PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences

Payel Das; Kahini Wadhawan; Oscar Chang; Tom Sercu; Cicero Dos Santos; Matthew Riemer; Vijil Chenthamarakshan; Inkit Padhi; Aleksandra Mojsilovic

arXiv · 1810.07743

PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences

Payel Das·Kahini Wadhawan·Oscar Chang·Tom Sercu·Cicero Dos Santos·Matthew Riemer·Vijil Chenthamarakshan·Inkit Padhi·Aleksandra Mojsilovic

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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.

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BibTeX RIS

Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cicero Dos Santos, Matthew Riemer, Vijil Chenthamarakshan, Inkit Padhi, Aleksandra Mojsilovic. 2018-11-13. PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences. https://arxiv.org/abs/1810.07743

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