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Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains
Author(s) -
Fields Francisco R.,
Freed Stefan D.,
Carothers Katelyn E.,
Hamid Md Nafiz,
Hammers Daniel E.,
Ross Jessica N.,
Kalwajtys Veronica R.,
Gonzalez Alejandro J.,
Hildreth Andrew D.,
Friedberg Iddo,
Lee Shaun W.
Publication year - 2020
Publication title -
drug development research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.582
H-Index - 60
eISSN - 1098-2299
pISSN - 0272-4391
DOI - 10.1002/ddr.21601
Subject(s) - bacteriocin , antimicrobial peptides , antimicrobial , peptide , computational biology , escherichia coli , lantibiotics , biology , colicin , natural product , chemistry , biochemistry , microbiology and biotechnology , gene
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 synthesized and evaluated for activity. We generated 28,895 total 20‐mer candidate peptides and scored them for charge, α‐helicity, and hydrophobic moment. Of those, we selected 16 sequences for synthesis and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. Peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against Escherichia coli and Pseudomonas aeruginosa . Our combined method incorporates machine learning and biophysical‐based minimal region determination to create an original approach to swiftly discover bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.