An in silico platform for predicting, screening and designing of antihypertensive peptides

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/

Brief Description
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.

Citation
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

About the Platform
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