PeptideSGCL: Structure-Enhanced Graph-Transformer Encoding and Dual-Level Contrastive Learning for Peptide Property Prediction.
TL;DR
A multimodal dual-contrastive learning framework for peptide property prediction is proposed, which improves both the structural encoder and the contrastive learning strategy to enhance the quality of joint sequence-structure representations.
Abstract
Peptides play important roles in biological processes and biomedical applications, and their hemolytic (Hemo) and nonfouling (NF) properties directly affect their safety and translational potential. Therefore, accurate predictive models are essential for the rational design of functional peptides. Although existing multimodal peptide property prediction methods can jointly exploit sequence and structural information, their structural encoders still rely primarily on local graph convolution and their contrastive objectives are largely focused on cross-modal alignment. Consequently, they remain limited in modeling long-range structural dependencies and in enhancing intramodal discriminability. To address these limitations, we propose a multimodal dual-contrastive learning framework for peptide property prediction, which improves both the structural encoder and the contrastive learning strategy to enhance the quality of joint sequence-structure representations. Specifically, ProtBERT is adopted as the sequence encoder, and a hierarchical GNN-Transformer structural encoder is constructed to capture local topological patterns and long-range structural dependencies. In addition, a parallel graph spatial channel attention module is introduced to enhance task-relevant structural features. Within a shared embedding space, we further design an interintra hybrid supervised contrastive learning strategy to jointly optimize sequence-structure alignment and intramodal class discriminability. Experimental results show that the proposed method achieves overall performance superior to baseline models on both hemolysis and NF prediction tasks, providing an effective framework for multimodal representation learning in peptide-property prediction.