Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains.

ABSTRACT: Bacteriocins, the ribosomally produced antimicrobial peptides of bacteria, represent an untapped source of promising antibiotic alternatives. However, bacteriocins display diverse mechanisms of action, a narrow spectrum of activity, and inherent challenges in natural product isolation making in vitro verification of putative bacteriocins difficult. A subset of bacteriocins exert their antimicrobial effects through favorable biophysical interactions with the bacterial membrane mediated by the charge, hydrophobicity, and conformation of the peptide. We have developed a pipeline for bacteriocin-derived compound design and testing that combines sequence-free prediction of bacteriocins using machine learning and a simple biophysical trait filter to generate 20 amino acid peptides that can be sy

SUBMITTER: Fields FR 

PROVIDER: S-EPMC9202646 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature