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• W4385265674 abstract "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/ ) . Key Points ⍰ BLAST-based similarity for annotating antibacterial peptides. ⍰ Machine learning-based models developed using composition and binary profiles. ⍰ Identification and mapping of motifs exclusively found in antibacterial peptides ⍰ Improved version of AntiBP and AntiBP2 for predicting antibacterial peptides. ⍰ Web server for predicting/designing/scanning antibacterial peptides for all groups of bacteria Author’s Biography Nisha Bajiya is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. Shubham Choudhury is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. Anjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. Gajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India." @default.
• W4385265674 created "2023-07-27" @default.
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• W4385265674 date "2023-07-26" @default.
• W4385265674 modified "2023-10-16" @default.
• W4385265674 title "AntiBP3: A hybrid method for predicting antibacterial peptides against gram-positive/negative/variable bacteria" @default.
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• W4385265674 doi "https://doi.org/10.1101/2023.07.25.550443" @default.
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