Discovering highly potent antimicrobial peptides with deep generative model HydrAMP.

ABSTRACT: Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increas

SUBMITTER: Szymczak P 

PROVIDER: S-EPMC10017685 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature