Designing novel functional peptides by manipulating a temperature in the softmax function coupled with variational autoencoder

Abstract

Development of an efficient peptide design method is crucial for tackling medical problems, such as designing antimicrobial peptides for combating drug-resistant pathogens and anticancer peptides for various cancers. Here, we present Variational Autoencoder (VAE) coupled with a Softmax function having a temperature factor (T) for high-throughput design of novel functional peptides. VAE is a generative machine learning model, which has proved to be useful for generating peptide sequences. In this study, we additionally use a Softmax function with T to facilitate determining the most probable amino acids at each position of peptide sequences to be generated, which is difficult to achieve using a conventional VAE. In particular, by manipulating T in the Softmax function, we select biologically most feasible peptides with a desired function. This method is demonstrated for designing novel antimicrobial and anticancer peptides in this study. The method presented herein should be useful for designing various peptides with a desired function upon availability of relevant datasets.