PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction.

ABSTRACT: New methods for antimicrobial design are critical for combating pathogenic bacteria in the post-antibiotic era. Fortunately, competition within complex communities has led to the natural evolution of antimicrobial peptide (AMP) sequences that have promising bactericidal properties. Unfortunately, the identification, characterization, and production of AMPs can prove complex and time consuming. Here, we report a peptide generation framework, PepVAE, based around variational autoencoder (VAE) and antimicrobial activity prediction models for designing novel AMPs using only sequences and experimental minimum inhibitory concentration (MIC) data as input. Sampling from distinct regions of the learned latent space allows for controllable generation of new AMP sequences with minimal input paramete

SUBMITTER: Dean SN 

PROVIDER: S-EPMC8515052 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature