Co-modeling the Sequential and Graphical Routes for Peptide Representation Learning

TL;DR

This paper introduces RepCon, a contrastive learning framework that co-models peptide representations from sequential and graphical models, improving discriminative performance by enhancing their mutual information.

Contribution

It proposes a novel peptide co-modeling method, RepCon, that fuses sequential and graphical peptide representations using contrastive learning to improve downstream task accuracy.

Findings

RepCon outperforms independent models on multiple peptide datasets.

Co-modeling enhances the mutual information between different peptide representations.

Attribution analysis supports the validity of the RepCon approach.

Abstract

Peptides are formed by the dehydration condensation of multiple amino acids. The primary structure of a peptide can be represented either as an amino acid sequence or as a molecular graph consisting of atoms and chemical bonds. Previous studies have indicated that deep learning routes specific to sequential and graphical peptide forms exhibit comparable performance on downstream tasks. Despite the fact that these models learn representations of the same modality of peptides, we find that they explain their predictions differently. Considering sequential and graphical models as two experts making inferences from different perspectives, we work on fusing expert knowledge to enrich the learned representations for improving the discriminative performance. To achieve this, we propose a peptide co-modeling method, RepCon, which employs a contrastive learning-based framework to enhance the mutual information of representations from decoupled sequential and graphical end-to-end models. It considers representations from the sequential encoder and the graphical encoder for the same peptide sample as a positive pair and learns to enhance the consistency of representations between positive sample pairs and to repel representations between negative pairs. Empirical studies of RepCon and other co-modeling methods are conducted on open-source discriminative datasets, including aggregation propensity, retention time, antimicrobial peptide prediction, and family classification from Peptide Database. Our results demonstrate the superiority of the co-modeling approach over independent modeling, as well as the superiority of RepCon over other methods under the co-modeling framework. In addition, the attribution on RepCon further corroborates the validity of the approach at the level of model explanation.