Welcome to the official repository for AHTpin, an in silico platform developed for predicting, screening, and designing antihypertensive peptides (AHTPs). The platform uses machine learning and QSAR-based approaches to identify bioactive peptides with potential antihypertensive activity.
Web Server: https://webs.iiitd.edu.in/raghava/ahtpin/ ZENODO : https://doi.org/10.5281/zenodo.20079810
Hypertension is one of the leading causes of cardiovascular diseases worldwide. Natural bioactive peptides have emerged as promising therapeutic agents due to their ability to reduce blood pressure with fewer side effects compared to synthetic drugs.
AHTpin was developed to provide a computational framework for identifying antihypertensive peptides using machine learning techniques. The platform integrates Support Vector Machine (SVM)-based regression and classification models trained on experimentally validated peptide datasets collected from public databases and scientific literature.
The system categorizes peptides into tiny, small, medium, and large peptide groups based on sequence length and applies specialized predictive models for each category. Various sequence-derived and chemical descriptors, including amino acid composition, atomic composition, and PaDEL molecular descriptors, were used to improve prediction performance.
In addition to prediction, AHTpin supports peptide screening, analog design, and mapping of antihypertensive regions within proteins, making it a valuable resource for peptide therapeutics, functional food research, and computational drug discovery.
Kumar, R., Chaudhary, K., Chauhan, J. S., Nagpal, G., Kumar, R., Sharma, M., & Raghava, G. P. S. (2015).
AHTpin: An in silico platform for predicting, screening and designing of antihypertensive peptides.
Scientific Reports, 5, 12512.
https://doi.org/10.1038/srep12512
AHTpin is a computational platform developed for identifying antihypertensive peptides from protein sequences and peptide libraries. The system integrates regression and classification machine learning models to predict peptide activity across different peptide lengths.
The platform categorizes peptides into:
- Tiny peptides (Dipeptides & Tripeptides)
- Small peptides (Tetrapeptides, Pentapeptides & Hexapeptides)
- Medium peptides (Length 7–12)
- Large peptides (Length >12)
The study compiled experimentally validated antihypertensive peptides from:
- AHTPDB
- BIOPEP
- ACEpepDB
- Published literature
Machine Learning Models
- SVM-based regression models
- SVM-based classification models
- QSAR-based peptide prediction
Prediction Modules
- Antihypertensive peptide prediction
- Peptide screening
- Analog peptide design
- Protein sequence mapping
Feature Extraction
- Amino acid composition
- Atomic composition
- PaDEL chemical descriptors
- G-scale physicochemical descriptors
The platform was developed using:
- 1745 experimentally validated antihypertensive peptides
- Positive datasets from curated databases
- Negative datasets generated from Swiss-Prot proteins
Peptide Categories:
- 131 Dipeptides
- 205 Tripeptides
- 153 Tetrapeptides
- 270 Pentapeptides
- 199 Hexapeptides
- 368 Medium peptides
- 76 Large peptides
Regression Models
- Dipeptides: R = 0.701
- Tripeptides: R = 0.543
Classification Models
- Tetrapeptides: Accuracy = 76.67%
- Pentapeptides: Accuracy = 72.04%
- Hexapeptides: Accuracy = 77.39%
- Medium peptides: Accuracy = 82.61%
- Large peptides: Accuracy = 84.21%
The prediction models were developed using:
- Support Vector Machine (SVM)
- Leave-One-Out Cross Validation (LOOCV)
- External validation datasets
Descriptor selection was performed using:
- Weka
- RemoveUseless filtering
- BestFirst feature selection
- F-stepping optimization
- Antihypertensive peptide discovery
- Functional food research
- Peptide therapeutic design
- Bioinformatics and QSAR studies
- Machine learning model development
Prof. Gajendra P. S. Raghava
raghava@iiitd.ac.in
raghava@imtech.res.in
Developed at:
Bioinformatics Centre,
CSIR-Institute of Microbial Technology, Chandigarh, India
This work is distributed under the
Creative Commons Attribution 4.0 International License (CC BY 4.0)
Supported by:
- CSIR (Open Source Drug Discovery & GENESIS projects)
- Department of Biotechnology (BTISNET), Government of India
We acknowledge all researchers and databases contributing to antihypertensive peptide research.
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