Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC

TLDR

Antimicrobial peptides are key innate immune effectors effective against pathogens, yet experimental identification is costly. The study aims to create a computational tool that efficiently predicts promising AMP candidates before laboratory testing. Using support vector machines trained on compositional, physico‑chemical, and structural peptide features, the authors developed a predictive model. The model outperformed several existing methods on benchmark data and is available as the free online server iAMPpred, enhancing AMP discovery.

Abstract

Abstract Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server i AMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/ . The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs.