Variational Autoencoder for Generation of Antimicrobial Peptides

Abstract

Over millennia, natural evolution has allowed for the emergence of countless biomolecules with highly specific roles within natural systems. As seen with peptides and proteins, often evolution produces molecules with a similar function but with variable amino acid composition and structure but diverging from a common ancestor, which can limit sequence diversity. Using antimicrobial peptides as a model biomolecule, we train a generative deep learning algorithm on a database of known antimicrobial peptides to generate novel peptide sequences with antimicrobial activity. Using a variational autoencoder, we are able to generate a latent space plot that can be surveyed for peptides with known properties and interpolated across a predictive vector between two defined points to identify novel peptides that show dose-responsive antimicrobial activity. These proof-of-concept studies demonstrate the potential for artificial intelligence-directed methods to generate new antimicrobial peptides and motivate their potential application toward peptide and protein design without the need for exhaustive screening of sequence libraries.