Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities.

ABSTRACT:

Motivation

Antimicrobial peptides (AMPs) have the potential to inhibit multiple types of pathogens and to heal infections. Computational strategies can assist in characterizing novel AMPs from proteome or collections of synthetic sequences and discovering their functional abilities toward different microbial targets without intensive labor.

Results

Here, we present a deep learning-based method for computer-aided novel AMP discovery that utilizes the transformer neural network architecture with knowledge from natural language processing to extract peptide sequence information. We implemented the method for two AMP-related tasks: the first is to discriminate AMPs from other peptides, and the second task is identifying AMPs functional activities related to seven different targe

SUBMITTER: Pang Y 

PROVIDER: S-EPMC9750108 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature