iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types.

Abstract

Predicting antimicrobial peptides (AMPs ) function is an important and difficult problem, particularly when AMPs have many multiplex functions, i.e. some AMPs simultaneously have two or three functional classes. By introducing the CNN-BiLSTM-SVM classifier and cellular automata image , a new predictor, called iAMP-CA2L, has been developed that can be used to deal with the systems containing both monofunctional and multifunctional AMPs. iAMP-CA2L is a 2-level predictor. The 1st level is to identify whether a given query peptide is an AMP or a non-AMP, while the 2nd level is to predict if it belongs to one or more functional types. As demonstration, the jackknife cross-validation was performed with iAMP-CA2L on a benchmark dataset of AMPs classified into the following 10 functional classes: (1) antibacterial peptides, (2) antiviral peptides, (3) antifungal peptides, (4) antibiofilm peptides, (5) antiparasital peptides, (6) anti-HIV peptides, (7) anticancer (antitumor) peptides, (8) chemotactic peptides, (9) anti-MRSA peptides and (10) antiendotoxin peptides, where none of AMPs included has ≥90% pairwise sequence identity to any other in the same subset. Experiments show that iAMP-CA2L has greatly improved the prediction performance compared with the existing predictors. iAMP-CA2L is freely accessible to the public at the web site http://www.jci-bioinfo.cn/ iAMP-CA2L, and the predictor program has been uploaded to https://github.com/liujin66/iAMP-CA2L.