Protein Crystallization Propensity Prediction Using Deep Learning Approach

Requirements (if using Anaconda)

1. Download Anaconda (64 bit) installer python3.x for linux : https://www.anaconda.com/distribution/#download-section
2. Run the installer : bash Anaconda3-2019.03-Linux-x86_64.sh and follow the instructions to install.
3. Install tensorflow-gpu: conda install tensorflow-gpu
4. Install imblearn: conda install -c glemaitre imbalanced-learn
5. Install sklearn: conda install -c conda-forge scikit-learn

Requirements (if using Google Colaboratory)

1. Clone this repository in Google Colab : !git clone https://github.com/onkarsabnis/ProteinCrysPred_DL.git
2. Go to approapriate directory using cd command
3. Now train the model on the data present in the data directory(it's not the complete data but a subset) by: !CUDA_VISIBLE_DEVICES=? python Train.py
4. To run the model on a sample test file do: !CUDA_VISIBLE_DEVICES=? python Test.py

To Train the entire Model

1. Protein sequences have to be saved in a fasta format similar to following format:

.>Seq1
MPKFYCDYCDTYLTHDSPSVRKTHCSGRKHKENVKDYYQKWMEEQAQSLIDKTTAAFQQG
where '>Seq1' represents the fasta id and the second line is the protein sequence.

2. Download the data files ( all files *.hdf5 and files *.json) by running the following command:
   !wget https://storage.entrydns.org/nextcloud/index.php/s/3ErNEaZiKp39x4N/download

3. Run the following two commands after downloading the files:
   !unzip download
   !rm download
   

4. To train the model on your own data, run the following command (Train.py and the fasta file have to be in the same directory):
   $ !python Train.py <file.fasta>.
   Add the trained model in the 'model' directory.

Now to run the model on test file: !CUDA_VISIBLE_DEVICES=? python Test.py
You can change the input file in the code.