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

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 iAMPpred 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.

Figure 1. Information gain for all the 66 features [AAC (20) + PAAC (20) + NAAC (20) + PHYC (3) + STRL (3)] in predicting antibacterial, antiviral and antifungal peptides.

Figure 2. ROC and PR curves of iAMPpred and AntiBP2 for the prediction of antibacterial peptides.

The performance of iAMPpred is found little higher than AntiBP2.

Figure 3. ROC and PR curves of iAMPpred and AVPcompo, AVPphysico models of AVPpred for predicting the antiviral peptides.

The figure shows that the performance of iAMPpred is better than AVPcompo and AVPphysico models of AVPpred.

Figure 4

Snapshots of (a) server page of iAMPpred and (b) result page after execution of the program with an example dataset. The results are displayed in a tabular format showing the sequence identifier and the probabilities with which the sequences are predicted as antibacterial, antiviral and antifungal peptides.

Figure 5. Distribution of length of the sequences in antibacterial, antiviral and antifungal peptides.

The antibacterial and antifungal peptides are > 50 amino acids long, whereas most of the antiviral peptides are < 50 amino acids long.