PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences
Abstract
Given the emerging global threat of antimicrobial resistance, new methods for next-generation antimicrobial design are urgently needed. We report a peptide generation framework PepCVAE, based on a semi-supervised variational autoencoder (VAE) model, for designing novel antimicrobial peptide (AMP) sequences. Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences. The model further learns a disentangled antimicrobial attribute space by using the feedback from a jointly trained AMP classifier that uses limited labeled instances. The disentangled representation allows for controllable generation of AMPs. Extensive analysis of the PepCVAE-generated sequences reveals superior performance of our model in comparison to a plain VAE, as PepCVAE generates novel AMP sequences with higher long-range diversity, while being closer to the training distribution of biological peptides. These features are highly desired in next-generation antimicrobial design.
Keywords
Cite
@article{arxiv.1810.07743,
title = {PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences},
author = {Payel Das and Kahini Wadhawan and Oscar Chang and Tom Sercu and Cicero Dos Santos and Matthew Riemer and Vijil Chenthamarakshan and Inkit Padhi and Aleksandra Mojsilovic},
journal= {arXiv preprint arXiv:1810.07743},
year = {2018}
}