AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens.

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 f

SUBMITTER: Li C 

PROVIDER: S-EPMC8788131 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature