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

Autor: Alejandro J. Gonzalez, Andrew D Hildreth, Nafiz Hamid, Stefan D. Freed, Francisco R. Fields, Katelyn E. Carothers, Daniel E. Hammers, Shaun W. Lee, Veronica R. Kalwajtys, Iddo Friedberg, Jessica N Ross
Rok vydání: 2019
Předmět:
Staphylococcus aureus
Antimicrobial peptides
Peptide
Microbial Sensitivity Tests
medicine.disease_cause
Machine learning
computer.software_genre
Protein Structure
Secondary
Article
Machine Learning
03 medical and health sciences
chemistry.chemical_compound
Structure-Activity Relationship
0302 clinical medicine
Bacteriocin
Bacteriocins
Protein Domains
Drug Discovery
medicine
Escherichia coli
Selection (genetic algorithm)
Combined method
030304 developmental biology
chemistry.chemical_classification
0303 health sciences
Natural product
biology
030306 microbiology
business.industry
food and beverages
Computational Biology
Antimicrobial
biology.organism_classification
Amino acid
Anti-Bacterial Agents
chemistry
030220 oncology & carcinogenesis
Drug Design
Pseudomonas aeruginosa
Amino acid peptide
Artificial intelligence
business
computer
Hydrophobic and Hydrophilic Interactions
030217 neurology & neurosurgery
Bacteria
Antimicrobial Cationic Peptides
Zdroj: Drug Dev Res
ISSN: 1098-2299
Popis: Bacteriocins are ribosomally produced antimicrobial peptides that represent an untapped source of promising antibiotic alternatives. However, inherent challenges in isolation and identification of natural bacteriocins in substantial yield have limited their potential use as viable antimicrobial compounds. In this study, we have developed an overall pipeline for bacteriocin-derived compound design and testing that combines sequence-free prediction of bacteriocins using a machine-learning algorithm and a simple biophysical trait filter to generate minimal 20 amino acid peptide candidates that can be readily synthesized and evaluated for activity. We generated 28,895 total 20-mer peptides and scored them for charge, α-helicity, and hydrophobic moment, allowing us to identify putative peptide sequences with the highest potential for interaction and activity against bacterial membranes. Of those, we selected sixteen sequences for synthesis and further study, and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. We show that bacteriocin-based peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against E. coli and P. aeruginosa. Our combined method incorporates machine learning and biophysical-based minimal region determination, to create an original approach to rapidly discover novel bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.
Databáze: OpenAIRE
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