 cggtccgtgc tcgtggcagc gcagccgccc gttgtcctcg cagtcgaggc acgtgggcgg + 64681 ggcctgcttg gcctgctcga tctcgatgag cgtgtcgagc ttgcggttgg tctcgcgcag + 64741 cagcgagacc atcgtgacca tcgcgtcctt gaactcgccg acggtcttgg ccacgtcgag + 64801 gccgccgagg tttggcatca gtcccatggg gtgcttccta tcagggtggg gtgaaatgtg + 64861 ttggggtggc ggggattctg tgaaccttga ccggctgtct gctcaaccgg gggacgcggc + 64921 ccgccatcgc gttgctcgcg ggaccgctca ggtccagaat tgcccgtccc agtaggtgac + 64981 cgacacggtc tcgccggggt tcaggtacgg cagcatggcg acgatgtcgc gctgcggggt + 65041 gggctcgagg cgctcgaccg gcgtcgacgg tgcgagggac atcgttgtgg tcccgtgggt + 65101 ggtcaccttg gtggcggcaa tctcgatgta gtggtcttgg tagtgcacct tgcgggcgct + 65161 gtcgacctga taggtcgtgc gaccggtgag ggtgcggata cggaagaggt cgccggtctc + 65221 aagctgggcg gcggtgacga tctggctcat ttggtgttcc tgctttcgtg gttgttggtg + 65281 gtcagctcag cgagccgctg cggtaggccc gcaggatgga cagtgcggta tcgatgtcct + 65341 tgtaggcggc gagctcggca agctcgcgga tctggctgtg gtcggtgtcg tgcacgttga + 65401 acgcggcggc gcacatggtc gcgtgaggca gggtgtggtc ctcggaggtg aggcagtcgc + 65461 acgcggggga ggtggacatt tcgatccttt ctcggcgggt cgatcccgcc tgacaagaga + 65521 taacttaccc gactagtgcg ggtaagtcaa caccttaacg tcgagtcgtg ccgagcacgc + 65581 agagctgccc ggcgctcacg tcgacgcagc gctcgccgag ggaccggtga ggcggcgacg + 65641 ggaaaccacc cgagcaggat gccgacctcg tcatgacgtt cctcgtagcg cagcacaaag + 65701 tgcttgaggc cccacatcgt ggtgcgcgca ccgacgacct tgacatcgcc gggcatcacc + 65761 gtgtaggccc acgggcggtg ccgtccgctg cccacggtgc cgcgcaatgg ccgcccgctc + 65821 atcgctgcgc ggctctcttg tcggcctcgg cccgtcgacg gtagtagtcg gcgatctgcc + 65881 gggtgaggtc atggagcgct ggggtgatgg cgtcggcggc gcgcatgttg ccggacgcgt + 65941 agaggatttc gcgcgcggtg gccagggtct cgcgcgcctt ggacgctttc gctactccgg + 66001 tcgggctcgt gatggtctga ttgacgttga tgactcggct ctgaactact gccttggtgc + 66061 ccacggttgt tcctgctttc gtggttgttg gtgacgggtg actaggagat ccggttgacg + 66121 tgcgaccagt cgagatgcgt cggctcgggg aggtggtcgt actgcacgac ggcgacctcg + 66181 gcatctgtgg tgttgagcag ctcggcggtg aaaccgtcct tgtccacgat gcggaccggc + 66241 gagttgtcaa tgatcttgaa cgtgccagcg acaccgccct tgttcggcgc ggtgcggtag + 66301 tgccagacac cggagatcgt gtcctcgtcg cgacgttcct gcgtggctgt gaggttgttg + 66361 accgccgcga tctgtccaat gcgcgcggtg gcgtcggctt gggtctcgtg ctgagtgacg + 66421 acggtgcggc cattgaggcg agcgacgaac ttgaacatgt cggttgttcc tttctcggcg + 66481 ggaccgtccc gccctgacaa gagaaaactt acccgcctat gtcgggtatg tcaaccctgc + 66541 ctgtgataca gaaaaccccg cccggtgagg ggcggggttt ctgcgcggtt accagtcgct + 66601 gtaccagact gccgactgta tcagaactcg aactcgctgt aggggccggg gcggacctcg + 66661 ccgccgtact gcgcgccgcc gacaaacggc gaggacggtc gttcaaacgg cgaggacggg + 66721 cgttcggtgc gcagcacctt gccgtagcgg cccatgacct cggcaaccgg cggctcgccg + 66781 ctgctggcca gcaccgtgcg ggtctgcgtg acctcggcga tcgtgtgcca attggcctcg + 66841 ccgatgaaat cgagcaggcc cgaccaattg aaccgccgtg tctcggtgcc ggtgaccgcc + 66901 caccgcactg aggtgcctgc gcgccacccg atggccgcgt agttgtactc gcgcccgctc + 66961 gtgtagcgcc ggaacgtcac gacaacgctc accccggtct ggaccgtcgg cattttgggg + 67021 cggcgcgctt cctcggcagc tcgcgcgatc tcggcggcct gcctgcggaa cgcggcggcg + 67081 gctttctcgg cctcggcggc agcggcgagc aggtcggcgg gagtgctcgg gtcggctacg + 67141 acacccttag gggccttgtc ggccccgttc ggatcttcca tggtggtgtt ctctttctct + 67201 cgttggttgt tgcgtttcac ctcggcgagg cggcgcttga gttcccgctt gcgggcgcga + 67261 agatcggccc tgaccgtgct gatatcggtc caccctcggc atgtcgtggc cagcagcgac + 67321 gagacgatgg ccaattgtgc actgacctcg acctcggtgt ggggcgcgct cacagcgtca + 67381 ctgccccaaa cgcggccccg atgatgccgg tgatcgtcag tgacagcgtg agcgtggcga + 67441 acactgcctc gcgcagtgat cctcgcgggg agaagtgcag cgcgaagcac gcgagcagtg + 67501 ccagcccgag cacgacgtat gccgtgctcg ggtcgtatgc gaagctcatg cgcgggcctc + 67561 gcgtgcggcg cggcgagcgg cccagtaggc accgttgcgc tcgatctcga aatcgaccat + 67621 ggcgctcact accgcgtaga gctcgacctc gggatcgtgc aggacgttct cgtgatccga + 67681 gatgatgccg agtccggtct tgcatccgtc ctcgtgggca gtaatgtggc ccatgcagtc + 67741 gtagcaggtg ggcggctcaa cgatgtcggc gggatcgaac attgtggtgg tccttcctgt + 67801 tgggggcggg tctgtcccgc ctgacatcca ttaagttacc cgacaagggt gggcttgtca + 67861 acaccgtcgt acggatcgat gagcgtgaac tcggaccgta cccggcacag ggtcttgccg + 67921 tagggcatgc gtatcgcgta caccacacga tctgcgggcg tctcgtcgcc gttgagccgc + 67981 tgcgcgagct gcccgacctt gccgctgtcg cgcagcaaca ggtgatcgcc gggcaccacc + 68041 tcggtggtgg gatcatcgat ccttgcgttg agcgcacgct cgcactcggc ctgcgcatct + 68101 gccttggtct cgtggtgcga ggtctcgaca cccggcccct tggccaccca accgagggca + 68161 gccgcctcgg cgcggtggac gtggtaggca ccggcggcgt aggcaccgcc tgtgtgcttc + 68221 tcgcgatgcc atctcatcag atggctccct gagtgatccc gttgacccac agcacggtct + 68281 cgagaacttc ctcgcgggtt gcgccgtgca tgtcgatcac ggcgatgcga tctatggtgc + 68341 cgtgctcact gccgtagtgc gtgccgcatg ccacgtgcgc gctcagtttg ccccacagga + 68401 tctgtccgag ggtgttctgt gcggtgagca tcccgtcgaa catggtctcg tcactgttga + 68461 ggatgtcgac ggtgccctcg cccgcaacgg tgacgtagtg gtcggggcca ccatcggtgc + 68521 tcgtcgtgcg cgtgttccag tcgccgagct tggtgatgat gtggatggcc atggtgttcc + 68581 tgctttcggt cgtggttgtc ggatgatggt gtgcgttggc cggacgcacc cccggtcgag + 68641 tctcaggcga tgtgagcctc gacatccgcg taggtcatgg ccatgtgcgc ggcgtacggc + 68701 atggtccaat ggaccaccag tccggccttg tcgaggttct tctcaaagct gcggaaccgc + 68761 ttgatggcgg tctcgttgcg atctccggtc accccgttgg acgagaagtt gaacaccgcc + 68821 tcgatcatgc cgacgtggcc gccgttgtcc ttgataccga tcattttctt ggcgacgact + 68881 ttgttggact tcgggaactg tgcagcaaat tcctgggcct cggcctcggt ggcgaagaac + 68941 tgaacgacct tgacccgaac gttgtttttc atgcgggcgg cctcggcatg cagacccttt + 69001 tcgatccaga acgcggcctt gttgtagtcg gtggtggtct gggcggcggg ggcggtgatc + 69061 tgcatttctt cctctctcgg cgggacgttc ccgcctgaca cgaactaagt tacccgagtt + 69121 gggcgggtat gtcaacacca atcttcaagt tctccatatc tgtcgggtaa c +// + diff --git a/examples/parsegenbank.py b/examples/parsegenbank.py new file mode 100644 index 0000000..1d0eb15 --- /dev/null +++ b/examples/parsegenbank.py @@ -0,0 +1,211 @@ +"""parse genebank file.""" +from pathlib import Path +import sys +import csv +from collections import defaultdict, Counter + +from Bio import SeqIO +from Bio.SeqFeature import SeqFeature, SimpleLocation +from Bio.Graphics import GenomeDiagram +from reportlab.lib.units import cm + +import matplotlib.pyplot as plt +from matplotlib_venn import venn3, venn3_circles + +def convert_genbank_translation(cds_annotation): + """ converts genebank file CDS region annotation to prediction-enabled names. """ + conversion_table = { + 'minor tail protein' : 'MinorTail', + 'major capsid subunit' : 'MajorCapsid', + 'portal protein' : 'Portal', + 'hypothetical protein' : 'Hypothetical', + } + return conversion_table.get(cds_annotation, cds_annotation) + +def parse_genbank_file(genbank_file_path: Path): + """ Obtain the CDS regions from a genbank file. """ + cds_regions = [] + genome_length = 0 + with open(genbank_file_path, 'r') as f: + for record in SeqIO.parse(f, 'genbank'): + genome_length = len(record._seq) + for feature in record.features: + if feature.type != "CDS": + continue + cds_regions.append({ + 'genbank_start' : int(feature.location.start), + 'genbank_end' : int(feature.location.end), + 'genbank_annotation' : f"{feature.qualifiers["note"][0]} ({convert_genbank_translation(feature.qualifiers['product'][0])})", + 'genbank_prediction' : convert_genbank_translation(feature.qualifiers['product'][0]) + }) + return cds_regions, genome_length + +def parse_prediction_file(prediction_file_path: Path, start_index=2, end_index=3): + """ Parse the prediction file. """ + model_names = [] + predictions = [] + with open(prediction_file_path, 'r') as f: + prediction_csv = csv.reader(f, delimiter=',') + for index, row in enumerate(prediction_csv): + if index == 0: + model_names += row[6:] + continue + prediction_temp = { + 'phanotate_start' : int(row[start_index]), + 'phanotate_end' : int(row[end_index]) + } + for ind, model_name in enumerate(model_names): + pvp_prediction = row[6+ind] if row[6+ind] != '-1' else "Non-PVP" + prediction_temp.update({model_name : pvp_prediction}) + predictions.append(prediction_temp) + + return predictions, model_names + +def phanotate_results(cds_regions, predictions): + """ Get the results for PHANOTATE CDS predictions. """ + already_added = set() + for genbank_cds in cds_regions: + start_end_tuple = (genbank_cds['genbank_start'], genbank_cds['genbank_end'] ) + if start_end_tuple in already_added: continue + already_added.add(start_end_tuple) + print(f"genbank unique count: {len(already_added)}") + + already_added = set() + for genbank_cds in predictions: + start_end_tuple = (genbank_cds['phanotate_start'], genbank_cds['phanotate_end'] ) + if start_end_tuple in already_added: continue + already_added.add(start_end_tuple) + print(f"PHANOTATE unique CDS count: {len(already_added)}") + +def join_genbank_and_predictions(cds_regions, predictions, threshold=10): + """ Joins the prediction file with genbank based on the start and end indices. """ + joined_results = [] + start_end = set() + already_added = set() + for pred in predictions: + for genbank_cds in cds_regions: + if (abs(genbank_cds['genbank_start'] - pred['phanotate_start']) > threshold) or \ + (abs(genbank_cds['genbank_end'] - pred['phanotate_end']) > threshold): + continue + start_end_tuple = (genbank_cds['genbank_start'], genbank_cds['genbank_end'] ) + if start_end_tuple in already_added: continue + already_added.add(start_end_tuple) + + pred.update(genbank_cds) + joined_results.append(pred) + start_end.add(str(genbank_cds['genbank_start']) + ' ' + str(genbank_cds['genbank_end'])) + + return joined_results + +def get_prediction_color(prediction): + """ Get a color representation for a prediction. """ + shades_of_green = { + 'MajorCapsid' : '#7cfc00', + 'MinorCapsid' : '#64ee00', + 'Baseplate' : '#4de100', + 'MajorTail' : '#36d300', + 'MinorTail' :'#1fc600', + 'Portal' : '#19b800', + 'TailFiber' : '#13ab00', + 'Collar' : '#0d9d00', + 'Shaft' : '#089000', + 'HTJ' : '#088300', + 'Hypothetical' : '#00008B' + } + return shades_of_green.get(prediction, '#AA4A44') + +def create_genome_map(gdd, counter, joined_results, genome_length, annotation_name): + """Creates a figure for a given class.""" + gdt_features = gdd.new_track(counter, greytrack=False, height=1, name=annotation_name) + gds_features = gdt_features.new_set() + + # Add three features to show the strand options, + for cds_region in joined_results: + feature = SeqFeature(SimpleLocation(cds_region['genbank_start'], cds_region['genbank_end'], strand=0)) + gds_features.add_feature(feature, + name=cds_region[annotation_name], + label_size=10, + label_angle=45, + label_position="middle", + sigil="OCTO", + label=True, + color=get_prediction_color(cds_region[annotation_name])) + + +if __name__ == '__main__': + genbank_file_path = Path(sys.argv[1]) + prediction_file_path = Path(sys.argv[2]) + + # parse genbank and prediction file. + cds_regions, genome_length = parse_genbank_file(genbank_file_path) + predictions, model_names = parse_prediction_file(prediction_file_path) + print(f"genome length:{genome_length}") + phanotate_results(cds_regions, predictions) + + # join genbank and predictions. + joined_results = join_genbank_and_predictions(cds_regions, predictions) + + # plot results across the genome. + gdd = GenomeDiagram.Diagram("Test Diagram") + counter = 0 + number_of_models = len(model_names) + 1 + for annotation_name in joined_results[0].keys(): + if annotation_name not in ('genbank_start', 'genbank_end', 'phanotate_start', 'phanotate_end', 'genbank_annotation'): + print(f"genome order {number_of_models - counter} - {annotation_name}") + create_genome_map(gdd, counter*2, joined_results, genome_length, annotation_name) + counter += 1 + gdd.draw(format="linear", fragments=1, start=0, end=genome_length, pagesize=(40 * cm, 35 * cm)) + gdd.write(f"all_genomemap.pdf", "PDF") + + # plot vendiagram of overlapping predictions. + modelname2predictionset = defaultdict(Counter) + for cds in joined_results: + for cds_info, value in cds.items(): + if cds_info in model_names: + modelname2predictionset[cds_info][value] += 1 + out = venn3(subsets=modelname2predictionset.values(), + set_labels =modelname2predictionset.keys()) + venn3_circles(subsets=modelname2predictionset.values(), + linestyle='dashed', + linewidth=2, + color="grey") + for text in out.subset_labels: + text.set_fontsize(16) + plt.savefig('venn.png', dpi=300, bbox_inches='tight') + + # get metrics + true_annotations = { + 'MajorCapsid', + 'MinorCapsid', + 'Baseplate', + 'MajorTail', + 'MinorTail', + 'Portal', + 'TailFiber', + 'Collar', + 'Shaft', + 'HTJ' + } + metrics_per_model = {} + for cds in joined_results: + for cds_info, value in cds.items(): + if cds_info in model_names: + if cds_info not in metrics_per_model: + metrics_per_model[cds_info] = [0,0,0,0] + if value == cds['genbank_prediction']: + metrics_per_model[cds_info][0] += 1 # TP + elif cds['genbank_prediction'] not in true_annotations and value == "Non-PVP": + print(cds_info, cds['genbank_prediction'], value) + metrics_per_model[cds_info][1] += 1 # TN + elif cds['genbank_prediction'] in true_annotations and value == "Non-PVP": + metrics_per_model[cds_info][2] += 1 # FN + elif cds['genbank_prediction'] not in true_annotations and value != "Non-PVP": + metrics_per_model[cds_info][3] += 1 # FP + for model, metrics in metrics_per_model.items(): + recall = metrics[0] / (metrics[0] + metrics[2]) + precision = metrics[0] / (metrics[0] + metrics[3]) if (metrics[0] + metrics[3]) > 0 else 0 + f1_score = 2 * precision * recall / (recall + precision) if (recall + precision) > 0 else 0 + print(f"{model}: recall: {round(recall, 2)}, precision: {round(precision, 2)}, f1 score: {round(f1_score, 2)}") + print(f"\tTP: {metrics[0]}, FP: {metrics[3]}, TN: {metrics[1]}, FN: {metrics[2]}") + + \ No newline at end of file diff --git a/misc/parsing_proteins.py b/misc/parsing_proteins.py new file mode 100644 index 0000000..56edd6e --- /dev/null +++ b/misc/parsing_proteins.py @@ -0,0 +1,76 @@ +""" Script for parsing protein names from a fasta file. """ +import sys +from pathlib import Path +from collections import defaultdict, Counter +from typing import Dict, List +import re + + +class FastaNameParser: + """ Class for parsing fasta file and retrieving information from the names. """ + + def __init__(self, filepath: Path): + self.name2proteinlengths: Dict[str, List[int]] = self._parse_fasta_file(filepath) + + def _parse_fasta_file(self, filepath: Path) -> Dict[str, List[int]]: + """ Parse a fasta file and return a list of the protein lengths. """ + name2proteinlengths = defaultdict(list) + with open(filepath, 'r') as file: + fastalines = file.read().split('>') + for line in fastalines: + name, protein = line.split("\n")[0], "".join(line.split("\n")[1:]) + name2proteinlengths[name].append(len(protein)) + + return name2proteinlengths + + def get_species_counts(self) -> Dict[str, int]: + """ Get the species count from a dictionary of protein names. """ + organism2count = Counter() + for proteinname in self.name2proteinlengths.keys(): + match = re.search(r'OS=([^ ]+ [^ ]+)', proteinname) + if match: + organism = match.group(1) + organism2count[organism] += 1 + + return organism2count + + def get_protein_counts(self) -> Dict[str, int]: + """ Get the protein name count from a dictionary of protein names. """ + proteinname2count = Counter() + for proteinname in self.name2proteinlengths.keys(): + match = re.search(r'\|[^ ]+ ([^OS]+) OS=', proteinname) + if match: + organism = match.group(1) + proteinname2count[organism] += 1 + + return proteinname2count + + +def get_go_names(filepath: Path): + """Returns a set of all of the GO names (biological functions). """ + gotermCounter = Counter() + with open(filepath, 'r') as file: + lines = file.readlines() + for line in lines: + go_terms = line.split('\t')[2] + if len(go_terms) < 1: continue + for i in go_terms.split(';'): + gotermCounter[i] += 1 + return gotermCounter + +if __name__ == "__main__": + fastafile_path = Path(sys.argv[1]) + + go_terms = get_go_names(fastafile_path) + print(go_terms) + + # parse the fasta file and extract info. + # fastaparser = FastaNameParser(fastafile_path) + # species_count = fastaparser.get_species_counts() + # protein_count = fastaparser.get_protein_counts() + + # # printing examples. + # print(species_count) + # print(protein_count) + + \ No newline at end of file diff --git a/misc/plot_results.ipynb b/misc/plot_results.ipynb index 45e73e6..f86078d 100644 --- a/misc/plot_results.ipynb +++ b/misc/plot_results.ipynb @@ -463,13 +463,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 104, "id": "5b04fc35", "metadata": {}, "outputs": [], "source": [ "# data maniputation - open dataframes\n", - "data_type = 'Toxin'# set as `Binary` or `Multiclass` or `Lysin`\n", + "data_type = 'Binary'# set as `Binary` or `Multiclass` or `Lysin`\n", "if data_type == 'Binary':\n", " feature_results_df = CSVUtils.csv_to_dataframe(FilePaths.binary_result_path)\n", " index2class = CSVUtils.csv_to_dataframe(FilePaths.binary_classnames_path)\n", @@ -499,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 105, "id": "4735430a", "metadata": {}, "outputs": [], @@ -531,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 106, "id": "7ef42dc5", "metadata": {}, "outputs": [ @@ -544,13 +544,13 @@ "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " ax = sns.violinplot(data=df_exploded, x=\"model\", y=\"f1score\", cut=0, inner=\"point\", palette=shades_of_green)\n", - "/var/folders/4f/nzw4pyy13p90pvc1lkbpbjpc0000gn/T/ipykernel_39325/2556837833.py:5: UserWarning: The palette list has more values (17) than needed (5), which may not be intended.\n", + "/var/folders/4f/nzw4pyy13p90pvc1lkbpbjpc0000gn/T/ipykernel_39325/2556837833.py:5: UserWarning: The palette list has more values (17) than needed (8), which may not be intended.\n", " ax = sns.violinplot(data=df_exploded, x=\"model\", y=\"f1score\", cut=0, inner=\"point\", palette=shades_of_green)\n" ] }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -1657,27 +1657,115 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "95108df7", + "cell_type": "markdown", + "id": "e1b6a71b", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "# Comparing to genomes with known annotations\n", + "\n", + "#### @ 0\n", + "PhageScanner_RNN_multiclass_PVP: recall: 1.0, precision: 0.57, f1 score: 0.73\n", + "\n", + "\tTP: 4, FP: 3, TN: 0, FN: 0\n", + "\n", + "PhANNs_FFNN_multiclass_PVP: recall: 1.0, precision: 0.57, f1 score: 0.73\n", + "\n", + "\tTP: 4, FP: 3, TN: 0, FN: 0\n", + "\n", + "#### @ 25\n", + "PhageScanner_RNN_multiclass_PVP: recall: 1.0, precision: 0.57, f1 score: 0.73\n", + "\n", + "\tTP: 4, FP: 3, TN: 0, FN: 0\n", + "\n", + "PhANNs_FFNN_multiclass_PVP: recall: 1.0, precision: 0.57, f1 score: 0.73\n", + "\n", + "\tTP: 4, FP: 3, TN: 0, FN: 0\n", + "\n", + "#### @ 50\n", + "PhageScanner_RNN_multiclass_PVP: recall: 1.0, precision: 0.57, f1 score: 0.73\n", + "\n", + "\tTP: 4, FP: 3, TN: 0, FN: 0\n", + "\n", + "PhANNs_FFNN_multiclass_PVP: recall: 0.8, precision: 0.67, f1 score: 0.73\n", + "\n", + " TP: 4, FP: 2, TN: 1, FN: 1\n", + "\n", + "#### @ 75\n", + "PhageScanner_RNN_multiclass_PVP: recall: 0.8, precision: 0.57, f1 score: 0.67\n", + "\n", + "\tTP: 4, FP: 3, TN: 0, FN: 1\n", + "\n", + "PhANNs_FFNN_multiclass_PVP: recall: 0.6, precision: 1.0, f1 score: 0.75\n", + "\n", + "\tTP: 3, FP: 0, TN: 3, FN: 2\n", + "\n", + "\n", + "#### @ 97\n", + "PhageScanner_RNN_multiclass_PVP: recall: 0.8, precision: 0.8, f1 score: 0.8\n", + "\n", + "\tTP: 4, FP: 1, TN: 2, FN: 1\n", + "\n", + "PhANNs_FFNN_multiclass_PVP: recall: 0.0, precision: 0, f1 score: 0\n", + "\n", + "\tTP: 0, FP: 0, TN: 3, FN: 6" + ] }, { "cell_type": "code", - "execution_count": 86, - "id": "bcdfb86e", + "execution_count": 103, + "id": "8deada06", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "## END OF FILE" + "rnn_name = 'PhageScanner_RNN'\n", + "ffnn_name = 'PhANNs_FFNN_multiclass_PVP'\n", + "\n", + "probability_thresholds = [0, 25, 50, 75, 97]\n", + "f1scores = {\n", + " rnn_name : [0.73, 0.73, 0.73, 0.67, 0.8],\n", + " ffnn_name : [0.73, 0.73, 0.73, 0.75, 0.0]\n", + "}\n", + "\n", + "# Convert the data into a DataFrame\n", + "data = pd.DataFrame({\n", + " 'Probability Thresholds': probability_thresholds * 2,\n", + " 'F1 Scores': f1scores[rnn_name] + f1scores[ffnn_name],\n", + " 'Model': ['PhageScanner\\n(LSTM-RNN)'] * len(probability_thresholds) + ['PhANNs (FFNN)'] * len(probability_thresholds)\n", + "})\n", + "\n", + "# plotting\n", + "fig, ax1 = plt.subplots(figsize=(4, 3))\n", + "ax1.grid(False)\n", + "\n", + "# Plotting using seaborn\n", + "green_colors = [shades_of_green[0], shades_of_green[3]]\n", + "sns.barplot(x='Probability Thresholds', y='F1 Scores', hue='Model', data=data, palette=green_colors)\n", + "\n", + "# Adding labels and title\n", + "plt.xlabel('Probability Thresholds')\n", + "plt.ylabel('F1 Scores')\n", + "plt.title('Comparison of F1 Scores at Different Probability Thresholds')\n", + "legend = ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + "legend.set_title(\"Model\")\n", + "\n", + "plt.savefig('testing_thresholds.png', dpi=300, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": null, - "id": "8deada06", + "id": "25205333", "metadata": {}, "outputs": [], "source": [] diff --git a/predict_protein.py b/predict_protein.py new file mode 100644 index 0000000..9d70198 --- /dev/null +++ b/predict_protein.py @@ -0,0 +1,42 @@ +import logging +import os +from pathlib import Path +from typing import Dict, Union +import gc +import sys + +import numpy as np +import pandas as pd +import swifter + +from PhageScanner.main import utils +from PhageScanner.main.exceptions import IncorrectValueError +from PhageScanner.main.feature_extractors import ProteinFeatureExtraction, DPCExtractor, ProteinFeatureAggregator +from PhageScanner.main.models import ModelNames +from PhageScanner.main.pipelines.pipeline_interface import Pipeline + + +protein = sys.argv[1] +path2savemodel = Path(sys.argv[2]) + +#extract features +#protein = ProteinFeatureExtraction.clean_protein(protein) +dpc_extractor = DPCExtractor({"gap_size" : 0}) +protein_feature_aggregator = ProteinFeatureAggregator([dpc_extractor]) +feature_vector = protein_feature_aggregator.extract_features(protein) +feature_vector = np.vstack([feature_vector]) + +with open("feature_vector", "w") as f: + print(feature_vector) + f.write(f"{feature_vector}") + +# call model +print(feature_vector.shape) +model_object = ModelNames.get_model("FFNN") +new_model = model_object.load(path2savemodel) +predictions, _ = new_model.predict(feature_vector) + +print(predictions) + + + diff --git a/testing.ipynb b/testing.ipynb new file mode 100644 index 0000000..541126f --- /dev/null +++ b/testing.ipynb @@ -0,0 +1,285 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 20, + "id": "12bf55b8", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from keras.preprocessing.sequence import pad_sequences\n", + "\n", + "from keras.models import Sequential\n", + "from keras.regularizers import l2\n", + "from keras.preprocessing.sequence import pad_sequences\n", + "from keras.layers import Input, Dense, Dropout\n", + "from keras.layers import Embedding, Bidirectional, LSTM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1b12c38", + "metadata": {}, + "outputs": [], + "source": [ + "# model\n", + "model = Sequential()\n", + "model.add(Input(shape=(100,)))\n", + "model.add(Embedding(21, 128, input_length=max_length))\n", + "model.add(\n", + " Bidirectional(LSTM(64,\n", + " kernel_regularizer=l2(0.01),\n", + " recurrent_regularizer=l2(0.01), \n", + " bias_regularizer=l2(0.01))\n", + "))\n", + "model.add(Dropout(0.3))\n", + "model.add(Dense(10, activation=\"softmax\"))\n", + "\n", + "# Compile the model\n", + "model.compile(\n", + " loss=\"sparse_categorical_crossentropy\",\n", + " optimizer=\"adam\",\n", + " metrics=[\"accuracy\"],\n", + ")\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "40cb481c", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "codes = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L',\n", + " 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']\n", + "\n", + "def create_dict(codes):\n", + " char_dict = {}\n", + " for index, val in enumerate(codes):\n", + " char_dict[val] = index+1\n", + "\n", + " return char_dict\n", + "\n", + "def integer_encoding(data):\n", + " \"\"\"\n", + " - Encodes code sequence to integer values.\n", + " - 20 common amino acids are taken into consideration\n", + " and rest 4 are categorized as 0.\n", + " \"\"\"\n", + " char_dict = create_dict(codes)\n", + " encode_list = []\n", + " for row in data:\n", + " row_encode = []\n", + " for code in row:\n", + " row_encode.append(char_dict.get(code, 0))\n", + " encode_list.append(np.array(row_encode))\n", + " \n", + " return encode_list\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ba995450", + "metadata": {}, + "outputs": [], + "source": [ + "int_enc = integer_encoding([\"AGTGCTGATCGTACG\", \"CGTGW\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "42c407ef", + "metadata": {}, + "outputs": [], + "source": [ + "max_length = 100\n", + "train_pad = pad_sequences(int_enc, maxlen=max_length, padding='post', truncating='post')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "dab6ab00", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 6, 17, 6, 2, 17, 6, 1, 17, 2, 6, 17, 1, 2, 6, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0],\n", + " [ 2, 6, 17, 6, 19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0]], dtype=int32)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_pad" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e1821261", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
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+       "│ embedding_2 (Embedding)         │ (None, 100, 128)       │         2,688 │\n",
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 Trainable params: 102,794 (401.54 KB)\n",
+       "
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+GCAGGAGGGCAAGTCGACGCTCGCAGCGGTGGCCACGCCGCTGCGCGCGCTACAGCACAACCCGCACCGC +AAGATCATCCTCGCGACCTACGCGCTTGACCTCGCCGAGACCCACAGCCGAACGATGCGCGAGTGGATCG +AGACCTACGGCACCGATGTGGTCGACCCGCTCACCGGGCTGCCGGTCGAGGACAAGATCGGGCTCAAGCT +CGCACGCGGTGCCAACAAGGTCACCGCGTGGTCGGTGGCCGGTGGCCGGGGCGGCCTAGTGGCCGCCGGT +ATCGGATCGAGGCTGACGGGTATGCCTGCCGATCTCATGATCATTGATGACCCGTTCAAGAACATGATGG +AAGCCGACAGCGCGCTGTACCGCAAGCGCGTAGACGAGTGGTTTTCGTCGGTGGCTCGAACACGTCTCGC +GCCCGACGCGTCGATCATCATGATCCAAACCCGTTGGCATCCTGAGGATCTCGCGGGCAAGGTGATTGCC +GCCGAGAGGTCGCTGCCGAGAAACGAACGAACATGGCGAGTCATCAATATCCCCGCCATCGCCGAAAAGG +GCATCCCCGACGCGCTCAAGCGTGAGCCCGGCACCCCGATGGTGTCGGCCCGTGACACCCCTGAGGCCAA +GCGTAATTTCCCCAAGATCCGGCGCGAGGTCGGCGAGCGTACGTGGTACGCGCTCTACCAGGGATCACCG +CGCAACCCCGAGGGCGGGATATTCCAGCAGAAGTGGTTTGACGCGACACGGCTGCCCGAGGCACCTCTCA +ACCCCTACACCGCCGTGGTCGGCATCGACCCCGCCGACAGCGGCGAGGGAGACGAGGCGGGCATCATCGG +CGGCATGGCCGCCACCATCGACAAGCGGCTCACAGCGGTGCTCACGCACGACCGCAGCGGGCAGTACACC +TCGGACCAATGGGCCAAGGTCGGCGTCACGCTGGCCCTTGAGATCGGCGCACGAGTGCTCGCCGTCGAGG +GGTACACCACCGCCAAGACCTACACGCGTGTCGTGCGTGAGGCGTACAACGCGATTCACCGCGAGGCGCG +CAAGAAACAGCTCGCCGGTGTCCCGCTCACCCCGGTCGAGCACCGCGCGCTGCCGGATCTACCGCCGTTC +ACGATCAAGCCGTGGCGCGGTGCGAACAAGGCCGACGCGGTGGCACGTGCGGGCGGCATGAGCCAGAGTT +TCGAGACCGGAAGGGCACGCACCATCGATTACGCGCTGGCGACGTTCGAGCAACAGGCGGTCGACTGGCA +GGCCGGGCAGCATCAGCCTGACCGAGTGGCTGCGGGCATCATCGTTCACGACACCATTTTCGATCTCATG +GGCGGCTCGATGAGCATCGCAGCACCGCCGACCGGCACCAATCCGAGCGGTGGCCAGGGACGAGAAATAC +CTCCCCCACCTGCATGGATGCGGCGATCCATCGGGAAATAACCGGAAGTGTTGACAACACCTCCCAAGTC +GGGTAACTTATCTCTTGTCAGGCGGGACCGCCCCGCCGACAGGAAGGAAATCCCATGATCGAGACCACCG +CCACCAGCGTCACCGAGTTCGACGGCACTTTCGAGTCCGTCATCACGTGGCGGTTCGATGGTCTGTGGGA +TGTCGCGGTTGCCCACATGGGCACGTGGCTGTGCCACGCGCAGGTCGCGGCGTCCTTTGAGGACGCTCGA +TTTCTCGCGGGATACATCTGCGGGCAGCACATGCGTGCAGAGTCGATGGTGGCGGCGTGAGCCGCCGCCC +GGCGCGGGCCGCGCTGCGGCGCTGGCTGCACGACCGCGTGTGCGTCGGCGCGACCTCGCCTGACCCGTAC +TGCACAGGCAGCGGGGCCGACGAGCATGCACGCACACAGGATGCCAACGCGCGCCGTCTGATGGCCTGCT +CGACGCGCGAGGAAGTCGCGCGTGAGCTGCACCATCAGGCGTGCACTGTGTGGATACAGCAGGCACACGG +CGGCGTCTACGTCGAGCTCGCGCGCAACACGTGCGGCCCCGATGGTGCGCACCACGTCGAGCGGCTGATC +GGAGCGACTTTGATCTCGGAATTGTGCGAGATGATCGGCATAGTTGACAAGACCGACTTAGGCGGGTAAC +TTATCTCTTGTCCGGCGGGACCGTCCCGCCGATTGACCGCCCCGCCGGGGCAGATCGGAGGCCACCATGG +CTGATCAGGATTTCTACTTCGAGGTCGACGGCCAGCGTTTCGACCAGCGCGTCGAGGCCATGGCGCACTG +CGATCTGTACGGGTACCACTACTCGGCGATCCAATGGGTCGACCCGAACGCGCCCTGGGCGTGACCTCGT +CCTACTCGGCCCGCTCGTCGGGCCGAGTAGGGACGGGCGCAGCGATGACCTCGGGCTGAGAAAGGTGCAG +CCGTTCATGGGCCATGCTTGCCGGGGGAAAACGATGCGAAAAGCCCGGTTTGACGCCCGTCCCTCGGTGT +CTATGGGCCGATTTTGTCAAGTACCTCGGTCTGGTACGCAGTCGGCTCACCGAGAGGTTGTCCGAGGTAG +TGACACCCTATGGCAGCCAGACACGCGGCGTCAGCCTCGTTGTTGTTCGCAGGCGCGAGCTCAGGCCATC +GCTTGGCCACCGTGAGCATGACGGTGTCCTTGTCTGCGTTGCCCTTACCGGTGGCGTATTTCTTGACCTG +CGACGTGCCCGCGCACAGCAGCTCGACATCGAACCCCTCACACAGCTCGATGACCTTGCCAAAGATCCAC +GGCAGCACCCACACACCCTCGCCCTTGGCTGCGAAGGCGAGGGTCTCCATGACGACGAGGTCGGGCCGGT +CCTCGGCGAATACCCATTGGATCTGATCGACCAGCGCCTTGACCCGGCGAGCCATGGCCCGTTTCGACTT +GTCGGCGGTGGCCGCCGGTGCGCCGACGGTGACACACTCACCGTGCCACGCGTCGAGCCCGACTGAGGGC +GGCAGCGACACAAGGCACATGCCCGTGGCGCGCAGTGAGGTATCGATCCCGAGAACGACTGGCATTCCAC +GATCTTACCCGACAGCAACGGGCAACAGTAGACTGCCGTGCATGACCATCGACAACCCTGCCACCCTCGC +GCTGGCCCTCGTCATCCTCATGCTCGCCGTCGCTCGTGTCACGCGGCTCATCAACTCGGACAAAATCTCC +GACCCGATCAGGGTGGCCATCGCCAGCAGGGTCCGTCACCACACGCTCATTGCCGCCGAGGCCGAGGCAC +ACGGCCAGTCGCAGCGGGCCGCCGAGGCGCACAGGCGCGAGGCGCGGTGGGCCGGGGTCTACGAGTTCGT +GCAGTGCCCGTGGTGCATTGGTATGTGGGTATCGTTCGGCGGCGCGGCGGTGCTGGTGTGGGTGGTCAAA +TACTCGTGGACGCTTGACTGGTGGGCGCTGCTGCCCGTCGCGCTCGCTGCCAGTCACCTCGTCGGCGTGC +TGGCCCGTTTCGCCGACACCGAGGAAATCGAGATCGAGGACGCGGCTGAGGACGAGCTTGACGACTGACC +GCACGCCGTGCGGATAACCTGACCCGTATGGCTGCTACTTCACTGCGCGTCGTTCGCCGCCCCAAGGGCA +GCGCCCCGGCAGCTCGTCGGCGATCCCTCACGGCTGCCTCGCAGCTCATCACCGACCCACAAAAGCAGAT +GAAGACCTCGCTGATGGGCACCGCCCGCAACGAGTGGCAGTCCGAGGCGTGGGACTTTTCCGAGAGCATC +GGCGAGCTGAGCTACTACGTCTCGTGGCGCGCGAACTCGTGCTCGCGCACCACATTGATCCCGTCGTCTA +TCGACCCCGATACCGGGCTGCCGACCGGCGAGGTCGACATCGAGAAAGATCCCGACGCACAGATAGTCGC +CGACTACGTCAAGGGCATCGCCGACGGGCCGCTCGGTCAGGCGGCATTGATCAAGCGCGCGGTCGAGTGC +ATGACCGTGGTCGGTGAGGTATGGATCGCCGTGCTGATCCGACAAGAGAAAGATCCAGTCACCGGCATCG +CCGGGCCTCGGGCGCGGTGGTATGCCGTCACGCGCGAGGAAATCAAATCCAAGGCGGGGGAGACCGCCGA +GATCTTGCTGCCGGACGGTAAGACCCACGAGTTCAATCGCGACCTCGACTCGCTCGTGCGGATCTGGAAT +CCACGTCCACGCAAGGCCAGTCAGGCCACCTCGCCGGTACGTGCTTGCCTCGAAACGCTGCGCGAGATCG +AGCGGACCACACGCAAGATCAAGAACGCGGCCAAGTCGCGCGTGATGAACAACGGTGTGCTGTTCGTGCC +CGCCGAGATGTCGCTACCGGCTGCGCAGGCCCCGATCCCCGAGGGGCAGGCGCAGATCCCCGGTGCGCCG +GTGCCCGAGGTGTCCGGCGTCCCGGCCAGCGAGCAGCTCGCCACGATGATCTATCAGGCGTCGGTGGCCG +CCATGGAGGACGAGAACAGCCAGGCGGCGTATATCCCGCTCGTCGCGTCGGTGGCCGCCGAGCACCTCGA +AAAGGTCCAGCACATCAAGTTTGGCAACGAGGTCACCGAGGTCGAGATCAAGACCCGTGTCGACGCGATC +ACGCGGTTGGCCATGGGCCTCGATGTCTCTCCCGAGCGGCTGCTCGGCATGAGCAAAGGCAATCACTGGT +CTGCGTGGGCCATCGGCGACGAGGATGTGCAGCTTCACATCAAGCCAGTCATGGATCTGATCTGCCAGGC +GATCTACAGCGACATCCTGACCCCGCTGCTCGCACGCGAGGGAATCGACCCGACCAGGTACATCCTCTGG +TACGACGCGTCTGGTCTGACCAGCGACCCCGATCTGTCCGACGAGGCTGTCGAGGCGCACGACCGTGGTG +CGATCACCTCGGCGGCGCTGCGACGGCTGCTCAACGTGGGCGACGACAGCGGCTACGACCTCACCACCCT +CGACGGCTGCCGCGAGTTCGCTGCCGATGTCGTCACCAAGAACCCCGAGCTCATCGCGATGTACGCACCG +CTGCTGTCGAGCCAGCTCGCGGGTATCGAGTTCCCGCAGCCTGCCGGTGCCATCGAGTCGACGCGCGAGG +ACGAGGAAGACGACGAGGACAGCGGTGCCCGGCAGCAGCGCGAGCCGGAAACCGAGGACGAGCGTAGTAC +CGAGGAAGCGGCGTCGCTCAATGACCGCGCGGCCTACCTCGTCGCCGAGCGGCTGCTCGTCAACCGTGCG +CTCGACCTCGCGGGCAAGCGGCGATTCAAGGTGAACGACGCGGCGCTCAAGACCAAGCTGCGCGACGTTC +CGGCCCACGAATACCACCGCGTGCTGCCGCCGGTCCGGTCGAGCGAGATCCCCCGCCTGATCGCCGGATG +GGACACCGCACTTGAGGACGAGGTCGTGGCCTCGCTCGGTCTCGACAACGAGAAGCTGCGCAGTGCGGTG +CTGGCCACGGTGCGGCGTCAGCTCACACAGCCTCTCATCGAGGGCGAGGTCGTCTGATGTGGCCGCTGCC +CGGTGAGGCTATCTCTCGGACCATCGAGGCCGAGGAAGTGCTGACCGCTTTGTATTCAAAAGCGTTGAAT +TCGTGGGTTTCCGATGTAGTGCCCTTTGTACTGCCCACACTCACTGCTGCGTCCGAACCACCACCGGATA +CGGGTGCCGTGGTCGAGCAGGCGGGCGGTTTGGATCTGCTACTCGATGAGCTGATCCTACCCGGTCTGTC +AATACTGTTTGCGCTGTCCCTGATAGAAGCAATGACCGGTATTTCTATACCGATACCGGAGTTGCCTGAC +GTTGATGTCAATATTGGTGGGAGGGGTCGACCTCAGATTGATCGTGAGACCGAGGTCGAGGCACCTCGTG +TGAGCGGTGACCGTGTGCGTGACGAGCGTGTCGAGATCGCGCACCCCGAGGTGCTGCGTCCTGAGGTGGT +CGAGACGATCACCTCAGTGACTGACTACGAGCGCGACGAGATCGTTGAGGCGGTCGAGCGTGTCTATGCC +GATCCATATCTGCGCGAGCGGGCCGAGGACTACGTCGACACACAGCGCGAGCGGGTACACCGCACGGTGC +CCATGGTGCAGAGCAAGGTAGAGGCAGCGGCGCGCGAGGCTGCGGCGGCCCCGGCACCCGAGCGAGAGAC +CGAGCTGCCGACGACGACCGGCGACCTCACCCCTGATATCGAGGTCGTGATCACGCGGCAGCGCGAGGCG +GTGGCCGAGGTGCTCACCCCCGGTAGCAACATGCTGCTTGAGGTCGCACAGCTACAGGGCTATCAGGCCG +CCGGTGTGCAGAATGCGGCGGTGATTGCCGCCGCACAGATCTCGCCGGACGCACACTTGCTCGACAAAGT +GTGGATCGCGACCATGGACGGCAAGACTCGCGATTCACATTTCGCCGCCGACGGGCAGCGCGTTGAGCTG +ACCGGTGAGTTCACCGTCGGCGGTGCCGGTCTGCTGTTCCCCGCCGACCCCACCGGCCCACCGCACGAGA +TCCTCAACTGCCGGTGCCGTGTCGGCATTCTCGCCCGAGACGAGGAAATTCCCGACGAGGTCGACCGGCA +CACCGAGCGCCTCAACGGTCGCGACAGCGTGCAGGTCAATCGGCAAGGATCGCAGCAGGATGAGATCGAC +CGGCGCGCCCGTAAGGGTGTTGTCCGTGCCCGCGAGGACACCGACGGCATCGGGCGTGTGGCAGCAGGTG +GTTGGACCGCCCCGAGCGAACAGGATTACAGCATGGCCAAGACAGAGCTTTTCCGGACATTCACCGATCA +GCCGCTCGCGTTCGTCGGCATCGAGACCAGCGACGGTCGCATGCTCGCACCCGAGATCGAGTTCTCGGTG +CGCACACCGCCGCTTCCCGCGATGTGGTGCAAGCAGACCGGCGAGGGCCATACCGAGGCGTACACCGTCG +GTGTCCTTGAGTCGGCACGGATCGAGAACGGCACCGTGCTCGGCTCGGGTTACTGGCTCAACAAGCCTGA +GGCCGACGACGCCTATGCCGATGCCCGGCATAAGGTGAGCCGCCCGTCGGTCGACCTCGCCGCCACCGAG +TGGAAGCTCACCGTCGACGGGCGCGAGCTCACCGAAGAGGAAATGTTCGATCTGCCGCCGGACGCGCACG +TGGTGCAGACGATCACCAAAGCCGAGCTCATCGGTTTCACGATGGTGGCCAAGCCCGCGTTCGGTGACAC +CGTCATCGAGTTCAACGCGACCCGCGAGTCTCGCGACCTCGCGATGGTGGCCAGCATCGCCGACGATTTC +CGGCCTCGGGTGTACGCGGCTGATCTGTTCGCGCAGCCCAACCTCGCCGAGCCGACTGCACTGCAAATGG +ACCCCGCCACGGGCCGGATATTTGGACACATCGCGTGCTTTGGTGCGTGCCACCGATCGGTGCAGAACGC +GTGCGTCATGGCCCCGAAATCGCCGTCGAACTACGCACAGTTCCACACTTCGCCGCCGGTGCTGCTCGAT +GACGGCACCCGCCTCGCCGTGGGCCGCCTGACCGTCGGCACCGGGCACGCAGACGAGCGGCTGCGTCCCG +GCCCGGCCATGGCGCACTACGACAACACCGGCGCGTGCTGGGCGCTGGTGCGTGCCTACGAGACCAGCGT +CGGAATCGAGGTGTCGGGCGTGGCCGCCCCGTGGGCCACCCCCGAGCAGATCGAGATGGGCCTCGCCTCG +CCGCTGTCCGGCGATTGGCGTGATTTCGGGCAGGGCCTCGATCTGATCGCCGCCCTGAGCGTCAACACGC +CCGGTTTCGCCGTCCGTGGCCGCACCGACGACGATGGCCGCCCGGCGGCGTTGGTGGCCAGCCTCGCACC +CGCACGCAAGCGCGTCATCGAGCTCAGCGCAGACGAGATCGGCAAGATCGTGGCGCGGTCGGTCGAGCAG +GCATTCGCTGCCCGTGAGGCCGCCGAGGCGGCGGCCAAGGCCGCGCAGGACGAGGCCGACAACATCGAGG +CGCTGGTCGAGCTCGCCATCGAGAAGGTAGGCGAGCCGCCTGAGCCTGAGCCCGAGAAGACCCCGAACGA +CATCGTTGCCGAACTCATCGAAAAGGCGGGACTGTAATGGCATGTGGATGTGGTGGCGGCGCTAAGGCCA +ACAGCAATTCGCCGGGCGGTGACACCCTCGGCTATCGGGCCATCCTGCCCAACAAGTCGGTCGTGCCGCC +CCGTAATGAGCCGCCGTTCTTCATGGCCGCCGACGCCATGCGCGAGGTCACGCTCGCGCGCGGAGGCACG +GTGCGGCGTATTCGCAAGAGCGATCCCACCGACGACTATGCGGTAGCGAAACGCGAGGCTGAGCTCGCCG +CTGCCGCCACGACAGCGTAGGTCTGCACACCGCTGGTCTACGTTTCCGGTTAGAGCGTTTCCGACTGCGT +TGTGTACCGGGGAGCGATCACCGAGTTTGACCGCTGTTCGATCAAGACAAGGAGACAAGCGCCATGTATA +AGCGCCCTGAGCAGCTCCCCGGCACCGCTGCCGAGCTCGACGCTCTGCTCGATGCCGCGCGTGCTGACAT +CAACGTCATCACTGCCCGCCACAAGGCCGGTGAGAGCCTGACCCCTGAGGACGCGCAGCGCCTCAAGGAT +CTGCTCTCTGAGGTCGATGAGCTCAACGGTGAAAAGGCCAAGATCGCCGTCGCCGACGAGCTGCCCGACC +TGCTGGCCAAGGCCGACGCTGCCACCGCGCCCGCCGACGACAAGACCGCCGATGAGTCCGAGGGTGATGA +CGACGGTGATGCCGAGGGCGACGACGACGGCGAGCCCGAGGGTGAGACCGAGGGCGGCGAGGGTGCCGAG +CAGCGCGAGCCCGCCGAGGCCATCACCGCCTCGACCGGCGCACAGCGCCCGCAGCGCCGGTCGCCGAACT +TCGCCGGTGCCGGTGCCAACGACACCCCCGGCGAGGGTGACGGCGGCGGTGACGCGCCAACCCCGCGCTG +GAAGCTGCACCCCGGCGCACCCGGCTATCGCGAGGGCATGGGCACTGTCGGTTTCGCCGACATCAGCCAG +GCGCTCGAAAAGATCCGGCCCGGCAGCCGCGCCGCGATCCGGCCCAACCGCCCGAGCCGTAACCTCGACG +GGCAGGAATTTGCCCGTCAGGTCGTGTCGACTCTCGACCGCGAGGTCGATGTCGTCGGCGACAGCCACGC +GCTGGTGGCCGCGATCACCAAGGCCACCGATCAGCGCAATCTGCCCGGTGGATCTCTGATCGCCGCCGGT +GGCTGGTGCGCACCGTCGGAGCAGCTCTACGACTTCTGCGACGTGCCCGAGGCCACCGACCTGATCTCGC +TGCCCGAGATCACCATCAACCGTGGCGGTATCCGCTGGCCTCGCGAACCTGATCTGTCGGGCATCTTCGA +GGAATTCGAGTGGTTCTTCACCGAGCCCGAGCTCGAGGCCACTGACCCTGACACCGGCAAGCCCACGGCG +GTCAAGACGTGCGTCGAGATCCCGTGCGCCGACGAGTTCGATGAGATCCGGCTCAACGCGGTCGGATGGT +GCGTCGAGGCGGGCATCCTGCAAGAGCAGGGGTGGCCCGAGCTGGTCGAGTGGTTCATGCGTTCGCTGAC +GCAAGAACACTTCCGCGCCCTGTCTCGCCGGACGATCCTCAACATGGTCGCTGGCTCGACCGGGGTCACG +ATCCCGGCAACCTCGACCATGGGTGCCATGGCCTCGGTGCTCAACTCGCTGGCACTGGTCGCGACCAACA +TCCGTCTCAAGCGGGGTCTGTCGCGCACCGCCACCATCGAGGGTGTCGCACCGTCGTGGTTCCACGAGGT +CATCCGTGCCGATCTGGCCATGCGTGCGGGCGGCGTCGAGGTCTTCAACGTCTCCGACGCGCAGATTCAG +CAGGCGCTCGCGGCTCGCAACATCGCGTTGCAGTACGTGGGTGACTGGCAGACCCGCGAGTCGGGCAAGC +CCGGCAACCTGGCCACGGTCGATTGGCCGGACACGGTCGACGTGCTGCTGTACCCGGCGGGCACATGGTT +CCGCTCGATGAGCAACGTCATCGAGCTGGGCGTCATGTACCCCAAGGAACAGTTGCAGTACAACCGCTTT +ACGCGGATGTTCACTGAGGACGCCATCGCCGTCGGCAAGCGCTGCGGTGAGTCGGTCAAGGTGACGTTGA +CGCTCGACGTGTCCGGTGCCACCGGCCTGCCTCAGCGTCGCACCAACCTCAGCGCATAAGCGAGCCGGTA +GCAGCAGACTGAGAGCGGGCACCGTGGGCAAGAACCTTCCCGAGACTGCCCACGGTGCCCGTTCTCTCAT +CTTGAGAGGACAACGCACATGACCACACCGACACCGCCGGTGCTCGATGTCGTGCACTACGAGGTGCCAC +CGGTCAATCCCTCGCCCAACGGACTGTTCCCGGCAACGACGTGGATCTCCGACCCCGACAACCGGTTTTT +CAACGGCGTCGAGGTTCGCGGGCCGAACTACGGCGGCGAGGACGCGTTCGGCACGTGGGAGGGTCACTAC +TGCTCGGTCCCGCCAACGGGCGAGGACGATCAGCGCAAGGACGGCACTCGACCCGACATCCTCGACGCGT +TCGACCCGATCACGGTGTGGGCCTACGACGAGTGCGACCCCACCGCGCCGAGCCGCGCTGAGGTGCTCGC +ACGGGCAGCTCAGATCCTCAGGCTTGAGGAACAGGTTGCCATCGAGCGCGAGTTCGCCAACCGCATGCTC +ACCGACGCGGGCACGCCTGCTGCGGCGGCCACGCTGCGATTGGCGGTCGGCTACATCGAGGGTGTGCTCG +CACTGACCAACACGCTCGGTTTCATCCACATAGGCGCGCAATGGGTGGCCACCGACCCCGATCTGTTCAT +CGGCAACGGCGGTGTCAAGCGGTCCCCGATGGGCCACACGTGGGTCATCGGTGGCGGATACGTCGACGCG +CTCGGGGACACCATCGTGGCCACCTCGCAGCCGTATGGGTGGCGCAACGAGCCCGTGACGCGCGGTGCCA +TCGATGCTTTCGAGAACACCTATGCAGCAATCGCCGAGCGTACCGTTCTCATTGGATACGAGGCGCTCAT +CGCAGCCGCGACGATCACCGACACCGACGAGCCCGAGACACCGTAGGAGACATACCAATGCCTGAGGGAA +TCATCGCCACCGTCGAGGACGGTTTCGCGACTATCGATTTCGTCAACCCTGATCTGCGTGGCCCGGCGCT +CGCTGCTCTGCTTGAGCTCGGCGGGCCGGGCACCATCGAGACGATCAGCCGACGCGGCCCGCGACGGCTG +TACCGCGTGCCCGAGGGCAATGCCGAGGAAGTCGGGTTGCTCGACGGCGACGAGGGTCCGGCCAAGTGGT +CTGCTGGTGCCGATACGGGCCGGGCGGCGGCGCTCAAGGCTGCCGATCCCAACGTGGTCGGCGGCGACGA +CTGGCATACACCGATCCTTGAGCACAGCTCGCGCAACGCGTATGTCGGTACCGTGGCCAACGAGGACGTG +CTCGACCGGACGCCGGTTTACACCGGCAGCGCACACAGTTTCGGTGGCCACGCGGTGGCCCCGACGAGCA +CCGATCTGATCGCCGGTCTCGACGCCATCAAGTACCCGCAGCAGCCCGAGGAAGTCCCGCCCGGCGAGGG +TGAGGGTGACGGCACGGGTGAGGGCGAGGGTGACGGCATCGAGGGGATGTCCGCACAGCGGGTCAACCTC +GGTCTCGCCGAGCAGACCAGCGGTCTGGCCAGCGACCCCGGCGCGACCCCCGAGGTCGGCGGCGAGGCGC +TCGGCGAGTTCACCACGGTTCAGTCCACACGCGTCGAGGACGCGGCGGCCTACCCCGAGGGTGAGCCGTC +GGAGGACTGGAAGCGGGCCGAGCTCGACGCGTACGCGGCTGCGCACGGTCTCGACACCACCAAGCTTGCC +AACAAGGGCGAGGTGCTCGCGGCCATCAACGAGGCTGCCGACGGGGAGGATGATGCGTAATGACCGGCAA +TGACCTGAGCCCACGGGCTCGTCTACAGGCCCTGATCCCGGCCAAGGCGCGCGAGACGTGGTATCGCGTG +GCCAGCGCGGCGGTGATGTTCCTGCTCGCTTTCGGCATCCTCGACGCCAACGAGGCGGCGCTGTGGGCAC +AGTTCGCTGTGGGCCTCGTGACGCTGGTGTTCGCGCTCATCTACGCGTACACCCCTGCACGGGTGGCCCT +GTACGCGTTCGTCGGCGTCGTCGGCTCGGTGCTGCTGTACTACGGCATCACCACCGAGGAAACGTGGGCA +CTGATCACGGCTGCCGTCGCTCAGGCGTTCGGCATCGCCACCGCTGCCGCCAAGACCACCCCTGTGGTCG +TCGCACGCGGCCAAACGTCCCTCAATTGAGGGACAGGTGACTAGCCCGTGGGGTGGACCCCGACATAGGT +CGTGGGCATCGAACACGATGCTCACGTTCTTGTTGCTGGTCGTGTGCATCATCGCAACGGTCGTGACCGA +CTATTTCGGTGAGCCACCCAACTACCTTGTCGGCCTGCTCGGCACGGCAGCCGGTGCATTTTTCGCCGCC +ATCGGCAGCGATCAGCAGAAGAAAGCCGCTGATCTCGCGCAGACTGCCACCGAGGCAAGCACCTCGGCAG +CTCGGGCCGAGACGACCGCCAAGCGAGCCGAGCACAAGATCGATGAGGTGCTGCACGAGCGCCTTGACGA +CGATGACGCGTCGACCAAAGACGGGGCGGGTACATGAGCTCTGTGCTTGAGGTCGTCTATTCGCTGCCGT +TCGCGGTGGGCCTGCTGTGCGGAATCCTCGGGCAGCGGGCCTACTGCTACGGTCGGGCTTGGTACAAAGA +CAGGAACGATCCCCTACCGAACGGTCGGCACCGCACTGTGGCAGGCATAAGCAAGGTTTGGGTCGGCGGC +CTGATCGCTGTCGGCAGCCTCGGCTATGTCCTGTACCAGGCTGAGGCCACCCGCCTCGACACGGTCTCGC +TCGCCGAGCATACGCAGGAATGCACGAGCGACCTGATTGCCTCGGTGTCGCGTGGACGGCAGATCAGCAC +CGAGAATGACCGGCTCAGCATTGCCCACCGCGACAAGCTCACCGAGCTCGCGCAAGTTCAATCTGTGTGG +CTGGGCCGGATACTCGACCCGCCACCCCACATCGCCGCCATGCCCGCCGATGACCCTCGGCGGGATGGCT +ATTTCAAGACCATTACGCAGTTCTACAAGGTACGGACCGACGAGCTGCGCGCGGATATCGACAAAATCCG +CGAGGAACAGGCCAAGCTCATCGGTGATCGCGAGCGCAATCCGTTGCCCGACCCTCGATGCTGGCCCGCT +GGCCCCGAGGTGAAATAGCCGTGCACGGTGCTGGCCTAGTGTCATTTTCAGTCTCGGGAAACGGACTAGG +CGCGTTCACGCGCCCGACTTAGGAGGACCGACCCTATGCCCGGTATCCAGCCCGTCAAGGGCCGCCGCCT +GAGGGCCACCAAGATCAACAGTTGCGGTATGCCCATCGCCGGGCCTCGAAACCGTCTGGTCACGAGCGGC +TATGTCAGCCTCACGTTGACCCCGGTGATGCGCGACGCCACCGACCTCACGCAGGACAACGCTGAGGGCA +AGGAATGCTTCGCAGACCGCACCGCGCCCGAGCGTCGCTGGTGGACCCCGGCGTTGGAGCTGTGCAACGT +CAATACCGGCCTGCTGACCATGTTCACCGGGTGGGAAACGCTGGTCGACGCCAACGATCTGCCCGTCGGT +TTCCGCGACCAAAAGGAAATCGAGTCCGATTTCGGCGTAGCGCTTGAGCTGTGGTCGGCGGGCAAGATGG +AAGAGGATTGCGACGAGATCCCGACTTCCGACGATGTCCTCACCGACACGAGCTCGGGCCGGTCGTACGG +CTATTTCCTGTTCGGCGGCACCGAGTGGACACCCGGCGACATCACCATCAGCGGCGCGGTGACCACGTTC +ACGTTGACCGGTCGCACCATCGCGATGCCCGCGTGGGGCCTCGGCCCGTACAACGTGCAGGAAGCGGCCA +ACGGCACCGCTCGACGCCTGCTGACCCCGACGAGCAAAAAGGAACACCTCACGGTGTTCCGTACTCGGAT +CGCACCGCCCGAGCCGACACCGGGCACCGAGCCCGTGCCGCTGGCTACGAGCACGTTGTTCACCGATCCG +AATTTCTACTACGGCGGGCCGGACGACGAGCCAGCCGCCGTGGTGGCACCTGCTCAGTCTGCGTAAGTCG +CGCTACGCAGAACCGCCCCGGTCCCCTTTGGGCCGGGGCGGTTTGCTGTCTCGGTTTAGCCGTCGAGGCG +GGCGCGAACGTAATCGTAGGTGAGGCCACCGGCAGGGTAGAGGGAACGAATCTGGTAGGGGCTGAGATCG +ATCTTGGCGGCACCGGCGACAGCGGCGGCGACGGTGTCGGCGGCGCGAACCTCAAGCAGGGCGCGGGCGA +CGCGAATCTGATCGCCGGGGACACCGCGACGGGCGGCGGCGGCGAGATCGGCCTCGGCGTAGCGGCTGAT +GGTCATTTCTTCCTCTCTGTGAGCGGGTCCGTCCCGCTGACAAGAACTAAGTTACCCGCCTAAGTCGGTC +TTGTCAACCCCCGTCACGCAGCAATTTCCCGCGAGGTCACGATGCCTCGACGCCACCGCAGCGCCCAGTC +GAGGCAATTGCCGCAATGCACATGCTTGCCGATGCAGCCGGGCAGCGGCGTGTGTTTACGGGCGTTGAGC +GACCAGGCCATCGAGTCGGCGGTCGTGAGCAGGTGCCCGTATTCGCGCAGCCCGAGGGACTTGACCCCGA +AACCGTGCACAGGCAGCTCGGGGTCCGTGTCGAGGATGCCCTCGAAAACCCGCCGGATCTCGTCGGTGTG +CTGTCGACGGCACACGCTGCCGACACCGACGAGCTCGATGTTGTGCAGATCGATCCCGGCGTCGGCGTAC +ATCGACATGCATTTCGCGTAGTCAGAGACCTCGTAACCTTGCAGGACCGGCATGAACGGGCAGTGCTCGT +CGCTCACCTCGCCCCACAGCCGACGCAGCTCGACGTAGTTGGCCACCGTGCGGCGCTGGTGCTCGGCGAC +GCTCAGGCCGGTCCGGTGGACCATGTCGGGCTCGCACATCCAGTCTTGGGGCGCGGCCCATTCAAGTTTG +CCGATCTGCTGGTCGTATCGGTTGACCGCCTCGACGTACTCGCGGGCGGTGGTCTGCCAGCCGCCGAACA +TCGAGAGCTCGGAGAAACCGCCCGAGTCGAGCGCCCATACCTCGATGGCCCGAGGCAGGCTGCGCAGCCG +CTTGAGGCGGCGATGAGAGACGAACAGCGGCACCCCGGCGTGGCCGAGCCACGAGGTGATGTGGGTACCG +AGGTAGAGATAGGCGCGCATCGGGACGGTCCTTGTCTGCCCGGCCCCCCGCACCCCGGCGTGGCCGAGCC +ACGAGGTGATGTGGGTACCGAGGTAGAGATAGGCGCGCATCGGGACGGTCCTTGTCTGCCCGGCCCCCCT +GAGGGGCCGGGCGGTAAGCGGGGTGATTAGCGGGCGCGGAACGCGGTGCCCATGAGCCGTGCGTTGTCGG +CGCGGTCCTGCTGCATGAGCGCTTCGGCGCTGCCTGCCTCGGCGAGGTGCCGGACGGCGGTGCGGAGATT +GCCGCTCAGGTTGCCGCCTGAGACGTTCATGAGGAAGTACGCATGTCGGAGGACGGCGTCGCGATCCTCG +ACGGCGACACCGCGCAGTGCGGCGGCCTGACGCTCGTGCATGCGGTCGAGATCGGCGTCGGTGATGAGTC +GGGTGGTCATGGTTTCCTCTCTCGGCGGGGCCATCCCGCCTGACAAGAGATAAGTTACCCTCCTAAGTCG +GTCTTGTCAACTCCCTCGGTTCATCACCACCTCGCGGTAGCCTGAGCTCGTGACTTTCGAGTGGCCGATC +AACCGGGCGGGTCTGCCCGTCCTGCCTGAGCTGACCGACCCGCCGAGCCCCGAGTACCTCAAGGCGCTCA +CTGAGCGCAACGCTGCCGAGAGCGTGGCGGTGGCCATCATGTGGGCCTTGTCCGGTCGACAGTTCGGCAC +CTATGAGACCATCGCGCGCCCGTGCCCGACGCGCCCGCGCGGCAACCCGTTCGCCTACCGCTCCGACGAC +CTCGTGTGGACCGGCGAGGGGTGGCTCACGTGCGGCTGTGTCGGCACCGGCTGCCGGATCGTCGGCCCCA +ACGTCGTGCACTTGCCCGGCCCCGTGCACGAGGTCACGCGCGTCGAGATCGGCGGCGTCGACCTATCCCC +GGCGGTGTGGGTCGCCGAGGGCAACAAGCTCTATCGGCGCGAGGCCCCGTGGCCCGCCCAAGACCTCAAT +CGGCCCCTCGGCGATGTGAACACGTGGGGAGTCCACTACACGCGCGGTGTGCCGGTGCCGCCGGGCGTAT +CCGAGCTCACCGGCATCCTCGTCAAAGAGATTCTCGGCGCACTGGAAGATGTCGGGCGCTGCCGCTTGCC +GCGCACCGTGACCACCGCGAGCCGCAACGGTGTGACGTACCGGGCCTACGATCCGGCGGTGATCTACCGC +AACGGTAAGACCGGACTGCCCGAGATCGACCTATGGCTCGCCACGGTCAACCCGAACGCGCTCATGGCCG +CCCCGACGGTGATCTGATGGCCTGCCGGACAGATCCCGCCCTCGTCATCGTGGGCCATGCCACCGAGGCG +CTGCTCGATTGGTTCAAGGACGGCGCGTGCCCGCCGCTCGTCGGCAGCACGACCAACGTCCGGTTTTTCG +CCGGTGACTCGACACCGCTCGCCGCGTGGGACAGCCACGCGAGCCAAGGCTGCAATGAGCCGTTCGTGTG +GGTGCGGCTCATGCGGCGGTACCGAACGCAGCGGTTTCCCGATCCGACCATCGGCACCGATTGCAATCTA +CCTCGCGTGGCCCCTGTCGAGATCGGCGTCGGGTGGTGCGCACTGACCGAACAAGAACCTCGATGGTCCG +ACTACCAGCGCGAGGCCGAGGTGTCGTCCGACACCGCGTGGCGGCTTGAGGAAGCGCTGTGTCAGGCCAG +CACCCGGCTCAAGCGCGACGACGAGCAGCGCCTCGTTGGCACCGACGCGCTGGTGCCCTATGGCCCCGAG +GGCGGCGTAATCGCATGGACCGCTGTGCTGTATTCGACCTACTGAGATAGGAGCATGACAAATGACGACC +GTGGTGATTGAGGGCAGCATTCTGCCGTGCGGTTTCCTCGCCCGAGGCGAGCGCGTCGAGGTGCAGCGCA +CCGCCCACATCGACCGGCTCATCGAGCAGGGTTTCGTCAACGTGATCAGCGAGATCGGCGACCCCGAGCC +CGCACCGCTGCCCGAGCCGGTCAAGGCACCGGCGCGCTCGGCGAGCCGCGACACGTGGGCCGAGTTCCTC +GCCGAGCACACCGACATCGTGACCGAGGGCAAGGGCCGCGACGAGCTCATTGCCGAGTACGACGAGTGGC +AGAGCGTGCACGACGTTCCCGCCGCCGACGACAGCGAGTAGCCCGTGGCCACGATCCGCGCCCGCGCTCG +GATCGAGATTGACGAGGCGGCGCTTGAGCGGGAGTCGGGCGAGCATCTGCGGGCATTCCACCGCTCGCTC +ACGCGCCGGATCGCCAACCAATCGCGCGTGGCGGTGCCAGTGCGTACGGGCAACCTCGGGCGCACCATCG +GCGAGCTGCCGCAGGTATACACCCCGTTTCGCGTGCGGGGCGGTGTCGAGGCCACAGCGGATTACGCAGC +GCCCGTGCACGAGGGCAGCCGTCCGCACGCGATCCGTGCGCGCAATGCGCAGTATCTGCACTTCTGGTGG +CATGGCCGCGAGATGTTCCGCAAATCGGTCTGGCACCCCGGTACGCGGGCACGACCGTTCATGCGCAACT +CGGCGCAACGCGTCGTGACCAACGATCCTCGGGTGCGGATGACTTAGCCGACCTCGCTGGTAAAGTCGGG +CTCACCTATCTCGGGAGGAAAACAACCATGGCCACATTCAACGCAGACGGCAAGACCGTCACTGACGAGC +AGCTCTCGCCGCCCGCCGAATATCTGCCCGATCTCACCGACAGCGAGCGCGAGGCCGACTACCAGGCCGA +GCAGGCGCGTATCGAGGCCGCCATCGCCGACGCGGCTGCCGCCGATGACGATGATGCGCTGCCCGAGGTC +GAGGTGCTGACCCCGCCCGCCGGTCTGGCCACGACCGACGAGCAGCCCGGCACCGAGGTGGTCACGTTCG +AGCGGTTCAACGTCGAGGAAGTCGAGAACTGGTCCTACGACAAACTCGAGTTCAAAGGCGACATGCTGGG +CATCCGGCTGCCGACCAAGGCGGCGCTCGCCGGTTTCTCGCTGGCATCGAGCAAGTACGTCTCGCTCGGC +GTCAAGAACGATCTCACCGGCCTCTTCATCGCGCGGCACCTCTCGCCTGAGTCGTACGGACGCGTATTCT +CGCGCCTGATGGACCCCGACGATGTCGACTACGACGTAGACACCGTTGGAGAGCTGTTCAACGCAATCGT +GACGGCAGCGGTCGAGTCCGACGACGAGTAGACCCCGCACAACGGAAACCGCCCCGGTCCTTTCGGATCG +GGGCGGTTTCTGTGCGGCGGGTAGGTCAGCCGATCTGCTTGGCCAGCTTGACCCCGGTGATCAGCGTCTT +GAGATCCCGGCGGGCGGCCATGACACCCTCGAACGATCCCTCTCGCCAACCGCCGATCACGTCATCGTCC +TTGCCGACTCGACCGGGGTGGGGGAGGAAGACGGACCAGCGGCAATCGCTGTAATCCCAGCCGTCACGAC +CCATGCGGGCGTTGCCGATGTAACCGATGGTGGCCTTGTGCTCGGCGTCGGTCAGTCCCAGCAGCTCGTT +GAGCTCTTCGACGCTGTCCTTCAGAAAACCCATGTGCATTTCGTAGTTGCGGGCGATCTCTTCCGGGGTG +CGGGTGATGGTGGTCATTTCTACCTTCCTCGGCGGGACGTTCCCGCCTGACATGAACTAAGTTACCCGCC +TAAGTCGGTGTTGTCAAGCCCATTGCGTAAACGAGTTTCCGCGCGCCCGTGCGATAGCCTGACCAGGTGA +CAGATGTCGGCAAAATCAGCCTCGGGGTTGAGATCAATGGCGACGATCTGGCAGCCCGTCTCGGCGAGGC +AGTGCGCACGGCGGTAGCGCCCGCGCTCGACAAGCTCAATCGTAAGCTGCAAGAGACGCAGCGCGCGTAC +AACAAGACCGGCGACAGCGCCGAGAAGTCGGCAGTGCGGCAAGTCGCGGCCCTCAAGCGCGTCGAGGAAC +AGGCCGACGCGACCCGGCGCGCGGTGGAACGCGCTGCCGCTGCCTCGCGCCTCGGTGGTGGCGGTCGCGG +TGACTCGACGGCGGGCGTCGGCGGCAACGACAACAGACGTGTCGACAACCGACGTATCGACAATCGCCGG +TACGACTATCGCACGTACAACGACAACCGCAAGATCGATCAACGACGGTACGACAACCGCAAGGTCGACA +ACCGTAAGTACGACTACAGCACCAAGAACGACCAGAGCATCACCTATGACTATCGGTCGTACCACTACGA +CGGCGTGCCCGGTCCGGCACCCGGCGGCGGCGGGGGCGGTTTCGGGCGAGGTGGTGGCCGGGGCGGCGGC +GGTGGTCGAGGCGGTGTGCTCGGTTTCCTGACCGGACCCGTCGGGCTCAACACCATCGCTCAAGGCGCGG +CGGCGCTGCCCGCCGTGGCCACCGGAGTGGTCAACATCGTGGGCGCGGTGCAGCAGCTCACGCAGGCCGG +TCTCGCGCTGCCCGGCATATTCGCGGGCGGCGCAGCGGCCATCGGCACCGCCGTCATCGGCTTCCAGGGC +CTCGGCGACGCCATCAAGGCGCTCAACGAGGCCGACGCTGACCCGGCCAAGCTGGAAGAGGCCAACAAGG +CGCTCGAAAAGCTCGCACCGTCAGCGGCAGCGGTGGCCCGCGAGGTCTCGCGACTCACCGTCGGACCGCT +GCGCGATTTCCAAAAGGCCATCGCGCAGCCGCTATTCGAGGGCCTCGACAAGGACATCGGGACGTTCGCC +GACAAGGCATTGCCGCGCATCCAACCGGGCATGCAAAAGGTGGCCAAGGCCATCAACGGCACGTTCAAAG +AGGGCCTCGCTGCCCTCGGCGACGACAAGAACCTCAGCCTCGCCGACCAGATATTCGGCAACACCGCCGA +GGGACAGCAGCGCGCGAACAACGCAATTCGACCGCTGATCTCCGCATTCGGCACGCTGGCCAAAGAGGGC +AGCGTGTTCCTGCCGCGCCTCGGTGACGCGATCACCTCGGTGGCCGAACGGTTCAATCGGTTCATTCAGA +CCAACACCAATAACGGCAACATCTTCCGATGGATTGACGAGGGTCTCAACGGCGCGCGTGCGTTCGGCAA +CACGATCCTCAACATCCTCAAAACGATCACGGGGCTGACCAAGGCCGCCGGTCAGGCCAAGGGGTCGCTG +TCCGGTGACGGCGGTCTGCTCGGTGCGCTGGAACGCGTCACCGAGCGCTGGTCGACGTTCACCAACAGCG +TCGAGGGACAGCAGAAGCTCGGCAAGTTCTTCGCCGACGGCACCGCCGACCTCCACCGCTACGGTGATCT +GCTGCGCACGTTGTGGCCCACGATCAAGGAAATCATCGCCGGGTTCCAGACGTGGGGCGAGATCGTGCTG +CCCATCGTCTCGGCGCTCGCCTCGCTCACCAACACGCTCGCGCAGATCCCCGGCCTGCTGGAGGCGGTCG +TGGTCGGCTTCCTCGCGTGGCGCACCCTCGGCCCGATCATCGGCGGCGTGGCCAACAAGCTCAACGCGGT +CGGCGGCGCGGCGGGCAAGCTCGGCGGCGCACGCGGACTGCTCGGCGGGTCGCTCATCGCGGGCGGCGGC +GCGATTCAAGCCTCGACCGACAGCGGACTCGGGCAGCTCGCCGGGGCGCTCACTCAGGTGGCCGGTGGTG +CGCTCGCAGGCTCGGTGTTCGGCCCGGCGGGCGCGGCTGTCGGCGCAGGTGTCGGCGCAGCCATCGCAGG +CATCACCTACGTGCTTAACGAGAATGCACGCGCGTCAAGGGACGCGGCTGCGGCTCAGGCCGAGCTCGCC +GAGAACCTCAACCGCAGCCGCATGGCCATGGAGCTGACCAGTCAAGCCACCAAGACCGCCAACGACGCGC +TGCTCGCCTCGGGCGGCGCGGTCGACGCCGCGACCATAGGCGCGGTCGGCGATCAGATCTCGGCGATCCC +CGACCGGCTCGCGGGATCGTACGACGAGGACACGATCAAGGGCGTGGCCACCGCGCTCAAGGATCTGAGC +CTCACCGAGCAGCAGCTTGCCACCATCGTCACGGGTGCACAGCCCGGTTTCGATGCCCTCACCGCGCGGC +TGCGTGAGATGGGGCCGGGCGGTGCCATCGCGGCCCAGAACCTGCAAGCGGTGCGCGACGCGACCCTCGG +GGCCGCGAACAACGCGGCCACGGCGGCCCCGCTGTTGCAGAACCTCGCGCAGACCCTCGGGGTCGACGTG +CCTCAGGCGGCAGCCGAGCTGACCAATGCGTTCAGTGCGGTACCCAAAGAGGTGCCCATCAACATCGACG +CGCCGGGCGGACAAGCGGTATTCGACATGCTCGTCGCTATCGGTGAAAAGGTCAGTGTCGACAACAACAA +GAACATCGTGGTTGAGGCACCGCTCGCCCCGGCGGTGCTTGAGCAGCTCAAGGCCATCGGCTACGAGGTC +ACGCAGAACAACGACAAGACCATCACCGTTCGCGTCGACCCGCAGATGTACGCGGACACACTCGCCAAGC +TCGGCAACGTCGGCAACGTGCTGAGGGATCTGCACGGGCAGAGCCTCGGTCTGCCAGCGGTGCCCGCCGG +ACCGAACAACACAGCGGGCGGACTGTTCCCCGGCATGCCCGGTCCCACCGGCCCCGGTGGTGCGGACGGC +ATGGTCGTCCCCGGCTACGCACCCAAGCAGGACACCGTCAACGCGGTGCTGGCACCCGGTGAGGGCGTGC +TCATCCCCGAGGCGGTGCGCGGCCTCGGTGGTGCGCAGGCGATCTACGCGATCAACTCGCGCTTCCGTAA +GGGCCTGAGCAAGCGGTACTACGCTGACGGCGGTGTCCACGGCGGCACCGGCGCGCTGCCCGGCCCGGCT +GGCATCGAGACCGAACTGTCGGTGCTGGTGCAGATTCGTGACATTCTCGCGGGCAAGGGTGGTCCGGCGA +GCAACCCGCTCGCAGCCACGGCGGCCAACACCGGCGCGGTGGCCACAGCGACCAAGGCCACCGGCGGCCA +GAAGCTCGGCCCGTTCGGCACCCCGATCAAGGACCGAGATCCGGCCTACGAGGCAGCGGCGGCGGCCATC +AGCGCGCTCGGCGGCGACCCCGAGAAATTCCTCGGGCAAAACCCCGTCGACTACGCGGCGGCCCAGGCGG +CGCAGGCCACACAGTTCGGCGGCGGCCTCGGCGTCACCGGCGCGGTGAACATGTCTGCGTACATTGAGGC +GTTGACGGCGTTCGCGATGACCGGCGACCTGAGCAAGGTGCAGGGTATCGGGCTCAACGCGAACTCGCCC +GTCATCACCGCGCTGACCTCGGCGCGCAACAAGGCCAAGGGCGGCCTGAGCGATCAGCAGATTGCCGACC +TCGTCGGCACCACCCTCGGCCCGACCGGATACACGGGCACGCTAGACAGCACCAACAGTTCGCTGATCAA +GTCGCTGACCCGGTACCGCGAGCAGCTCATGAAACAGGGCGGCGCGGCCCCGACGGTCGGCGGCACCTCG +GCAGTCGCACCGGCCCTCGGCATCCCGATGACCGCACTGCCCGCTGGTGCCATGGACCCGATCAGCGCGT +ATGCGGCGGCCCACAGCGGCGGAAAATACTCGTGGGGCGCGTCGGATCTCGCCGCCGGTCTGAGCGACTG +TTCGGGCGCGGTGTCGGATCTCGTGGAGCTGCTGACTAAGGGTCAGGCCACCGGCGAGCGTCTGTTTTCG +ACCGCCGATGCCGGATCGGTGCTCAAGAGCCTCGGCGCGGTCGAGGGCGCGCTGCCGGGTGCGTTGCAGA +TCGGGTGGGACGCGGGCCATATGCGTGCCACGCTGCCCAACGGTGTGGCATTCGAGTCGGGCGGCGGCAC +CGGTCAAGGCGCAACCTACGGCGGCAACGCTAAGGGCGCTGCGGGCATGCCGAACATCATGAGCCTGCCG +GTCGGCGCGCTGCCCGCCGGTCTCGGCATGAGCGGACTGCCGGGCGGCGGCGTGGGTGGCCAGGGCACCC +CGGTCTACGTCACCAACTGGCCCGGCCAGGGCGGCGGCATCCCCGGTCTCAAGAACATCCTCGGGGCCGG +TATCGGCGCTGCCGGTCAGGCGGGCACCGACGTGCTCGGCGACATCATGGGCGGTGTGGTGGGCCTCGGC +AATGAGCCGTGGAACACTCCCGGCAGCTACGCGGCGCTCAACACGCTCGTCAAAGAGGGCAACCCGCTCG +CGCTGGCCAAGGCGTTCGGCCTCGATGTCGCCGACTTCTCGCGCGCGGGCGGCGCGGCGGGCGAGCTCGA +AAAGAACGAGGGTCGCGGATACGACGCGAGCGGTCGACTGTTCTCCGACACCGGCGCGCTGCTCGACCGC +ACGTTCACCTCGCTCAACGCACAGCTACAGGCGATGCGCGAGCAGATGGTCGACGTGATCGAGCAGGTCA +GCCAGAAGCTCAACGACAGCGCACTCGAGCCGGTCGTCAAGGCCGGTGTTCAGTCCGCGCTCGAAACGCT +CAAGGACAGTGTTTCAGCGTCCATCGGCACCGCCCTCGGTCAGGCGGCGGCCCCGCCCATCGCCGACGCT +GTCAGTAGTGCGGTGTCGTCGCTGCCCATCGATCAGTCCGGCGCGGGCAATGTCGGCGGCAACGCTGCCG +GTGTCGTCACGGGTGCGCTCGGCATGGCGAGCGGCGGCCCTGTGTCCGGCGGTACGCCCGGCAAGGACTC +GGTGCCCGCGCTGCTCATGCCCGGCGAGTACGTGCTCAACACTTCCGATGTTGCCCGGCTCGGCGGCATG +GCCGCCATCGACTCGATGCGGTCGCGCGGTTTCAAGAGGTTCGCCACCGGCGGCGGTGTGATCGGCAACG +ACACCGTCGGCGCGGATTTCTTCGGTGTGTCTCAGGTTCCGATCATCGGGGCCATCGTCAACTTGCTCGT +CCGAGTGCTGCTCAAGGTCATCGGTGTCGACATCGAGGTGCGCGACACCCTGATGGAAATGACCGACGAT +TTCCGGCAGTTCCGAGGGGACGCGTTCAAGGCGTTCGACGCTCAGGGCCGGTTGCTCAACGACACCTCGG +GTCTCATCGAGCGCAGCCAGTCGAGCGAGGAAACCGCCGCCGAGGAACGTATTCGCATCCTCAAGATCGT +GATTCAGGCGCTCATCAAGTACATCATCGAAAAGGTGATCGTGCCCATCGCCAAGGCGGTGGCCAACGCA +GCCATTCAGGCGGGCGCGTCGGCGGCGGGCGCTGCGGTCAACTCTCAGGCACCCGGCGCGGGCGGCATCG +TCAGCTCGCTCATCAGCTCGGCGGGTCAGGCGGGCGTCGACATCGCCGCCGAGGTCGGTACCGACTTCGC +ACTCGCGATCAGCGAGACACTCATCGATGTCGTCGGAGAAGGTCTCAAATCGCTGCTGCCAGATCTGACC +ACGGGACTGTTCGGCGGAAACGGGCTCGCGTTCCTCACCGACCCCGTCGGCTCGCTGCTCGGCGGCCTGC +TTGGCGGCATCGCCGGTCTGTTCTCGTCCCTGTTCGGTGGGGCGGCAACGATGATCCCCGGCATCCCGTT +CGACAACGGCGGCCTCGCCCACGGTGAGGGCCTCATGCTCAAGGCCACCAGCGATCCTGAGCTCGTGCTC +AACCCCACCGAGACCGACGTGTTCACGAGGTTCGTCAAGGCACTCGAGAATGGCGGTTTCGGCGGCGGCA +GCAATACCACAGTGCACGCGCCGATTACAGTGATCGGTGGCGGCCCCGAGACCGCCGAGCAGATCGAGAA +CCGCCTGCTCAAGCACATGCCTTAGGAGACGGTCGTGGCATTTCGTGGCTACTTCGCCCTCAACGGGGTC +GAGATCGCCAACAGCAGCCGCGTGGCCGCCCACATCGGGGCCGAGGTGCCTACGCGCGACATCGGGCTCA +TGACCGCCGATGTCGACTGCTCGCTCACGCCTATCGATGACGACCGGCTGCTCGCCGAGCTGCCCGCCAC +CTCGGTGCCCATCGGGGCCGGACGGCTGCTGGCCACACCGCCGGACGGGTCGCGGCTCTACGGTCCAGGT +CTCGCCGTTGTCGGCGACTGTTGGACACCGAACACGCTGTGTTTCGGCTGCCGCACCGCCATCGAGTACG +ACGATTCATGGACCGGCCTGCCCGCCTTCCTCAACGATCATGTCTACCGGCCCGAGCTCGCCCCGTGGTT +CACGACGAGGGTTCCCGAGTCGGCAGAGTTCGCGGGTGTGTGGGTCATGGATGTCAAGGGCCTCGACGTG +ACCACCTCACAGCGCGAGGTGATCGAGATGGCCGGGGACGGCGGCGCGGCGGGCATCCATCGTGACGGCG +CGCAGCGGATACAGTTCGACGTGCTGCTCGTCGCGTGCACCAACGCGGGTGCGACGTACGGTCTCGACTG +GCTGACCACACAGCTCCGCAGGACCAATGACCGGACCGACTCGGTGTTGCGATACCTCGCCGCCCACCCC +GAGGACAGCGCAGTCGACCCGACAACCCTCGTGCGTGATCGCCACGGTGTGGTCCTCACCGCCGGTCCTG +AGATCACCGGACAAGTCAACGCGAGCGGACGGCAGCACAACCAAGCGACGTTCTACCGGGTCACTTTCGA +GCTCACCGCGCTCATCCCGCACGCGTACCGGCCTGCCACGGTGCTGCCCGTCGAGTGGGACACCGTCGAG +GTCGAGCCGATCCAATGGGTGCACTCGTCGGAGTGCAAACCGCCCGCCGACTGCTCGCCGATGCCGGTGT +TGTTCGCGCAGGGCTGCGAGGTCGAGCGCGTCGAGGCGGTGTCCTCCCCACCCCCTGTGTGCGGCGGCTG +CATGCCGGTGTGCGCGGTGGCCACACACGTGGTGCAGATCCCTCTCACCGAGCGCCCGCAGACCGGCACC +ACTACCGTGGTCAGCCTCGCGATCCGAAACACCGACGCGCGCCCGCTCACGCTCAACGGGTATTTCCGGC +GGTGTAACGCTCGCGACGACTGCGACGATGAGCTTTTCCCCGTGCAGATCCACGGTCTGCCCGCCACCGC +TGAGGTCGTGCTCGACGGCGTCACGGGCCGCTTCTGGGTCTACTATGCGGGGCGCAAATGGCGGCCCGTG +CACATCGTGGGCACCCCGAGTGGGGCACCGTGGGTGCCTGCCAAGCTCGACCGATCGCTGTGCTGGGAGT +TCATCGTGGTCTCCGACGGCACCGCGATGTTCGAGGTCGACCTCGCGCTCACGGATCGTGACGAATGAGT +GCGCCGACGCGGGTGCTCGACCGGGGCGGTGAGGCGCACAGGATCGTCACCGCCGAGCAGGGCGTGACGA +TCCACACCGACAACGGCACCACCATCGATCAATTCACACCTCGCCAGTACACCTCGTGCACGTGGGGCTT +GAGGCTGCGCGACGCGGGCACCGCCGACATCGTCATACCGCCGACCGCCGACTATGACCGGCTGCGTGAC +ATCGAGCCGTGGGCACACTCGGTGACGATCTGGGACGTAGACAGCGGCACCACATTGTGGACCGGCCCGA +TTCAGAAGGCCCGAGCCTCACGCAGGGGCATGACGATCAGCGCCAAGGATCACTCGGCATACCTCAGTCG +CACCCGCAATCCGCTCACCAAACGGTGGGACGCTGCCGACCCCGCCACGGTGGCCGGTGAGCTGTGGGCG +GCCATGGTCGAGGCGCAGGGCATCAACACGCGGCCCATCGTGCGCGTCGACCCCGAGGGTGACCGCTACG +ACTTTCAGGTCGTCGCCGACGAGCAGATGCTCGACCAGACGTTGAGCGACATGAACAGTCAAGGACTGCG +GTGGACCGTGGTGGCGGGCACCCCGATCATCGGGCCGGTGAGCACCAAGGCGCTCGCGCTGCTCGGCGAG +CACGACTTTCTCGGCGACGGCATCGAGTTCGTGCGCGACGGCAGCCAGACCTACAACGATGCGCTGGTGC +GGGCCGCCGACAGCAACACCGCGCGGGCGCGCGTCGACTACCACGGCAAGAACCTACAGACCATCGTCAA +CCTCGACTCGATGTTCGGGGTGTCCAACGTCAACCGCGCGGCCCGGCAGTACGTCAACCACACCGGCAGG +CCACACACCAGACTCGAGTTGACCGGAGGCACCGAGCTACATCCGAACGCGCCCGTGTCGATGGAAGACC +TCATGCCCTCGGCGCGGTTCATCATCGAGGCGCGCGGTGTCCGTCAGCTTTTCGAGCTCACGAGTGTCGA +CGTTGAGCGTCGGCAGGGTGCGGTGAGTGTTCGTGTCACTATGGGCACTGTGGAGGACGATATCGAGTTG +CTCGACGCGGCCAACGGCAAGCAGCAGAACATGACTCTCGGAGGTCAACGGCTGTGACGGTCGAGCTGCC +GGGGCGGGCACCGACAACCGACGGCGAGCTCGCCCGCCAATTCCACGAGCGCATTCGCCGACTCGAGAAT +CCCCGCTCGGTGCGCGTCGGCCCGTGGGTCATCGCGACCGACCCGATCACCGGCGACCTCAAAGCATCAC +GTCCCGGTCAGACCCTCATGCTCGGCGGCGATCAGCCCGTCGAGGTGGTCGAGCAGTCGCTCAGCCTGTC +CAAGTTCGTCACCAAGGACGACCTCGACGCGGCCCTCGACGGGATCGAGGCGGGCGGCGGCGACAACGAT +TCGGTGTGGGCGCAGCTCTATGAGAAGCTCACCGGCATCCTGTCGCCGACCAACGCGCTCAATGCGCTGG +CCAATTTCTTTCGGCTTGAGCTCGGCGCACCGATCACCAGCAGCAGGCTGCCGCTGCTGCCGCTGTCGCA +CATCCGGCCCATATCTCCGAACTTGCTCACCGACGGATCGTTCGACTACGAGGAAACGCTCTCCGGTTTC +CCCGATTGGGACTGGGACGACACCGTAGGACGGAACAAACCGGGCAGCGCGTACACCGTCGCTGATGGCA +CCACCCACACGATCCACAGCAACAGCATCGAGGTCGGGACGGACGACCGGCTCAACGTCGAGGTGTACGC +ACGTTGGGCCGGGCTGGTGGCCAGCGGCGCGGCCATCAAGCTCGATATCTCGTGCTACCGCGACGACGGC +TCGCCAGTTTTCTCCGGTCCGATCACGCTCGACTCGCGGACGGTGTCCGGCACCGCCTCGGGGTGGACCA +AGCTCAGCGTGACGAACTGGGACGTTCCCGACGAGGCCCGGCACGTCGTGGTCGAGTTGACGGTCACCAG +CGGCGCGACAGCGGGCGTCGTGCACTTCGATGACGCGGGGTGCTTCAAGATCGGCACGATGCCGCAAAGC +TATATCTCGGGCCTTGTCGAGGGCCTCAATGCGTTGTGGCAGGGCATCCAAGCACGGATCGATGACTTTC +TCGACCTGCTCGACGTGTTCGGCGGTTTCAACGTCGGCGATCACATCGGGCAGCTCACCGATGTCGTGAC +GCGATTGCAGCACCTCAACCCGCTCAACGGACTGTTCGACGCCAGCAAGCTCGGCAACATCGCCAACATC +CCGATGATCGCGCAGGAACGGATCTCGGGTCTGGTCACCGCGCTCAACGAGGCCGGGCAGGGTATCCGTG +ATGCCATCGTGCAGGCTCTCGGCGGCAGCGGTACCGGGCACAGCAACATCGACGTGCTCAACGCGCTGAT +GAACATTCCGGCCAGCGTCGTCAACACCGCCATTGCGGGCGCGTCGAACATCGAGAACGCACTACAGCAG +GCCATCGACTCGGTCATCGCGGGCGCGGGCGATCTCATAGGCAGCGGTTTCGGTTTCGCCGACATGATCG +CGCAGCTCACCGGCCTGCGGCGGGCCACCGCCGGTACGGCGGCAGCGGTGGTCAACCTACAGTCTCAGGT +GGCCAATCTCGACCCGGCAGCCAACAGCGAGGTGGTCGATTTCGGGGAGTTCGCCAACTCGGCGTCACCG +CCCTCGATGTTCACCAAGGTCAGTGACACCGGCGCGGGCAGCATCATCCTGACCAACGGTGTGCTGGCGT +GGAACTCGGCTGATGCGGTGGGGCGCGAGTTCTATCTCTACAACGGCGGCCCGCTACAGACCGATCTTTT +CGAGGTGCAAGTTGTTCTGCCCTCGATGCCCTCACACGGCATTTTCGGTCTCGACTCGACCAACTACGTG +TATCTCATCGGACGCTCGGATGCCACCGGCAACAACATGGTCGTCGCACGGCTCGGGTGGGACGAAGTAC +GGGTCTACTCGCGCAACTCGGGCACCATGACACAGATCGGTCCGACGATCAGCGCCGATGACATTCTCAC +CTCGGGCTCGTCGGTATCGTTCAAAGGCGGCACCATCGCCGATCCACGCTACTTCACCGTCTCGATCAAC +GGCGACAAGGTCTACGAGTACAGCGACGAAGCACCAATCACCGTCATCGGCGCGAGCAACCGTTTCTGCG +GTCTCGGACTGGAAAAGGGCAACAACTACGCGACGGGCCGGATCTCGACGTGGGCCATGCTCGATGGCGG +ATCGTCGGCAGGATCGGGTGTCGTGGCCGGATACACCAACGCGGGCCTGACAAACTTGATCCTGTGGAAG +GGTACGCAGGCTCAGTACGACGCACTGCCGACCAAGAGCCCGAACGGGATCTACGTGGTCGAGGGGTGAG +CGGTGGGCGTCTACATCGGTGACACCCCGATCAGCAAGATCATGGCGGGGACCTCGCCGTTCGCTGGCAA +GGTCTACATCGGATCGACGCAGGTCTGGCCCGAGGTCGAGTTCCCGCTCGTCTACGAGGATGTCAACCTC +ACGGATGCGCTCGTGCCCGCAGGGGCCACCGCGCTGGTCATAGAGCAGCTCGTCGGCGGCGGTGCCTCGG +GTCTGGCAGGGCAAAATCAGTCCGGCGCGGGCACCTACACCAACCGTGGCGGCACCGGCGGCGGCGGCGG +TGCGGTCATCGGGCAGCACATCATCCCCATCAGCGAGCTCGGCCCGACGTTCAGCTTGCAGATCGGCGCG +GGCGGCGAGCAGTCGACATCGGACACCACACGCAACAACGGCGGCCCGACGATCTTCTCGTCAGGCAGCA +TCGTGCTCACCGCCGGGGGTGGGTCAGCCAGCGGCGGCGTGGCCAGCCAATCGGGCCAATGGTATGTCCC +GATGGCCAATGGCACGGCGGGCGGCGTCAACAACACCATGGGCGGCGCGGCGGGCGGTGCCGAGGGCGTG +AGCTACGCGCACAGCGGCAACCCGCAGAACGACAACGAGAAATGGGACGCGCTGTCTGCTGACCCGGCGG +GCACCGCCCTGCAAGTCCTGCCGGGCGGCCTCAAGCCGGGCAACGGCGGCAGCCAGTCCGGCACGGGCCA +GCGTCCCGGCGGTGGCGGCGGTGGCGGCAAGGGCCGCAGCTCGTCCTCGCGCGGCGTCGGCGGGCGCGGC +GGTGACGGCTATGCGCGCATCTACTTCATCAACACCGCCTACAAGCGCGACATCACACGCGTCTACACCA +CCGCCGGTGCGTGGACGTGGACACCGCCGCCGTGGGCGGTGGCGGGCACCAAGATCGACATTGTCATGTT +CCCCGGCGGGCGCGCGGGCAAGAACGGTGCGACGTTCGGCGGCGCAGGCCGAGGCGGTCTCGCGGGCACC +CCTGTGTCAGCAACCCTCACCGTCGGCACCGATATCCCCGTCAGCGGGGCGCTCACCGGCGTCGTCGGCG +CGGGCGGTGCCAGCAACGGCGCAGCGGGCGGCGTCAGTACGTGCACAACCGTGGGTCTCACCGCGCCGGT +CAACAATGCCGACGCGAGCGGTCAGGACGGCGGCAACGCGCCTAACGTCACGCTCAACGGGATCACGTAT +CCCGGTGGGCGCGGCGGGTCGAGCGGCACGAGCTCATCGGCGGGCGAGGACGGTCAAGACCCCGGCGGCG +GCGGCGAGGGCGGCGGCTCGCTGATTGTGGGGCTCGCGGGCGGCGCGGGCGGCAAGGGTCGCGTTTACGT +ACGCGTCTACGAGGTGTAAGCCGTGCCGTTTGGCTGGAATCCCACACCGCAGATCACCCCGCGCGTCACA +CCGCGCGGGTGGTTCAAGGACACGCCAGAGCTGCCCGTCGACCGTGAGATCGGGTGGTGGGCGGTGCTCA +CCCTCGACGCGATCGACCTCGGCGACGCTGCGCAGACCATGACCCTTGAGGCGCTCAAGGCGCTCGCGCT +GGCCAACCTCGCCGCCTCGTCGCAATCCCTTGAGCTGCAACAGATCGCGGGCATCTCGTTCGCCAACATG +ATCCCGCCCTCCCAAGCGCTCGCGCTCAAGAAAATCGCACTGCTCAATCTGTCCGGCGTGGCGCTGCCGA +CGCAGGCGCTCGCGCTGGCCAAGGTGCTCGGCATCGCGCTGTCGAGCATGGGCATCAGTACGCAGGCCCT +CGACCTCGGCAAGGTCGTCACCGTGGCCATGGCGACGGACAGCCCGGCGGCCCAACTACTCACGCTCACC +AAAAAGGGCGTGCTGTCGCTCAACAGCACCGCGACGAGCACACAGCAGGTGGTGCTCGCGCGGGTGGCCT +CGCTGATCATGTCGACCTCGCTGGCCACCTCGGTGCAGTCCGGCCTCGCCGTCGGTTTCCCGCCGTTGAC +AGCGACCACGGTCACGTTCACCAACACCACCAACATCCAATCGTGGACGTTCCCGCGCAACGCAGAGTGG +CTCGCCGTGGTCTGCCTCGGCGGCGGCTGCGGTGGACGCGGCGGCGGCCCTGTGCTCGCGGGCGGTGGCG +GCTATGCGGGGTCGTACGGCACCGTCCTGTTGAGGCGCGGCGTCGACATCCCGTGGTCGACCACGATGTT +CTACATGAACGTGGGGCCGGGAAGCGCCGGGTCGTCGGGCGGATACCTCGTCAGCGACCCCTCGCCGGGC +ACGGCGTCGCTGTGCACCACCACCGCGCTGGCCACCCTCGCCAGCGGCAGCGGCGGCGGCCCGAACTACT +CGACGCAGCGTGGCCAGGACGCACCCGACCTCAGCTACAACGGGATCTCGGCGGTCGGCGGCAAGTCATC +GGGCGGATCGGCGACGGCGGGCGGTGTGCCCGGCGGCGGCGGCAACGGGGCCAACCCAGGATCGTTCGGC +CTACCGGGCGGTGCTGGCGGTGCCGGTGGGCGCGGACAGATATGGGCGCGAGCATTCCAGTGACCGCCAT +CCTCGACAGATAGTATCGACCCCATGGCCGTCGGACCTACCGCCTACTTGGTGAACAAGCTGCTCGATCA +CGCGCTACGCGGGGTCGCGTACACACCGCCCTCGGTCATCTACTTCAAGGCGCACACCGGCGACCCCGGC +GCGTCGGGCGCGAACAACGCGAGTGCGCAGACTGCACGCGTGGCGGTCACGTTCCTCGCCGCCTCGGGCG +GCACGGTGCTGCTCACCGGCACGCCAGAGATCACGCTCAACGCAACCGAGACGATCACTCACGGATCACT +GTGGGACGCCGCCGGGCCAACGGGCGGCAACTGTCTGTGGACTGCGCAGGCCACCGTCAGCAAGGGCGGC +GTGTCCGGCGACATCATCCGGCTATCGGGCTTCCAAGTCGGCTTCACCGGCCTCGCAGCGTAAGGGAGAA +CATGACCGCACTGACACCGACCCCTGATTCACAATGGGCTGTGCAATGGGAGGCGTTCGCGCCCGAGGAC +AGCAGTGTGATGCCCGAGCGGCCCATCCTGCCCGAGGCACCCCGGTCCGGCACCGACGAGGACACGCCTG +AGAACTGGGCGGCCTATCAGGAAGCGTATGGGATCTACGTCGAGGCCATGGCTTCCTACGAGAGTCTGCT +TGCAGTGCTGCTCGCCGACGATGCCAATTGGCGCACGATCACCAACGGTTTCCCCGACGAGGCGCTCGCG +CGGCAGATGGTCGGCATCATCCGCAGTGCCAATGCGGGCAACCCTCAGACGCGTGATTTCCGGCTAGTGT +GGTCCGCGCCCATCGTCTGGCACATCGCGGACGAGTGATGTCGACACCGCGCGTCTGCCTATCCGAGAAC +CTCGCCGTCAGCCCCGACGGCAAGCTGAGGCTGCCTCGGTGGTCGGTCGTGCGCAATGTCGGCGACATCG +TCATCGCGTCGGCGGGGGACACCGCCAAGCTGCTCATCAGCGACACGCTTCCCGGTCGGAAGCTCATCGA +GGGGCGGCTCGCGTGGACCAACGACAGCCCGGTCGAGCAGCAATTGCGCGTCGAGGTTACGCGTCGATAC +CGGCGATGGGTCACGTCTAACCCCAATGCCATCGAGTTCCGTGACCGATGGTCGTGGGTCGTGATCCCCG +AGGGCGATCCACTCATCGATCCGGCTGAGCCCGATGTGTCGGACACGTTCAACGGCAAGACCGGAAGCGC +CGGGGACATTCAGACCAACACCGTTGCCGAGCCGTTGCCCGGTGTGTTCCGGCATTGGTGGGGCACCACG +ACCTCGGAAGAGTGGATACCGGACGTGCTCGCGCCCGGCGACACATTCTCGCTGTGGTACAGGGCCTACG +TGTGGACACCGCCGCCGTGGTCGGACAATGCCAACAAGAACGCGCCGACGCACGCCGCCGAGGCCGGATA +CAGCCGGTTGCAGCTCACTGCCTACCCGGCTCAGGGCAAGGTGGTCGTGGGATGAGCCTCAAGCTGTGTA +CTGGCGAATACATGCTCAGCACCGTGGCCGGTCTCGGCCCGTCGCGCGGGTGGTTCCCGCGTGTGCTCGC +CGAGCAGATGCTTGAGTCGACCAAGGACGGCGAGATTAAGCTCTCGCCGGACCCCGTCACGATGATCGAC +GGCGACCTCACGTGGTTCAACAACAGTGATGACCGCGTGCGGGTGTTCGTGCTCGTGCACACCGCGCCAC +GCGACATCATCGCGCAGAATCCGTCCACCGTGGTGATCCACGATGCGTGGTCGCACCGGGTGGGCAGGGC +ACCCTCGGCTGATTTCCCGAGCGCGATTCGCAACAGTTTCGGTGGCCGGTTGCAGCTCGACCGCGCCTCG +GTGGCCCGCGACGCGGTCAAGTTCGGTCGGTTCTTCCTGACCGGCGACGATCAGCAGGCGTGGGTGGACC +TCGGTATCGTGCGTCCACAGCAGTCTTTTCACTTCCGCTACCGTGCGGCAGTCCAGACACCCGGCACGTG +GGTCACACCGAGCGAGCTTGAACCGCGCTGGGAGGCGTACGCGCGGTGGACACGGCTCGTCGCGCTCGGC +GGCCCGGTAGGAGAGTCATGACCTCGCCCGATTGCATCGACCCCGATCACTTCCGCGTGACCGCCGACGG +CGGGATCGAGCCGCAGCCATGGATGCAATGGAGGCACGTGCGCAGCATCAGCGCCGAGGGCAGATCGGGC +ACGTACGCGGTGACCGGCGGTGGCAACAAGAACGTGCTGATTCATGGTCTGCAAGTCAGCTACACCAATG +ACACCCCGGTGCCTCAGGCGGTCTACGGCAAGATCACGCGCGGCGGCTGCCGCGTGGCCCTACAGGCACG +CTCGCGGGCCTACCTACAGGTGTCCAGCGGATACCAGAAGCACGCGAGCGACCCCGGCAAGCTTGAGGTG +TCGAGCCGGATGGGCTGTGGTGCCGACATCGGGCGTGGCGGCACCCTCGGCATCGGCACTCAGTTCTGCG +TCATCGAGCAGCGACAGGGGATGGTGACGATACCGCTCGCGCCCGAGCGGGCCGGGTGGCTCACCCTCAG +CCCCGGTGAAGCGGTCACCGCGCGCGTCGAGGTCAAGTTCATCTCAGAGTTCTGGGAGAACACCAATATC +GACGGCGGTGCGTCGGGCTCGACCTCGGGCTACGACTCGGGTGCCACACAACTCGATCTGTTCGCGGTCC +CGGTACTGTAGGGGTAATTCCCCACCTCACTCGGGAGAGTGCCAGTGTACGAACCACCGCCCGGCTACGA +CGATTGCGAGTCGGATGCCGACGATTTCCCCACCCCGGCCAATGCGGCGTACCGCGAGCTCGATGTCGAG +GGTGTCGGCATCCTGCACGCGCGTAAACCGCTGCCCAACGCTATCCCCGCGCTCGCCGGGGCGGCCAACA +GCAAGGTCAAGCCCGCCGCCCGCCTCGACTACCTCACGGTGTTCATACAGAACCACCTCGCGCCCGGCGA +GTACGAAACTCTGCTGGCCAAGATGATGGACCCCGAGACCGAGCTGCCCGAGGACACGGTGCTACGTGTG +AGCCGAGCTATCGCGACGGCGGGCAGTGCCCGCCCTACACGGCGGTCATCAACCTCGCGTTGATGGCCGC +GCACAATTGGCGGGCACTGCGGCTCAAGGCCCTGGGCCAAGGCATCACCAACATCATGGCGTGCCCATCG +ATGCACGTCGTGCTGGATCTCATCGAGCAGCTCGGTGCCGAGGCGTCGGTGTCTGAGGCCAAGTCGCAGA +CTGAGGCGCGCAGCGAGCTCACGCGTTACTACGACAAGCTCTATAAGCCCGACATCACCGCCAAGGTGAT +CAACGGCGAGAGCTATTTCCCGCCGCCGCCCGGTTTCTCGGACGAGGAAGTCGAGGCATCGTTCGACGCG +TTCCTCAGCAACCCGCTGCGATGAGTCGGGGAACTTGCCGCCCGCCGCGATACTCTGTGTGCTATGGCCG +TCACCGCTGTCCTGTTTGAAACCTCAAGCACCCGAGGTAACCGCCTCGACCCCGATGTGCGTGACGAGAT +TGAGGCGCTCGCGCCGGGCCTTGAGACCGGGGAGGTCACGACCGACAAGATCGCCAATGACGCGGTCACG +CGCGCGAAGATCGACGCGGGCGCGGTGGGGTCGGTCGAGCTCGATGACGACGCGGTCAAGGCGCAGCACA +TCGATGATGGGGCGGTGGGGACACCAGCGCTTGCGGCGGGCGCGGTCACCTCGGAGAAGACCGGTATCGG +TGTGGTCACCGCCGAAGATGCCTCGGGCAACCCCGTCGAGGTCAAGCAGGTCTACATGACCGTGGCGCAG +TACAACGCGCTGGCGACAAAAGACCCGAACACGGATTACTACCTGAGCTGATGGCGACGATCAGGCGCGG +TGCAGCGGCACCCTACAAGGCGATCTATCACGGCACCACCCCGATCAAGCAAGTCCGGCGTGGCGAGACC +CTGTTGTGGTCGCAGGGCAAGATCCGAGACGATTTCACCGACATTCTGGACCGGTGGCTCAACGAGTTGC +TCAGCGGCGACCTCGGGGCGCTGTGCTCCGATGTCACCGGTGTGCTGCGAGACGGTCTCGGCAACATCGT +CGGTCACACCGTGGCTTTCGTTGAGGACAACATCAACGGTCTCGGCAAGCTCGTTGCGCAGACCGGGCAA +GATCTCGCCACCGCCTACTGCGGCGTGTGGGGCGGCACCGCCGCGCCCAATGGTCTCATCGGACTGATCA +ACGGCATCCCGATTTTCGGCGGCATCCTCGCCGACTGGCTCAAGGGCGAGCTCGATATCGTCTCGATCAT +CGGCAGCATCCCGATCATCGGCGACATCGGGCGACTCATCGGACTGATCCCCGACGCGCTCGGGCACCTC +GCCGACCCGATCAACTATGTGGTCGACGCGTTCGGCGAGGTGATAGGCACGATCACGTGTGGACAGTTCC +GGCCCCACGGCGGGGACAACGAGGGAATCTGCTACACCATCGGTGTGCTCGGCGACATCGCCAAGATGCT +CATTCCCGATGGCCTGCTCACGCTCAACCGGCACACTTCGCGTCTGCGTCACGAGACCTTGCTCGACGGT +GATGACGGCTACCTTGAGGTGCAGGTTGCCGACCTCGGCGCGCCGGACTACCCGACTCAGGTGTTCCGGC +GGTACGCGAACAACGGCAGCGGTGCCACCGGAGTCGGCATGCAGTTCATGAACTCTCAGGTCTCGCTCGT +GCGGCGTGTCGGCGGAAATAACACGCTCGTGCTGCCTCAGGTGGCCAGCTACTCGCCGGGCGATGTGATG +CGGCTGGAACAGTTCGGCGACATTCACTCGATCACCAGAAACGGCGAGACCGTGGGCGTGTGGGAGGACA +ATAGCGCTACTGCGGCCAAGGGCGTCAACAACCGCTCGGTGGCCATGATCATGCAGGGCGCTAAAGAGCT +CAACACATCGCGCAAGGTCTCGCCGGGTCTGGCCTACCTCGAGGCTGCTTGATCGGTCACGTAGCCGAAC +AACCACGGCTCATCACGCCGCCTCGCGTACTTGTGCGGCTGGCTGCGGTAGAGGCCCGGTTTCTGGCCGG +GCAGGGGGCAGATCGGCGTCCACACGGTGAGCTCGTGCTGCGTGTCGGGTACAACACGGCGCTCGCCGTC +TAGTGGGCCGCCGTAGAGCTCGACGCTGCTGGCCCTGTGCATGTCAGTCGAACCAGATCCGCTCGCGCGT +GCGGCGCTCGTGGGTAATTCGCTCGCGGCGGGCACCGACGGGATGCTGTGCGGGCGGCTGTGGCGGCCTA +TAGGCAGCCTTGTCGAGCTGCCCGAGGTATCGCAGCCGGACGGCAATGCCGATGCACACAGCGCCGACGA +GGGCGGTCGACATCGGGTGTGGGTTGTCCGGCAGATAGCTGATGAACAACATGAACAGTCCCACCGCCCC +GGCGGTGGTCACCTTATCGTGGTGACCACCGTGCGGGGTTCGTTGGACGGTCACCGCCCGCCCCACCAGC +CGCGACCGGGCCGCGTGGCGTGCCACGCGTCGATGGTCTCGGGCAGCCATCCCTTGTGCTTGCCGACCTC +GACATCGGGCGGTGGCAATTCGATGCCCGACAGCGATCGAACGGATTTCATGCCGAGGCGCTTGGCGACC +TCGTCTCGACTGAGGTACCGAGGCACCGGGGGTGCCTCGTGGGTCTGATCAGTCATCGTGCAAACCTCCC +CGTTGTCTGTTTCCCGACAATGGTAAGCGTGATCGGTGCATCGGCATCGGTCTCAATTGCGGCGCGTCCG +GCAACGAGGCGGTCCCATTCGTCTTGAGGTTCGCCAGAGTTGCTGACAGCGATGGTAATGCCACCGGATG +CAATTGCCTCGGCAATGACCTTATCGACCTGATTAGCGAACTCATCGCTAAGCGCGTCAAGTTTGCTGAT +GAGACCGGGAGGGCATTTATCACGTCCTGTCTCGATGTGCTGATAGTCACGTCGGTCCATACCGAGCCGG +GCGGCAAGGTCACGCTGGCTGAGGCCGAGGTACATCCTGTGTGCGCGCACGAGCTCGCCGAGGCCCTCGG +TGTAGGTCGGCGGGTAGACCTCGACGGGTGCCTCGGCGGCGGTCTCGGTGGTGGTCATGGGGTGTATCTC +CTTGGTGTTGTTGTGGTGGTGAAACAGTGCGATGGGTTGGCGATCTCGTCCACGGCGCGGACGATGGTGA +CGATGGACAGGATGAGCAGGAACGCGCCGAGGCTGGCGAACCAAGCGGGGGCGAACCATCCGCCCCACGG +GTTCGGGTCGTCGTAGTAGACGGTCCAGCCGAGCGCGGTGAGCGAGGCGAATACCGCCGCGCTCACCAGC +AGGGTCCGCATCAGCTCACCACCCGGCCTTGGCAGCGCAGATCGGGCCGATACCGCGCTCGCGGCTCTCG +TCGTTGGTCAGGGTTCGACCGCAGACACCGCATTCGCCGATCTCGTGGCCGTAGCGGGCGCTCGCGGCCT +CGGCACCGACCTCGGCGATGCGGCGCAGGATCGTGCCGCCCTGCTTCTGGCTGAGGCGGCGTTCCTCGTC +GCCGACAATCTGCTTGACGAACACGTACCCGGCCCAACGTCCTTCGGTGGGGCGGTCGACTTTGTAGAAC +GCGGTGCCGTTGATCGCGTGGATCGTGGTGTCGATGGCGTAGCGACCGGCGGGTACCTCGTCGGCACCGG +CGACGATCTCGCGCTCGGGCGCGTAACCCTCGGCCTTGAGCCGGTCGATGTTCGCGCTGACCGTGGCGGA +ATCGTTGTGGGTTTCGAGCCACGCCATGATCTTGACGGCGGCCTCGGCGGTGTCGGCACCGGTGGCCTTG +GCCTTGGCCATCATCAATTTCGTCAGCAGCGCGATGGCGGCGTCAGAGGCGGTGGCGGGGGCGATGGTGT +CGAACGGGGTGGCCATGATCTTGAATCCTTCCTCGGCGGGTCGATCCCGCCTGACAAGAGATAAGTTACC +CGCCCTTGTCGGATTTGTCAACACGTTCATCGGGCACGTCTACCGTGACACGTATGCCGAGGTACATGCC +CATGCGCGCGGGCACCTACACACTGTCGTCCGGTTTCGGCCCGAGGTGGGGCAGCCAGCACCGTGGCCTC +GATTTCGCCGCCAAGGACGGCACACCGATCTACGCCGCGCAGGGCGGCACCGTGGCCTACATCGGCAGAG +CCGACGGTTTCGGGCAATGGATCGTGATCGACCACCCCGCCGCCGACGGTGGCGGCACGACCGTCTACGG +ACACATGTGGGACGCGTTCGCCACCGGACTACGTCAAGGGCAGCGCGTCGAGGCCGGGCAGCTCATCGCC +TACGTCGGCACGAACGGTCAATCGACCGGCCCACATCTGCACTTCGAGGTGCACCCCACCGTGTGGCGGC +AGGGGTCGCAGATCGATCCCAAGCCGTGGCTGGCCAACGCGCGCAACCCCGGCGACCCCGCCCCGGCACC +CGCACCACCCAAGGGAGGAACATTGGCCAAGCTGACAGATCCGTTCACCGGCGAGCTGTGGTCGCCGAAC +CGCTACCACCCGCGCGGTCTCGGCGACCCCAGGTGGATCGTGGTGCACACCCAAGAGGGCGGTCGTACGG +CGCGCGACCTCGCGGCCTACCTCGCGCAGAAGTCGAGCCAGGTCTCGTATCACGTCGTGGTCGATGACCG +CGAGGTGCTCAAGGTCGTCGCCGAGGGCGATGCACCGTGGGCGGCAGCGGGCGCGAACAAGTACGCGTTC +CACATCTGCATGGCTGGTAGCTACGCGTCGTGGTCGCGCAACAAATGGCTCGATGTCGACACGTCCGACG +GCAAGAACGAGGACTTGCAGCTCACCAAGACCGCGCACGTCATCGCATGGTGGTGCGACAAGTACGGCAT +CCCGCCGGTATGGATCGGTGGCCGCAACATCCCGCCGTGGGGCCTCGACGGCGTGTGCGGACACGTCGAC +CTCGGCGCGTGGGGCGGTGGCCACACCGACCCCGGCCCCAATTTCCCGCGCGACGAGCTCATGCGACGCG +TGAACCAATTCCTCGCGGGCACCGAGCTGCCGCCGCTGCCGACACCGCCGCCGGTCACCGTGCCGGGCAC +CAAGCCCGATCAGTACGGCGATTGGATGCTCTACCGAGGCAACCCGCGCAACGATGCCGACCGTGTCCGG +CGCGTGCAGCGGCGGCTCAAGGCGGCGTATCGGTCCTACGCGGGTCACCTCGAAATCGACGGCGATTTCG +GCCCGCTCACCGAGCTCGCCGTGCGCGAGTTCCAGCGTCGCAGCCTGCTCATCGCCGACGGCATCGTTGG +CCCCAACACCGCAGCCGCCCTCAAACCGTAGGAGATCCGCATGACACAGCCCACCGCATGGCAGCCGCCG +CAGAATGTCGGCGATGTCGCCGTGACTGTCGCTCAGGCCAAGGCCAAGCTCAAGGTGTTCAGCTACGGCG +CGGCGTTCAAGACCGAAACGTCGAACGTCTACACCGCCGAGTTCGGCACGGCGCTGCGCACGTTCCAGCA +GCGGCGTAATGCCGAGATCCACGAGGGCAAGAAACCCGGCCCCGTGATGAACACCGATGGCGTGCTCGAC +TGGGCCACCAAGAAACAGCTCGGCATCCTGCCCGAGCAGACCGCCCCGGCACCACCGCCGATACCGGCCA +ACCGGGCGGCGGCGCTGGTGTTCCGAGGCACCGGCGGCATCATCGGTCAGGATTACGTGAGCCAGGTCTG +CCAGCAGGTCGGCCCGATGGTCGAGGAAATCAACCCCGAGTTCCCCGCCTCGATGGGCGGGCTGCCGCCG +GGCGCGCCGAACCTACCGAGCGCACGCCAGGCCATCGATATCGGCTACCGGTCCGGCGCGGCGTGGATCA +AGGCAAACCCCTCGCGCAAGTTCGTGCTCGGCGGCTACAGCCTCGGCGAGATCGTGGTGGCCAAGCTGCT +CACCGCGCTCTTCTCGCCGGGCGGCGAGCTCGCGGCGTTCCGCGACAACTACGTGTGTTCGTTCCACATC +GGCCCACCGGCCCGCCCGCTCGGCGGTGCGTTCTACGGCGGCACCGCCGCGCCCGGCGTCGGAATCGCGA +GCAACAGACTCGCCGCCGACATCTACGCGCAGCTCGGCCCGAGGGCGTGCTATCTGTGCGACCCCGAAGA +CATGTACGGGTCGATCCCCGTACCCGTCGAGGGCGGCACCGGCGACATCATGGAAACCGTCTACGACATG +GTCACGACACTGGCCCTCAACGACTTCCTCAACACGGCGGCGGCCATGCTGCCGCACATTCTGGAGATCG +CACAGGACGCGGGCATTTTCGGGCTGCTCGGCCTCGGCGGCGTCGGACCGGCGACCGGCGGCGGCGGTCT +GCTCGGGGGTCTGCTCGGCGGCGGCCTCGGCGGTCTGCTCGGCGGCGGCATGGCCAACCCGGCGGCGCTG +CTGACCAACCCGCTCGCGGCCATCCCGCTGCTGCTGCCGTTGTTCACCTCGGCGCTGCCCGGTCTCATCG +CAGGCGTCGGCGGGCCGGGCACCGGAGGGGCGATCACCGGACCGGCGGCGGCGGCTCAGGCGGCCATCCT +CGGAATGAAATTCCTGTTCGCTGGCACGCGACCGCACATCGAGTACCACATCCGCGAGGTATGGCCCGGT +CAGACCTATATCGGTCTCGCCGTGCAGCACGTCCGAGATTGGGTAGGGCGAGAGCTGCCCGCGTAGCGGT +ATAGTGTGCGCGGCGGTCCCTTCGTGGTTGTTGGTACCCAACAGCAAGGCCGGTGTCCTTTGGGGTGGGA +CACCGGCCTTGACTGCGTTACAGGGCGTTACGCCTTGGCGAGCGACTGCCGCCATTGCTGGTAGAGCGCC +TTGTCTTCCTCGCTGCCCGCAACGAGGATGAACGGTGCCGACTGATTCGGCTTCTTGACACCCTTGACGA +TCCGTCCCAGCACCCACGCGATGCCACGGTCGAGGGCCTTTTTGCCCTCACGCGCGAGCGCCTTGTTGAA +CACCATGATGTCGTCAAGACGCTCGCCGACCTCATACGGCTCGCATTCCTCGACCTCGCCGTCCCGGTTG +AGGAAACCGAAGTTGCCGTTGTCCAGCCGAACAGCGGTCTGTGCGTTCGGCGTTCCCGGCTCGGGCACAG +TGAGCGGGATGATGTCGAACCGCACGAACTCGGATTCTTTGCCGTCCTTGTCGTGCACGGTGGCCATCGT +GCCGTACTCGGTGGGGTGCATGAGCACCAACTGCCCGAGGAAGAACGCGGGCTTGTAGCCGCTGACCCCG +GTGGGATCGGCGGCGGCGTACGGGTCGGACCCAACGGGCTTGTCCTCACCGACGTTGGTCACGTCGGGGA +GGTCGGCGGGGTCGACCTTGCCCTTGCTCGGGGCGGGCGCAGCGGCGGTGGCAGCGCCACCCTTGCCTGA +AAACGGGCTACCGGCCATGGTGGTGATCTCCTTTGTGTTTGTGGTGATGGTGGTGTTGTGGACTGCTCTG +AGGTCTCAGAGCAGCTTTGCAACCGTGGTCGCAAAGTCAGTGAGGGTGTCGTCCCAGACATCCTGATATT +CGGTGTAGACCCGCTCGCCGTCCTCGGCTGACTCGATAGCGGTCAGAGCGAGGCGTGCCTCGGCGTAGCG +CACGGACTGCTTGGTCGGTGCGGGCACGGCGTGCCGGGGCACCTCGGTCTTGGCCCGGCTGCGCATACGG +CGGGTGGTCGAGGATTCGATGAGCGCCTCGGCACCCCACACCGCATCAATCGTGATCGCCGAGGCTTTCT +CGGGGTGATCGCTCGGCACGTGCACGAGGATGGCGAACGGGTCGCGCTCGTCGTCAGTCTCGTCGTCGGG +GTGCGGGATGCCGATGAGCTTGGGCATACGTTCCCAACCGTCACCGGTAGCGTTGAGCACGTGCGGTGCC +CACGAGTACACCCCGCCGATCTGGACTGCGAACGCGAGCCACGAATACTGCATGCTGTCGACCTTGGTGG +TCTTGACATCGCCGAGGACGAGCTCTCCGGTCGAGACGATCTGGAAAATTCGGTCGATCTTGCCCGCTGT +CGCTTCCTCGCCGAGGTCATTGAGCACGGTGCGTTCGACGTACTCCGGTAGCGCCACCAGACCTCGGTGT +GCGAGCACCCGGCGGTAGGCCAGCGCGTGAGGCCGGACCATGTCGGGTAGATCGCGCATCAGCACCATCC +CGAGGTCGAGCGCTTCCAGCCACGCGTGCACGCACTCGCCGAACTCGCGTGCCTCGGCCCCGCCGGTCAC +GTTGTCGATGGTCTCGATGACCCGGTCGATCTCGCCCACCTTGGTGTGGTTGGCGTAGACATCGCGCAAC +TCGGCGAGCAGGGCGGCGGCGGTGTAGTCCCCGCCCGGTCCCATCGGCTCGTTGGCCTTGCCGGTGATCT +CGCGGTGCACGAGGTTGAGCACGGCACCGACGACGTTGCGGCGGTTCCACTTGGCCAGCCCGGTGGTGTC +TTCGAGTGTCTTGGCCACCGTGGTGGCGCGCGGGTACGAGGTCAGTCGGCCCGTCTCGGGGTGCGGCAAC +ATGTACTGGTGCCGACCGTTGTATTTCGACGGCGGGCGCGGCGGTTCGGGCGGCAGCGGATAGCGCATCC +ACTCGGTGTGCCGGGGGTAGAAATCGCCATTCTCATCGATTGCCGTCATGGTGGCTGTCTCCTGTCGTGG +TTGTTGGGTTTCGTGCGTCTGCGGGGCTGTGGGGCCGGTCGGCGGGGCATCGGGGTCGCCGTCCCACGTG +AGCACCGAACCGTCCGGCGCGATCACGACGCGGTAGTCGGCGTGCGCCTGCTCGGGGTCAGCGGTCCATC +CGTCGCCGTCGGCGGGACGAAACCACACCGAGCCGTCTGGCTGCCGCCACGCGGGGAACTGCCGTGCCAC +GCTCACCACGGTTACGCCTCGATCCTTGTGATGTTGCGGTCGAGGCGGCGGCTGATCTTCACCGTCGAGA +TCTCGTCGCTGAGCTCGGCCTTGGTCATGGCGTCGGCACCGACGATGCCGAGGCCCCGTGCGAAAGCAAT +CTGCTTGGCGGTGGCGGGCTGCTTGCGCCGCCATGAGTCTTTCTTGCTCGGCAGCCGTTTACCGCTGTTG +ACGATCCACGCCTCGGCGGCCTCGCAGGCAGCCTCGAGCCCGATGTAATCCGGCTCGGCGACACCGACCG +CGCCGGACCCCGATACCCAACCACCGCGCCCGGTCTTGGTGCTCATGGTGGCCACCGCCCACGACGTGGC +ATTGGCGTCGAGCGGACGGTACCCGTCCTTGGGCCACACGAACACGATCTCGTCATCCATGAGCGGGATG +AACGGGACACCGAGGGTGGTCTCGCACCACGTCACATCGCTGCCGGACAGCAGATCAATCACGACCATCT +CGACCGGTCCCTGCCGAACGAGCTTGGGTGTCGGGTCGCTGCCCTCGCCGGGCAGCTCGTCGTCGGGTTC +GATCTCGATGACCGAGCCGTCCTCGGTGACCTCGGCGGCGTCGACACCGGGCACGAGTTGCGTGAGGTTG +ACGAGTTTCATCGAGCGCGATGACCCGGCGAGGTCGAGCACGAGGGCATCCTGCTTGCCGTCGTACAGAC +GCAGCGCGCGACCGATCATCTGCGAGTACAGGTTGCGGCTGCGGGTGGGCCGGGCCAGCACGACGCAGTC +ACACATCGGGAAATCGGCACCCTCGGTGAGCACCTGCACAGTGACCAGCGCCTTGGCGGTGCCGTTTCGG +TACGCCTCGTATACGGGCTGGCGCTCGGCGTAGCTCATCGAGCCGGTGACGGCGACGGCGGGGAAATCGG +CGGCGGTCAGTGCGTCGGCGATGTGGTGGGCGGCGTCGACCGACGCGGCGAAGATGATCGGGCGGCGGTC +GGCGGCGTGCAGCTTGATCGCGTCGACCACGTACTCGGTGGCGGCTTCCATGACCTCGGCGAGGTCGGAC +TGCTGGAAATCACCGGCGACGGTGCGCACGTCATCGAGGGCGTTGAGGTTATTGATGCGGACGGTCAGAC +CGCGCGGGCGCACGAGGTAGCCAGAGTCGATGGCCCAGAGAATGTCTTTCTCGTAAACGACTTTCTCAAT +CACGTCGCCGAGGCCGACCGGGCTTTTGCCCTTGTCGTCGCGACGCATCGTGGCGGTAAATCCTGCGAAC +AGTGCGTCGGTGTATCCGCCGAGCTCTGTGAACGTCGTGTGGAAACCCTCGGCCCCGGCGTGATGCACCT +CGTCCCAGAGAATGCGTCGACGGAAACCGACAGCCTCGCGGCGGTGCGCGGTGGCCAACGTCTGCAAGGT +CGCAACCACGATGGGCGCGCTGTGGTCATCCATTTCGGCGCGCACGATGCCGACGTGGCTGCGCGGGATC +GAGGGGTCGACCTCGAAAATGGTACCCGCCATCTGGTCGATGAGCTCACCACGGTGAGCGACCATGAGGA +CGGGCTCGCGGTTCTGGTATCCGTTGACGGCAGTGCGACCGATGACCGTCGACTTGCCGGTGCCGGTGGG +GAGAACGACACCCACGCGGGTCACGCCTTGCGACCAGTGAGACTCGATGGCCTCGACTGCTTCGCGCTGG +TACGGGCGCAATTCCCGAGGCGCTGGTGATGTCATGTTGGGGTGCTTTCGGTGTGGTGTTGGTGTGTTGC +TGGTGTGTTGCTGGTGTTGGTGTGGTGACCGGCTGACCTGAGCGGGCTTGGGTTTCACTCAGGTCAACCG +GTCCATCAGAACTTACCCGAACTGTTCGGGGTTGTCTACCCCTGCGGCGCGGACGACCATTGAATACGTC +CATCCGCAACCGGGTGGACTGTCCTGCGGACGCTCACCATGCCGCGAGGCAGCGTTACCGCAGCGAACGC +ACTGAACGGCACGGTCTTCCGCAGCACCCATTAACGCTTCGAATGCCACAAACCTCGGATTGCCAGTCAC +GCTTTCCCCCTCGGCCAGTCCGCTGCGATCACTGATCGCGGAAATGCGGGTGATTGTGCAGCCCGAGTCC +GCCAACGCGCTCACGAATCGCTCGCAGGCGATGATCCTGTGGAGGCGCGCTAAATCCTCGGGCGGCGTAA +GTCCCGCCGCGTCCAGCAGCATCGCCCCTAAAAGCTCCTTGTTTTCATACGGATCTCGCCCACTCACTTG +TCTTCCCCCTCGGCTACAACAGCAACAACGGGCACCTGATGGGGCTCGATACTGCCGTCGTCACGGACGT +AGCGGTTGGTCCCGCACGCAGAGCACTTGGCGACCTCGCCGTCGCGGCTTAGAGTGCCGGGCTTGAACGG +TTTCCCCGATCCCGCGCAGTCCTGCATCGTCACAGCCTCAGTGGTGTAGATGAGCTTGGCGGTGTCGCAT +GGCCAATCGACCTCGCAGTGTTCGCATACGCGGTCTCTGAGTCGGCAGTCCTCTCCGCTGCAGCAGGCGT +TGATGCAGTTGCTCCACCGAGGGCGGTGCAGTTCGCGGATCGGCTTCAACGCCTCACGGGCGGCGGTGAT +CAGGTCATGGTTGGGCCTTGGGTAGTCGTGCACTTTCCTCATCGCCCGTTGTGCGGCTTCTACTGCTGGA +TCGTTCATTGTTCCCCTACCCGTCGTCGGTGAGCTCGGTGTTCTGGATCTTGACACGGGCATAGGTCTCC +CGTTCGGCCTCGTCGGCCTGCTCGTCAGGCCGACGACCATGGGCTTTCCAGAACCCGCGCGCTGCCGAGG +CGTACTCGGCTCGGATGAGCTCGGCACCTTGCATCTTGGTGCTCGCGCCGATGACCCGACTGCCGTCGGT +GACGACGTAGATGTCGCTCATTCCTCGGTTCCCGTCTTGAGTCGCTCGGCTTTTTCCTCCATGAGCTTGG +CCAGCGTCGACAGCGGCATACGCGTCCACCACGCTGCGGGCAGCGCACCCGTGCTCGGGACACCGTCGCC +GATGAGCTGCCGGGCGGTGTCGAGGTCGACGGCGGTCATCCGAGCCACCCCGGCTCGTCGTGGAGCACCG +TGGCGTCGGTGACCGGGTAGACCTCGATGCCCTTGCTGCGTCGAGGCACCACATATCCGCCATCGCACGA +GGTGGACGCGTAGTACCGACTGCCATAGGTCGTGCACGTGAACGGTTTGTAGTAGCTCGGCGACCAGAAC +TCTCGATAGCGCGTCCATGAGCCGTCTGACCGGCGCGGTGTGTCGCACAGGGTCCGAGTGTTCGATCCCC +ACAAGATCCACATCTCGGTCTTGCAACCGGCGATCTCGTCAGCCGAGGCGGCGGGCGCGGCGGCCAAGGC +ACCGGCTGCCGTCATCGCGACGGCAGCCAGTGCGGCCTTGAGACGGCGGCTCACCACGGGTCGGTGGTTT +CGACGTGGTGACGACCGCCACCGCAGCGGATATTGCAGCGCTTGACGGTCTTGAGACCGTCCTCGGTCAT +GACTTTCTTGGGGGTGCCGTCCTCGTTGTAGACGGTCACCCACACCGCCGAGCCGCCGTTGCCGGACGCA +CACGCGTGCTTGTAGATCTGGCCCTTGCCGAAACCGTGGTTGTCGCACATCTTGGGCGCGGCGTCGGCGA +TCCCGGCAGGCGCGAGCGCCACACCGAGCGCGGCGGCTGCCGCGATGATCAGGGTTTTCATGGGTGGTCT +CCCTTGTGGTTGTTGGTGGTTGGTTACTGCTGGTGATGAACTTACCCGCATGTGTCGGGTAAGTCAACTA +GTCCCCGTCGAGCACCTCGGCGGCAGCCAGCGTGCGAGCCTGTTCGATCCCGACCTGACCGGGGATGTAA +CGGCTGCCCGAGGCGGTGCCGGTGAGCGTCTCAATGAGGTTGAACCGCAGACCCATATCCACCGCACGCT +GAATGTGCAGCTTCCAGTATGCGGCGCTGTCCCGACGGCTGCGGGCATACGCGTCGGCCTGCACGGCGGT +GAGGATCTCCGCGCGCGTGGCACCCTGTGTCGGCAGATCGTTGACGAACTGCCGGATGCGCACGGCGGTC +TCGATGACAGCCTCGGGGCGGGGTCGAGCGAGCACGATTTCTCCCTGCATCGGGTCGACGCTGCCGTTGG +GGCCGGTGATCAGCGGGGCCTTGATCCCGGCGTCCTCGTCCTCGTAGCTGACCATCATCAGTGCGATGGC +TTCCTCGAGTGCCTCGGCGTTCTTCTGCTTGGTGGTCATGACTTCCATCGGGCGGCCCGGCAGGCGTCCA +TCGATCATCGCCGCCTCGGTGTCCCACGTCGCCGCCCGCACCACGAGCTCGCTGTCGAGGGCACCGTTGA +GCGCCGACGAACCTCGACCCTGATCGGTGTGCGCCTTACTCGTGTGGTGCACTACGCACACACCAGCGCC +GGTGAGCTCACGCAGCTTGTCGTAGCGCCGGACCGCGACACCGACATCGGTGGCGCTGTTCTCTTCAAGA +CCCGACGACTGACGCGCGAACGTATCGAAGACTACGAGGCCGATCTCGTGCCGGGCGATGTACGCGGCCA +GGTCACCCCATGCCTCGTTACTGGCCTTGATCAGGATGATCGAGTCGCCGAGCATCAGATCGTTGCCAAG +GTCGACATCGTGATTGGCTTCCCACGCGCGCAACCGTTGCACAGCACCGGCGAGACCCTCACCGGGCAGA +TACAGCACCTTGGTCTTGAGGGTGCGGCGGCCCTGCCACCGCTTGCCCACCGCGATGTGGCAGGCCATGT +CGAGCACCACCGACGACTTACCGATGCCGGGCGGCCCGATGATCGAGGTCAGACCGCCGTGCTCAAGCAG +ACCCTCGATGACGTACTCGGGCGGCGGCATATCGCGCCAATGCGAGAACGGGGCGATGCGCGGCACACCG +CTGTGCGGACTGTCGTAGACATCGGGGTCGGTCTGCTCGGGGTCGGCGGCGGCGTACGGCGATGGTTCGT +CGGCGGGCTCGGACTTGCCGAAATCGGCGGGCAGATCGCTGAGGTCGTCGGCGTAGCTCGTCGGCGTCGG +GCACGGCTCAGGACCGGCGGTGTGCGCGGTGTTGCACGAGGCGTCGTGCGCGTAGGTGGCGGGCGCGGGC +TCGGCGAGGTGCTCATCTCCTGCGCTGCCGCCCTCGGGCATGTGCCACATGTCGCCGTCGCCGTCGACCA +CGAACGCACCGGCCTCGGTGTCGCACACCGGGCAGAACGGTGGATCGTCAGGCGTCGAGGTCGTCTCAGG +CTCGGGCGCGGCGGGCAGCTCGAAATCACCGTCGCCGGTCATCGCGTGATCGGGGTCGACCTGCTTGGGT +GCGATGTCGGGGTCGACCTCGACTGCCAAATCCGGCGTCAGCCCGATCTCGTCCATGGCCTTGCCGATGT +TGCCCCCGTAGTCGATGAGCGCAACGGCTTGCAGCTTGCTGAGGGTCTTGGTGCCCTTGTCGGAGATCCA +CGCCTCAAATGGCTCGCCGGGGTTGTCAGTCCAGATGTGCAGCGGTGCGTTGACCTCGGTGTACCGGCCC +AGTGCGCAGCCCGTGTCGTGTGCGGTGGCGCTCTTGGGTGAGGCGTGGACACCGGGTGCGGTCCAGACCG +GGCATCCACACGCATCAGCGCGCGGTGCGGGGGTCCAACCGAGCGGCTCAAGAATCGCTGTCCACGAGGT +GGCCTCGGCCCACCCGTCGATGGCCTCGCTGAGCTCACCGGCCTCGCTGTCGCGGTGCACACGCTCGGTG +CGCTCGGCCCGGCGCGCGGCGGCCTCACGGATCGCGTCGAGCAGCCACGGTGCGTCGGTGATCGGGTAGT +CGCGACCGACCACCTCATAGGAACCCTCGGGGCGGGTCGACGGCGGGATGAGCACATACCGCTTGTCCCA +CAGGACTGCGAAACCGTCCTCGCCGCCCCACGTCATCGCGCCGAGGTTGCGCGGCAGCGTCGGGAGGATC +TCGTCGGGCACGGTGAAGTAGAAATGCCCGCCGTCGCTGTGTGCCCACGAGCTCGGATCGGCGAGGTCGC +CGTCCTTGCCCGCGTGGCCGGGCGTCACCACGGTGGGCGGTGGCACGTCGTCAATGTCGATGTCCTCGGG +CAGTGCGGCCTCGTAGAACCTCTGTAGCTGGGCAGAAGTGTCGCAGTCGACCACGACCAGCGCCGAGCCG +CCGACCTCGACGGCGACGTTGACCGGCACACCCTCGGGGTACCGCTCGGCGTACAGGGTCAGGTACCGCT +TCAGATAACGGTCGAGCACTGCCTTGTCCGAGGTGGCCAGCGTGAGACCGGCTGGCGATTTGACTCGCGC +CCAGTCCCGTCTACCGGCCTCACGCGCGGCTTCCTGCGCCGCCTTGTCGTCGGCTTTCTTCTTCTGCGGG +GTGCGCAGGTCGACGGGTGCCTTGGAATCGGGCGCGATGAACATGATCGACAGCCCGATATCGGCGGCCT +GACGCACAAACGCGCGCACCGCCTCGGGGTCGCGGTTGTCGATGCCAGCGCCGAGGATGGCGGTGATGGG +GGTTGATCCGAGCACGGTTGGTTCCTGTTCGTTTCGTGGTTGTTGGTGTGCGGAATGGCGAGCGTAGCGG +GTCATTTCGAGCCCGTCGAGGCACCGCTATGGGTGGGGTGCGACGACTCGACGGGACCCTACATCTAGGG +TCTCTCGCTGGTCTGCTGGCGAGATCTGACGGTGCGGACGGCGGCGAGAAGATCGGCTGCAAACACCTCG +GCCTCGGTGACGGTGAGCGTATAGGTGTCCTCGCTGCGGGGGCCGATCTCGATGGAGTCACTTTTGACCC +GAATGAAGGGGTAGTGGTTCCACACCGGCTCATCCTCGTCGTACTCCGAGGGCCGGGCAGGCAACAGCAC +GGGGAATCGAGACGACAAGTGCCCCGCCGGTACCGCCGGGGCGGCCCCGCACTTGACGCACGTGAGCCAC +CCGTCTCCGGTGTTGCCGTAGTGGTGCTGGCAGCTCTCAGTCATGGCCGCCCCGCCTTGTGCCGGATCAT +GACCCCGGCCCCGAGCGCGATGTAGGCGACCGCGTCGACCCATGAGTCGTGATGGTCCGGTGTCTCGCGC +AGCCGGGCGATCTTGAGCGTGGCGAGGCAGAGCGCGACCTGCTCGGCGGTCACCTCGGTGCCCAGGATTG +CCGACCACATCGAGGCGGTCACCCCGAAATTGTCGGCGGCGTCGCCGTAGGTGGCCTGCCTCGGCCCGTT +GAACAGTCCGATGGCCTCACGGGCGATGTCCTCGGCGATCTGCCCGGTGGGCGGGATCTCGGGTCGCTCG +GGAGCGCCCTTGAGCTGCGGCGGGGTGCCGATGCCGACGGTGCCCGGTTTGGGCCGCTCGACGGGCACCT +CGGTGGGCTGCTGGCCCCGTATGGTGTGGAGCTCGTCGGCGCAGCGGCACGCCCGGTCATCGGTCTTGTG +CGGGCATCCGTGCATCGGGCAGTATCCGCCCACATCACACGTGCACGGTTTGATGGTCATACCTTCAGCC +ATTGCGATCCCATATCTGCTCGGTCGGTGCGCAGCACCGGGGTGCGTTCGGCCCACCTGATCAAGAACTC +AGGCGGGGTGGACATGATCTCTTGGACCTCGGCGGACACCGTGGTGGCCACCACAACCTCGTCATGCATG +GCGAGGTAGAGCTCGTCGGTCATTCCGCGCCGGTACATCTCCACGATGGTGTTCGCGAGCACGTCATAGG +CGCTGCCCTGAACCGTGTAGTTGACCGCCTTGAAAATCCCACCCTCGTCAACCGGCAGGATGCGGCCACC +GGCGGTGATCACTCGACCGAACTGGGCGGCGACTTCCTCGACCTTGACCATCCACCGCGCCGAGCGTTTC +ATCGCAGCGAACATCTGCCGTTTGATCTGCGCAGTCGACTCTTCGGTGTGCCCGATGAGTGCGGCGAGCT +TGGCGATGCCCGAGCCGTACATCGAGCTCAGAAGCACCACCTTGGATGTCGGGCGGTCGATCCCGGCGGC +CAGCATGATCGGGCCGTAGAGATCCTCCCCGGCCTCGTAGGCGGCGAGGAAATCGATGTCGCGCGCCATG +TTGGCCATCGTGACCGGCTCGATCTGTGACCAGTCAATCGAGGTGAGACCGTCGCCGTCGTCGCACAGGA +TCGGACGGGCGTCCTTGGGGAACTGCTGCAACTCGGGGCTGCCGTAGCTCATGCGGCCCGTCGCGCTGGC +CCCGAGGGTGGCCACCTGAGGGTGGCACCGGCCAGTGACGGCGGCCTGAGTGCTGACCTTGGTGAGGTAG +CCGAGGGTCTTCTCGATGTTGGCCAGCGAGAGTTGCGCCTTGGCCAACGGGTTGATCTCGGCCAGATGCT +CGAGGTCGGCCTTGGTGGCACGCAGCGCACCCTTGGGAGTGCGCGGCCACGGCTGCGGCAGCTCGCCCAG +ATCGGCGAGATACTGGACCAGCTTGGCCCCTTTGCCGGTACCGCCGTCGAGACCGTGGGCGGCGAGGTCG +GCGGTGTAGATCGCTTGCTCGTCGGCGACCTGATTGAGGTAGCGGTCCAGATATTCGCGGTCGACGTTGA +TGCCGAGCGCCGAGCGCCACAACATCATGCGGTGCACGATTTCCTGCGTGTCGAGGATGGCCTCGGCCTC +GGCCACCGTGGTGGCCCCGTACACCTCAAACGGATGGTCCAAGGTCCACGCAATGCACATCCGGCGTAGC +ACCGGTTCGAGGGCGAGGGTGGCCACGGTGTCGGCCATTGCACCCTGCCGGTAGATCGGACTGTCGATGT +CCATGCCCTCGTACCCGGCCTGCTGTGTCTTGTACCCGGCGGCCTTGAATGCGCGCTCCATGCCGCCCTT +GAACTCGGACAGTCCGAGATAGCGGATGGCCAGTGCGGTGAGGGTCTTGGCCTGACCATATGGCGGCGGC +TCGGGCAGCGCGAACCTCGCGAGCACGACCGTGTCCACGATGCGTTTCTTGATCACCTCACCGGTCATCA +GCCCGGCGTGGAACAGCGGAGGGATGTCGAACGGCGCGTTGTGCAGGATCACCGGCCCGGCCAGCGCGTA +CATCTCGGCGACGAGCGCGGCGTGCTGTTCGTCGCGGCGGGGGTCGAGCAGAATCGAGTGCACGCGGCCC +GAGTCGGGCTGAGCCCACGACATCGTCACGCAATTGATCGTGAACGAGTTGTCGAGACCGGGGGTCTCGA +TGTCGGTGGCCAGCGCGATGGTGTTCGCCGGTCCCCGCGTGGCCTGCCGGACGAACTCGGCACCGAAACG +CACCGCGTGCTCGCCGGTGTGCATGCGGGCACCGAGCAGCGGATCGGACCACGAGCGTGAAGGCACCCGC +ACAGGGGCGTCGATGATGTCGGTCATGCTGTGGCACCGCCCTGTAGCCATGTGACGAACGTGTCGGCGAG +CTCGGTGACGAGATCAGCGGGGACACGTGAGTTGATGTTGATGGCGTCGGCGGCCCGCACGCGGGCGGCG +GCGTTGAGGGCGGCCATACGCACCGACATCGGTGATTGGGCAGGCGGTGCCGACGGGGTGACGACACGGT +CGTCGTTGCCCGGCCCAATGCGCACGACGCGCGCCGGGTAGTTGATCCCGGCCTGCTCGGGGGTGAGCGG +CTGCCACCCGCCCGGCGAGATCCACACGACGCGCTCGCACAGGCACTCGTTGAACTTGCACGCGATGGCA +TGCGTTTCCGCACCCTCGATCTCGCTGCGCACGTAGGCCACCGCCGTGCTGGTCTCGTCGCCGATGATGC +GCTGCCACTCGATGATCGTGCCGTCGGGCAGCGACTCGAGGTGGTCCTGGTCCCAGATCTCAAGCATCGC +GGGCCTCGGTGATGCAGGGATGGTCCTGCGTCGGTTTGGCGGTCATGGTGGTTGTTCCTTCCTGTGGTTG +TTGGTTGTCGGGGTCTCGGGTCACCACGTGATCCCGCAGTCGCACTCGTGCGCTTGCCGGGCAATGTGCC +CGTCGGTCATCACGCACACGTGGTGCTCGCCGGTGATGTCGCACCACTGCTCGTCGCCGTCGAACACGTC +GTCGGCGAGGTCTTGGCATATCTCGCGGGCCATCACGGCACCTCGATTGTCGGTGTCGAGGCGGGGTCGA +CCGGCCACGGGTCCACGAGCACACCGTCGATGTAGAGACCGGGCGCGTAGCCGTCGACGTTGAGCGGCCC +GCAGATGTGGTTTCCGTGGTATCGGCAGTCGAACGCGGGGTCGTCCTCGGGTGTCGGGGCGGGGTCGGCT +GGCCACATCGCGAACTGCTTGGTGGCCACAGTCTCGGTGACCGACAAGCCGGGGTGTGGGATCGAGACAT +CGGCGGGCAGTCCGGTGAGCACCAGCGCCGCGCCGACGGTCACGCCTAGCGCGGCCCCGGTGGCGACCCC +GGCCAGGAAGGCCGAGCGAATGCGTGGGTCGGTGAACATGGGGTGGATCTCCCTTGTGATCGGTGCGGCG +GGGCGGTCCCGCCGTGGGTTACCAAGATATACCCCGCCGCCCCGGCTCACGTCAATACTTGCCCGACATA +TGTAGGTGTTCTCATTTACCGCCGGGTGCTCGGCCCTCACGGTGGCCGGTCGAGGCCCACCGCTGGCCCC +GCTGCGCTTGGTAGAGCATGGCGAGCTGCCGGGCGGTCTCGGGCGGGATGGTGGCCATGATCGAGCCGAA +CACCGTGCGCTGGTCGTAGCCGCCGAGCGGGCCGGGGTCGGTGGCACCGGACGGTCGGGTCATGAGTTCC +TCGACCATCGCGCAGAACGGGCCGAACGTGTTGGCGTGCTGGGGCCGGGCGGCGGCGTAGCCGAGTGCGA +ACGCGAGCACCGAGGTGGGCGCTTGCGACAACGGGGTCGAGGCGGCGTTGCGCAGCCGGGGTCCGGTGGG +GGCGGGGAGGTTGTCGGTCATGATCCGACGGTAGGTGACCGATGTCGGGTAAGTCGAGCGGGGGACGAAA +CATATTCATGTATACGCATGTCATATCCCCGCCCTCACGCGCGCGTGCGCGCGACAGCGTACCGGCTGAA +CACCCGTTTTGTCAAGGGTCAATTTCAAGATCAATTACCACTACATGGTCCCTCGACCATGGGCTTGACA +AGGTGGGTAGGATCAAAGCCAGCAAGCTTGGCGTTTTCCTAAACCCCCCGTGTCCCCGCCCTCGATGGCC +TCGGTGACCACGTGATGGATGAGTGGGTGAAAACGCGTAATCGACTCAATGCAATGTCAACCGCATTGAC +ATGCCGCACTCGCAAAAGCGCCTCTGGGCGCTACTTTGCTCGCGGTACGTCGGCGCGCCCGACCGAGCTG +CCGTCAGATACGCAGTGTGTTGCAAGATATACTTTTATTGGGTACGGTCCGACTCATGGGAACGAAAACG +TCACCGGTGAGACTCGATATGGACCTCGATGAGTTCACCGGACATGTGAATGATCTGCTCGGGTACGACA +CGCAGCTTGGCGAGAACATCCGCATATGGCGGCGTAGTCGCAATGTCATCAACACGCTCGGCAGCGGCAG +CGCGTTCGCCAAGGAACTCAACGCCGCTATGGATGCGGCGGTGGGCAGCGTCGGGGTCGCCGAGGGTCTG +TGCCATGGAACGAACAAACGGGTGTACCGCAGACACATGGCGGCGCACACCAAGACCGTGGTGCGCAGCG +AGGCGGTGAAGAAGCGGTGCCCCGAGCTGTGGCTCGCCGCACGCAAACCCAGCTTTCGCATGGGGTGCGA +CACCAAGACCTCACCGGTGGCGGTGCCTAAGATCCGGCCCGGCTATGTCGGCAAGCTCATCGAGGCCAAG +ACCGAGGCGGCGCAGCGGCTCAAGTACGCGCGCGAGCACGAGCTGCGCGTCAAGGACCACCTCTACACCA +CGACGTTCGGTATCGAGGCTGAGGATGGGTGGACCGGAAACCCTCCGTATGCGCTCAGCGACGGGTGGGT +GATCGGGTGGACCAAGACGCAGCGTTACAGCGAGGCGCTCGCGAAAGAAAAGGCACCCGAGTTCGGTGTC +GACCTCACCGAGATCACTGAGTATGTGACCGTCCCGCCTCGGGTGTTCTACGTGGTCGGCGACCTCACCG +GCACCGAGGGCAGCCTTGAGGATTACGAGGGAGATGGCTACGCAGACTGAGCAGTGAGGCGGTGGCTCGG +TGCCCTTGTGGCCCGCCTGAGCAATTCTGGTATATCTTCCTAACCATGAGCGCACCGAGCCGACACACAC +GCAGACAGCCCGCCCGTGGACGCCCGGCGGGAGAGGTTCCCAACGTGCTGTGGCCCATCCGAGGGCCGAT +CACCATCCGGCAGCGCATGAAGATCGCATGTGCCGAGGAAGGGCTCACCTACGCGGGGCTGATCGAGAAG +TTTCTAGACGAGCGCGACGAGAAGATCCGGCGGCAGCTTGCCGCACAAAAATCCCCACTACATCGTCCGA +AACGAGAGATATGACCATGACCGAGCTCAACGGCATGACCGATGACGAGCTGCGCGCCCGTCTCGGCCTC +ACGCCCGTCGACCGCCCTCAGACCATGGAAGAGCGCATAGAGGCCCACGTGGTGCGCGCGATGCCCGAGC +CCGAGGACTGCGGCGTGTGCGACGGCAAAGGGTGCGGTGACTGCTATGCATGAGGCGCTGATCTGCAAGC +ACTGCCGCACCGAAGTGTTACCCGCAGGCGACGGCTCGCACCGTCACCGTCGCACCAACAAGAACACCTG +CGAGGTCGAGCCGTACGGCTTCCACGCTGAGCCGATCGGCACGTCATGCGGTGACCACCCGGCGAACTCC +TGCCTCGGCGCGTCCGGCGCGGTGCCGTTGCGAGGACAATCGTTCACATGACCGACACCCCTCAGACCGA +CACCGACGAGGTCGACACCGAGTTCGCCGAGCTCGTCGCAGCCAGCATCGAGGAACGACGGGGCCGGGTG +CTGCTCATGCGCTTGGCCGGATCGACGTTCAAGACCATTGCTCAGGCGGTGGGCGTGTCCATCGGCACCG +TGCGCAAGGACTACGACATCGCGCTCGCGGCCCACCTCGATGACCCGCCGGACATGATGATCGCCCGGCA +GCGCGCGGTGATCACCGACATCATGCGTGCCAACTACCCGTCCATGCTCAACGGCGACAAAGAGGCTGCG +CAGACCATCCTCAAGGCGCTCGACCGCGAGGCCAAACTGTTCGGCCTCGACGCACCCACACGTGTGCTCG +CTCAGGTCAACAACGACGATTTCGCCGTCGAGGCGGCGCGATTGATCGAGCACATCCAGAAGATTGACCC +CAACGAACTCAAGGAGCTGGCCCGTGCCGGACAACCCCAACAACCACGACCCCAAGATCGTCCCGCTGCG +CAGGACACCGCCGTCGGGCCACTCGATGTCGAGCTCGCCGAGGACGGTGCTCAAGGGCCTGAGCGAGATC +CTGCGGGATCTATCGACACGGATACTTCCGAGCCCGATGTCGAGCCCGCTGCCGCCGAGCCCGTCGCCGA +GCCCGATGACGACTACGACGATTGGTCCAACCTCGATGACGACGACATTGCCTGAGACCGGCCCGCTCAT +CAAGCACACCGCCGTGCTCAGCGAGCGCCATCTCGTGGTGGTCGAGCTGCCCGGTCGTCATCACGATGTG +CTGCCCTCAGAGTTCCCCGAGATCGTCAGTGCGTGCGAACGGCGGGCGCGCGCGTGGGCCGATGCGCACG +GGCTTGAGCTCGGCGGCCTCGGGGTCTACTCGTCGGCGTCCTGCGAGGGCAAACCGGCGCGCGTCGGCGT +TTCATTCGAGGTCGTGGTCGGCAAGGGCGAGATCGACGCGGACCAGTCACCGGCGGGACACCTCGACCGT +TGGCGGTGGTCGGGCGGTCGGCTCGACTGGTGGCGCAACGTGTTCAACCGCAGCGATCAGCGCGAGGGCC +GAAAGCTCGTCGCGCGCGAGGAAGGCGACAACATCGTCGCGTTCCCGAGGCTGGCCAGTGTCGATTGGGC +CAGCGCCGCACCCGGCGGTGCGGGCGGTAGTGGCGCGAGCGGGCACACCGGGCAGAAGTCGAGCCAGGTC +TCGTATCACGCTGTGGTGGATGACCGGAACATCGGCGAGCTCGACCGGCCCGAGGACGGTGCCCAGTGAG +CGGCGACCTCGACCGGCAGCGCATGTGGAACAACGGGCATACCGACGACGAGGTGATCGAGGGCACCGGC +GCGGTGTTCGATGCCACGGTGCTCTACGGCCAGACCGACGACGTTGACGAGCACCTCGCCGACGCGGTCG +AGGCCGGGCAGCTCTCCGGTGACCCGGCGGCGGTCGACGCGCGCGACGAGCGCGCGTTCCTCGCCTCGAT +GCGCGACAACACTGACGTGACCGTGCGGACCCTCGGTGTGCTCGGCGTGTGGCTGCTCGACAACGGTGTG +GCGCACCCGTACCGGGGCGCGCGTGCGTTGCTCTCCGAGCTCGCCGAGTACGACCTACAGACGGTCATCG +GTGCCGACCTCGACACGTTCATCGGTGAGATCCAACGGCTACGGGACGAGCGTGGCGGCACCGCATAACT +CGCCGTTTCGGGACCGGTTCGGGCGCTACTGCGGCCCCTTGAAGATCAAGACCGCAGCGGTGCTCGATCC +GGCCCCGTACGACGACCTCGACCATTGCATCGGCTGCCACCGGCATGCCGTCGACGGGCACGAGCACGGC +TGCCCGTACCGGGGCGCGTGGGATTGAGTTCCCCGCCCTCGGCGGGCAACGGGCCTATGCTGTGGCCATG +ACCCCCGAGCCGGTTACCGCCCCGCACATGATCGAGCATCGAGTACGTCTGACCCGTGACGAGGCCGCAG +CGGTCGCCGCTGGCACCTACGTGCCCGGCACGGTGCCCGTCCACGGCGCGCCCACCGTCATCGACAAGCG +CGCGCAGAACGCAGCCCGCGCTCAGGCCGAGCGTGACGCTCAGGCGGCGTGGGTGCGGCAGGCGGCGGGA +ACGCTGTGGTGGTCGGCGCAGCGCGTCAACTCACCGGCCCGGCGCGAGAAGATCCTCGCCAAGCGGTCCG +AGCTCATCACCGGCGCGCTGGCCAAGGGCCTAGACCTCGGCGGGCTGACTGTCTGACATGACCGACACTC +TCGACCGCGACGACCTCGACAGCGACATCGAGGCGGCACCGTTGCCCGCGTGGCAGCCAGACCATAGCCT +CGTGCCCGCCATTCTCGCCGGATCTCCGGTGACCATGAAACAGCTCAGGCCGCCGGATCGTGCGTGGGCG +GTGGCGGTGCTGCGGCGCATGGGCTACACCGCCGAGCTCATCGCCGATTGGCTGTCGTGCTCGCTGCGTC +TGGTGCACACCATCAGTGCCGACAGCGCGGTGCTCGTGCAGCTCTACCTCGACCACACCGAGACCACCGG +CAACGAGCATCGCATGCTCGCCAGCGAGGTGGCCCGGCTCGCCGAGGCGCTCGCTGATGCCGAGGCCGCC +GCCGAGCGCTACAGGCAGCAGCGTGACCGGCTGATCGCCGTCGGCAGCTCCGACAAGGCGACGTTCCCAT +GCGGGTGCCCGCGCACCCGGTACAACACCTACGTCGCGCCCAAGACCGGTAAGGCAGGGTGTCGGCACCA +CCGCACGCTCGCTGTTGCCCGACATCGGGCGCGTAAACGGCAGTCCACGGGGGTAGCCTCAGGCCATGGC +CAGAGCAACGAGGGCGCGGGGCACCCGTAGCCGACTAGGCCGGGGGTCTGCCAGGATCGCACGGTCACGC +CAGCGCGGCACGATCTCGATGTCGGCGGCGACCGGCGGTGTCGGACAGTTCGCGAAACGGGGTGGCCGCC +GGGGCGGGGGCTACCGGGGGTTCAAGAGCAAGAAACAATGGCGGTGGGCGTGGGCCACCAAGCAGCCGTG +GGCGCGCAAGAAGGCTCACGAGACCAAGGGCGGGCCGAAAGTAAGGTATCGGCGGCTGCCCGAGTCCAAG +CACTCGGGCCACCGTGGTCGACGCGGTCCGTCCCGTAAGGGGTGACCGACGTCGGGTCATTACCGGTCAT +TACAAACGAGAAAACCCCGCCGGGGCGCTCGGCGGGGTTTCTCTGTGTCTCAGACGGTGACCACGTCCTC +GAGGCGGGTGAGGTATCCCTCGGCCCAGACGATGGTGGTGCAGATGTACGGGCCGCCCCACGAGGCGGGG +CGCTCACGACGACGGCGGTCGAACTCGGCGACGGTGTAGGTCCGGCCCTCGTGGGTGAGGGTGTCACCGA +CCTTGAGGTCGGCGGCGGCGGCGCGAACGGTGGCGTCAAAGCTCATCATGATCGTTCTTCTCTCTGTAGT +TGTGTGGTGGCGGGTCTCCCGCCCGACAAGAATTAACTTACCCGCCTATGTCGGCATTGTCAAGCAATCC +ATCCTCGGCGCTGCCATGCAGCTCGGGCACCGCCGACGGCGACGAGGATGCCGTTGCGGGTCTCGCGGGT +CTGGCCCGAGGTGTTGTGCTGCCACAGCACGAACGTCTGCCCGGCCTCGACGTACTCGGCGACGGCGGTG +AACTCGGGCACCCACCTGTAGTGCTTGGGGGCGGGGCGGTAGCCGAGAAGTTTCTTGGCGCACTCGACAC +CGGCGCGGCTGCCGTCGTTCATCACGCAGATCCACCGCAGACCGGTGCGTCCGCAGGCCGGGCACTCGCC +GGTGCCCTCGTGGTCCTCGACCTTGACGAGTTCGAGCTTGGCGGCGGTGGCGGTGTTCGCGTTCATGTGG +ACAACAATACCGTAAAAGTCGGTATTGTCAAGCCCGCCCGTCGACAGGACCTCGCGCAACGAAAAACCCC +GCCGGTGAGGGCGGGGTTTTCGGTTTAGGCGAAGTAGGCGTCAATCTCGTCGTTGAGGTCATCGAGGGTG +TGGATGCCGTCGGCGGCGAGCTCGGCCTCAAGGCGGCGGCGGTTCTCGATGGCGGCGGCGCGAGCGTTGC +GAGCGACGAAGTCGTTGAGGTGGGCCATTTCGATCTTCTCTCTGTAGTTGTTTGGCGGGTCTCCCGCCTG +ACAAGAACTAACTTACCCGCCTATGTCGGTGTTGTCAAGTCCTGGCAACTCAACGTCGACAATCGTGACG +TGACCAGCGGAAACGCACTGCTTGCGGATGAGCGCTTGCTGCCGGTTGATCGTGTCGCGCCACTCTTGGA +ATCGGGCAGCTCGCGCGTCACGGACACCCTCGACCGTGTCGTCACGGGCGGCCCCGCTGGCAATCTTGAT +CGACGGGGTGATCCGGTAGTCGAGCCATTTATCTGAGGCGGACTGACACGCCGCGCATAGCTTCACCGCT +CGCCCCTGAGATCCGCCTCGTCCTCGGGGTGGCGGCAGTCGAGGCACAGATAGCGCCCCTCGGCGTCCCA +CGCCATCACCCGGCGCACCGGCGTACGACCACACCCGGCGCACCACTCACGGGCCTCGGGTGCGCGGCTG +TAGGGCAGGGGCCGGTTGTCGCGCGCACGGCGTCGGTCGATGTGGGCCAGCAGCACCCGCGTGAGCGGGT +ACAGGGCACCGGCGACGCTGCCGACGAGTGCCACGGTGCCGACATCGGCGAGGTCGGGCCACATCAGGGA +ACCGCCTCGAAAATGACGTTGACCTTCGCGCCCGGCGACAACGTGCGCACGTACGGCACCCCGTCGTGGG +TGTGCCCGGCAAGGTAGGTCATGCCGTCGACATCGGTACCGCACCACTCGACCACGAGGTAGGCGCGGCG +GCGGTGCGGGATATCGACCCACTCGCCCACGGTCACGGTGTCGGCGCGGCGGCTCACCCCGCTCACGCTG +CTCAGATCACCCATTGGTCACCTCGACCGTTCGGCGTTTCCTGAGTTCTTCTACGCGTTGCAGTGCCCGC +GTAGCGGCGGCCTCGGCCTCGGAGATCTCGGCGTTGAGGGCGTCCTGTTCGGCGGCGATCACCGCGCACA +CGAGCTCGTCGGTGTCGCTGAACATCCGCTCATCGAGCTCAACCTCGACGCGGATGAGCGTTCTCGGCTG +CCCGTTGTAGTTGGTGGTCGGCCCGCAGAAGTCCCGCTCGTGGCGGTGTCCCTTGCGGTCGACGGTGCGC +AGCACCTCGACCTCGACGTGGTCGACGGCATGATCGTTGATGTGCTCGTGGAGCACCGAGGCGACGAGCA +TGCGCACGTTCTGGTCTCGGGCGGTGTGGTTGTTGAATTTCATGGTCGCTAAATTACCCTCACTTGTCGG +GGAACACAACGGTGTTTCAGGCAGCTCGCCGCTTGGCCGCGAGAATGGCCGCCGTCCGGCGCTGGCCTTG +GCGGCACGAGCACAGCAGGCAGTGCGGGGCGATGTTGTCGCGCGTGTAGCGCCCGCCGTCCTCGCCGGGC +ACGATCCGGTCGGCTATGAGGTCTTCAAACTCGCACAGCACTCCGCATTCCCAGCACGGCACGGTCTCGC +CGTCACCACCCCACGCGGCCTCGGGCGAGAGCAGCCACAACTTGCGGGCGCGGCGCGCGTAGCTCGATCC +TCGCTCGGTGCTGTTGCACCGGCCATTGGTGCTGCTCATGCGATCTGTCGGCGGCAGCGGGCGCAGACCT +CGACGTGGCGGCCAGCGCGGGTGATGACGACGGTGGTGATGTGCGGGCACTTGCCCGGCGCGATGGGGCG +GCTCATGCGAATCTCACCACCACAACGTCATCGGTGTTGTCGATGTCGAGTGAGTCCACCGGTGACTCGA +TGTCAACGACGGGACACTGGCCGTAGTCCCCGCCGAGCCACACATCGATCTCAGCGTCGGGGTATTGCTC +GGCGAGCGCGGTGAGCTTGGCGGCAGCGTCGGAGAGTTTCATTGGTCGGTCCCCTCAGTCGATGTGGTCG +AGCTCGTGTGGATAGAAGACCCATTGCGGGTCAAAGCGGTCTGCGCAGATCGCCGGTGACACCGGCCCGT +CGACCCGGCCCACGATCTCGACCGTGATCAACGTGGCGCTGTCGTGTGGGGCCGGATCGATCTCGACCAC +CTTGCCGTAGTGCCCGGCCATCGGGCCGAACTCGGCTGATACCGGATCGTCGTAGCCGTTTTCGGGCCGG +ACGATCACGCGGTGCCCTACGGCAAAGCAGCTCATCCGATCACCCTCGGTGAGTTGTCGCGGCACCCGAT +ACAGATTGGGACACCACCCTTGGTGATTCTCATGTCGGTCTGTCGGCGCAAGGTCTGGCAGCTCGGGCAG +TACCGAGCTTCCCCGTTGCGGATCTGCTTGAGCCCGGCCCGAGGGACCGGCCTGAGCCGGTCCTTGCGGG +CTTTCTCGGGCGCGGTCATCGCACCACGGGGCCGAGCGGTCGGCCCGAGCCAGTGCAGACACCGTGCCCG +TAGGCGCGGTGCGCGGGCACGATCCAGAACTCGCGCCACCGGCCCGACAGCGTGCCTACGGCGGTGTTGT +CGGGGAACGTGCCGACGATCGGCGTGACCTCGTGGCCGCACTGTGCACAATCGACCGGGGTGCCGCGCAG +CGCCACGCCCGGCGCGGTGGCGATCACCATGGTCCACCGCCGATGGCCTTGGCCCATACGCTGCCCACTC +GCACGGGCGGCTCGACCTCGGGCGCGCGGTGGCGGCCCTCGTAGTTCGGTGTCGGTTGCTTGGCGATGAG +CAGGGCACGGGCGTGCCGTCCGGCGCTCATCACCGCGACGAGGTCCGGCACGAGGTTGGGCGTGCTCATC +GCAGTGCCGCCGGTCCGTGCTCGTGGCAGCGCAGCCGCCCGTTGTCCTCGCAGTCGAGGCACGTGGGCGG +GGCCTGCTTGGCCTGCTCGATCTCGATGAGCGTGTCGAGCTTGCGGTTGGTCTCGCGCAGCAGCGAGACC +ATCGTGACCATCGCGTCCTTGAACTCGCCGACGGTCTTGGCCACGTCGAGGCCGCCGAGGTTTGGCATCA +GTCCCATGGGGTGCTTCCTATCAGGGTGGGGTGAAATGTGTTGGGGTGGCGGGGATTCTGTGAACCTTGA +CCGGCTGTCTGCTCAACCGGGGGACGCGGCCCGCCATCGCGTTGCTCGCGGGACCGCTCAGGTCCAGAAT +TGCCCGTCCCAGTAGGTGACCGACACGGTCTCGCCGGGGTTCAGGTACGGCAGCATGGCGACGATGTCGC +GCTGCGGGGTGGGCTCGAGGCGCTCGACCGGCGTCGACGGTGCGAGGGACATCGTTGTGGTCCCGTGGGT +GGTCACCTTGGTGGCGGCAATCTCGATGTAGTGGTCTTGGTAGTGCACCTTGCGGGCGCTGTCGACCTGA +TAGGTCGTGCGACCGGTGAGGGTGCGGATACGGAAGAGGTCGCCGGTCTCAAGCTGGGCGGCGGTGACGA +TCTGGCTCATTTGGTGTTCCTGCTTTCGTGGTTGTTGGTGGTCAGCTCAGCGAGCCGCTGCGGTAGGCCC +GCAGGATGGACAGTGCGGTATCGATGTCCTTGTAGGCGGCGAGCTCGGCAAGCTCGCGGATCTGGCTGTG +GTCGGTGTCGTGCACGTTGAACGCGGCGGCGCACATGGTCGCGTGAGGCAGGGTGTGGTCCTCGGAGGTG +AGGCAGTCGCACGCGGGGGAGGTGGACATTTCGATCCTTTCTCGGCGGGTCGATCCCGCCTGACAAGAGA +TAACTTACCCGACTAGTGCGGGTAAGTCAACACCTTAACGTCGAGTCGTGCCGAGCACGCAGAGCTGCCC +GGCGCTCACGTCGACGCAGCGCTCGCCGAGGGACCGGTGAGGCGGCGACGGGAAACCACCCGAGCAGGAT +GCCGACCTCGTCATGACGTTCCTCGTAGCGCAGCACAAAGTGCTTGAGGCCCCACATCGTGGTGCGCGCA +CCGACGACCTTGACATCGCCGGGCATCACCGTGTAGGCCCACGGGCGGTGCCGTCCGCTGCCCACGGTGC +CGCGCAATGGCCGCCCGCTCATCGCTGCGCGGCTCTCTTGTCGGCCTCGGCCCGTCGACGGTAGTAGTCG +GCGATCTGCCGGGTGAGGTCATGGAGCGCTGGGGTGATGGCGTCGGCGGCGCGCATGTTGCCGGACGCGT +AGAGGATTTCGCGCGCGGTGGCCAGGGTCTCGCGCGCCTTGGACGCTTTCGCTACTCCGGTCGGGCTCGT +GATGGTCTGATTGACGTTGATGACTCGGCTCTGAACTACTGCCTTGGTGCCCACGGTTGTTCCTGCTTTC +GTGGTTGTTGGTGACGGGTGACTAGGAGATCCGGTTGACGTGCGACCAGTCGAGATGCGTCGGCTCGGGG +AGGTGGTCGTACTGCACGACGGCGACCTCGGCATCTGTGGTGTTGAGCAGCTCGGCGGTGAAACCGTCCT +TGTCCACGATGCGGACCGGCGAGTTGTCAATGATCTTGAACGTGCCAGCGACACCGCCCTTGTTCGGCGC +GGTGCGGTAGTGCCAGACACCGGAGATCGTGTCCTCGTCGCGACGTTCCTGCGTGGCTGTGAGGTTGTTG +ACCGCCGCGATCTGTCCAATGCGCGCGGTGGCGTCGGCTTGGGTCTCGTGCTGAGTGACGACGGTGCGGC +CATTGAGGCGAGCGACGAACTTGAACATGTCGGTTGTTCCTTTCTCGGCGGGACCGTCCCGCCCTGACAA +GAGAAAACTTACCCGCCTATGTCGGGTATGTCAACCCTGCCTGTGATACAGAAAACCCCGCCCGGTGAGG +GGCGGGGTTTCTGCGCGGTTACCAGTCGCTGTACCAGACTGCCGACTGTATCAGAACTCGAACTCGCTGT +AGGGGCCGGGGCGGACCTCGCCGCCGTACTGCGCGCCGCCGACAAACGGCGAGGACGGTCGTTCAAACGG +CGAGGACGGGCGTTCGGTGCGCAGCACCTTGCCGTAGCGGCCCATGACCTCGGCAACCGGCGGCTCGCCG +CTGCTGGCCAGCACCGTGCGGGTCTGCGTGACCTCGGCGATCGTGTGCCAATTGGCCTCGCCGATGAAAT +CGAGCAGGCCCGACCAATTGAACCGCCGTGTCTCGGTGCCGGTGACCGCCCACCGCACTGAGGTGCCTGC +GCGCCACCCGATGGCCGCGTAGTTGTACTCGCGCCCGCTCGTGTAGCGCCGGAACGTCACGACAACGCTC +ACCCCGGTCTGGACCGTCGGCATTTTGGGGCGGCGCGCTTCCTCGGCAGCTCGCGCGATCTCGGCGGCCT +GCCTGCGGAACGCGGCGGCGGCTTTCTCGGCCTCGGCGGCAGCGGCGAGCAGGTCGGCGGGAGTGCTCGG +GTCGGCTACGACACCCTTAGGGGCCTTGTCGGCCCCGTTCGGATCTTCCATGGTGGTGTTCTCTTTCTCT +CGTTGGTTGTTGCGTTTCACCTCGGCGAGGCGGCGCTTGAGTTCCCGCTTGCGGGCGCGAAGATCGGCCC +TGACCGTGCTGATATCGGTCCACCCTCGGCATGTCGTGGCCAGCAGCGACGAGACGATGGCCAATTGTGC +ACTGACCTCGACCTCGGTGTGGGGCGCGCTCACAGCGTCACTGCCCCAAACGCGGCCCCGATGATGCCGG +TGATCGTCAGTGACAGCGTGAGCGTGGCGAACACTGCCTCGCGCAGTGATCCTCGCGGGGAGAAGTGCAG +CGCGAAGCACGCGAGCAGTGCCAGCCCGAGCACGACGTATGCCGTGCTCGGGTCGTATGCGAAGCTCATG +CGCGGGCCTCGCGTGCGGCGCGGCGAGCGGCCCAGTAGGCACCGTTGCGCTCGATCTCGAAATCGACCAT +GGCGCTCACTACCGCGTAGAGCTCGACCTCGGGATCGTGCAGGACGTTCTCGTGATCCGAGATGATGCCG +AGTCCGGTCTTGCATCCGTCCTCGTGGGCAGTAATGTGGCCCATGCAGTCGTAGCAGGTGGGCGGCTCAA +CGATGTCGGCGGGATCGAACATTGTGGTGGTCCTTCCTGTTGGGGGCGGGTCTGTCCCGCCTGACATCCA +TTAAGTTACCCGACAAGGGTGGGCTTGTCAACACCGTCGTACGGATCGATGAGCGTGAACTCGGACCGTA +CCCGGCACAGGGTCTTGCCGTAGGGCATGCGTATCGCGTACACCACACGATCTGCGGGCGTCTCGTCGCC +GTTGAGCCGCTGCGCGAGCTGCCCGACCTTGCCGCTGTCGCGCAGCAACAGGTGATCGCCGGGCACCACC +TCGGTGGTGGGATCATCGATCCTTGCGTTGAGCGCACGCTCGCACTCGGCCTGCGCATCTGCCTTGGTCT +CGTGGTGCGAGGTCTCGACACCCGGCCCCTTGGCCACCCAACCGAGGGCAGCCGCCTCGGCGCGGTGGAC +GTGGTAGGCACCGGCGGCGTAGGCACCGCCTGTGTGCTTCTCGCGATGCCATCTCATCAGATGGCTCCCT +GAGTGATCCCGTTGACCCACAGCACGGTCTCGAGAACTTCCTCGCGGGTTGCGCCGTGCATGTCGATCAC +GGCGATGCGATCTATGGTGCCGTGCTCACTGCCGTAGTGCGTGCCGCATGCCACGTGCGCGCTCAGTTTG +CCCCACAGGATCTGTCCGAGGGTGTTCTGTGCGGTGAGCATCCCGTCGAACATGGTCTCGTCACTGTTGA +GGATGTCGACGGTGCCCTCGCCCGCAACGGTGACGTAGTGGTCGGGGCCACCATCGGTGCTCGTCGTGCG +CGTGTTCCAGTCGCCGAGCTTGGTGATGATGTGGATGGCCATGGTGTTCCTGCTTTCGGTCGTGGTTGTC +GGATGATGGTGTGCGTTGGCCGGACGCACCCCCGGTCGAGTCTCAGGCGATGTGAGCCTCGACATCCGCG +TAGGTCATGGCCATGTGCGCGGCGTACGGCATGGTCCAATGGACCACCAGTCCGGCCTTGTCGAGGTTCT +TCTCAAAGCTGCGGAACCGCTTGATGGCGGTCTCGTTGCGATCTCCGGTCACCCCGTTGGACGAGAAGTT +GAACACCGCCTCGATCATGCCGACGTGGCCGCCGTTGTCCTTGATACCGATCATTTTCTTGGCGACGACT +TTGTTGGACTTCGGGAACTGTGCAGCAAATTCCTGGGCCTCGGCCTCGGTGGCGAAGAACTGAACGACCT +TGACCCGAACGTTGTTTTTCATGCGGGCGGCCTCGGCATGCAGACCCTTTTCGATCCAGAACGCGGCCTT +GTTGTAGTCGGTGGTGGTCTGGGCGGCGGGGGCGGTGATCTGCATTTCTTCCTCTCTCGGCGGGACGTTC +CCGCCTGACACGAACTAAGTTACCCGAGTTGGGCGGGTATGTCAACACCAATCTTCAAGTTCTCCATATC +TGTCGGGTAAC

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