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AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens Li, Chenkai; Sutherland, Darcy; Hammond, S. Austin; Yang, Chen; Taho, Figali; Bergman, Lauren; Houston, Simon; Warren, René L.; Hoang, Linda M. N.; Cameron, Caroline E.; Helbing, Caren C.; Birol, Inanc

Abstract

Background Antibiotic resistance is a growing global health concern prompting researchers to seek alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are attracting attention again as therapeutic agents with promising utility in this domain, and using in silico methods to discover novel AMPs is a strategy that is gaining interest. Such methods can sift through large volumes of candidate sequences and reduce lab screening costs. Results Here we introduce AMPlify, an attentive deep learning model for AMP prediction, and demonstrate its utility in prioritizing peptide sequences derived from the Rana [Lithobates] catesbeiana (bullfrog) genome. We tested the bioactivity of our predicted peptides against a panel of bacterial species, including representatives from the World Health Organization’s priority pathogens list. Four of our novel AMPs were active against multiple species of bacteria, including a multi-drug resistant isolate of carbapenemase-producing Escherichia coli. Conclusions We demonstrate the utility of deep learning based tools like AMPlify in our fight against antibiotic resistance. We expect such tools to play a significant role in discovering novel candidates of peptide-based alternatives to classical antibiotics.

Item Metadata

Title
AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens
Creator
Li, Chenkai; Sutherland, Darcy; Hammond, S. Austin; Yang, Chen; Taho, Figali; Bergman, Lauren; Houston, Simon; Warren, René L.; Hoang, Linda M. N.; Cameron, Caroline E.; Helbing, Caren C.; Birol, Inanc
Contributor
Michael Smith Genome Sciences Centre; University of British Columbia. Graduate Program in Bioinformatics; BC Centre for Disease Control; Vancouver General Hospital
Publisher
BioMed Central
Date Issued
2022-01-25
Description
Background Antibiotic resistance is a growing global health concern prompting researchers to seek alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are attracting attention again as therapeutic agents with promising utility in this domain, and using in silico methods to discover novel AMPs is a strategy that is gaining interest. Such methods can sift through large volumes of candidate sequences and reduce lab screening costs. Results Here we introduce AMPlify, an attentive deep learning model for AMP prediction, and demonstrate its utility in prioritizing peptide sequences derived from the Rana [Lithobates] catesbeiana (bullfrog) genome. We tested the bioactivity of our predicted peptides against a panel of bacterial species, including representatives from the World Health Organization’s priority pathogens list. Four of our novel AMPs were active against multiple species of bacteria, including a multi-drug resistant isolate of carbapenemase-producing Escherichia coli. Conclusions We demonstrate the utility of deep learning based tools like AMPlify in our fight against antibiotic resistance. We expect such tools to play a significant role in discovering novel candidates of peptide-based alternatives to classical antibiotics.
Subject
Antimicrobial peptide; Deep learning; Attention mechanism
Genre
Article
Type
Text
Language
eng
Date Available
2022-02-09
Provider
Vancouver : University of British Columbia Library
Rights
Attribution 4.0 International (CC BY 4.0)
DOI
10.14288/1.0406541
URI
http://hdl.handle.net/2429/80806
Affiliation
Medicine, Faculty of; Non UBC; Medical Genetics, Department of; Medicine, Department of; Pathology and Laboratory Medicine, Department of
Citation
BMC Genomics. 2022 Jan 25;23(1):77
Publisher DOI
10.1186/s12864-022-08310-4
Peer Review Status
Reviewed
Scholarly Level
Faculty; Researcher
Copyright Holder
The Author(s)
Rights URI
http://creativecommons.org/licenses/by/4.0/
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Attribution 4.0 International (CC BY 4.0)