AntiBP3: A hybrid method for predicting antibacterial peptides against gram-positive/negative/variable bacteria
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.25.550443v1?rss=1

Authors: Bajiya, N., Choudhury, S., Dhall, A., Raghava, G. P. S.

Abstract: This study focuses on the development of in silico models for predicting antibacterial peptides as a potential solution for combating antibiotic-resistant strains of bacteria. Existing methods for predicting antibacterial peptides are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we introduce a novel approach that enables the prediction of antibacterial peptides against several bacterial groups, including gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify antibacterial peptides and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict antibacterial peptides and obtained high precision with low sensitivity. To address the similarity issue, we developed machine learning-based models using a variety of compositional and binary features. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98 and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, when evaluated on a validation/independent dataset. Our attempts to develop hybrid or ensemble methods by merging machine learning models with similarity and motif-based techniques did not yield any improvements. To ensure robust evaluation, we employed standard techniques such as five-fold cross-validation, internal validation, and external validation. Our method performs better than existing methods when we compare our method with existing approaches on an independent dataset. In summary, this study makes significant contributions to the field of antibacterial peptide prediction by providing a comprehensive set of methods tailored to different bacterial groups. As part of our contribution, we have developed the AntiBP3 web server and standalone package, which will assist researchers in the discovery of novel antibacterial peptides for combating bacterial infections (https://webs.iiitd.edu.in/raghava/antibp3/).

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