PocketStruct: Integrating Protein Pocket Structural Features for Protein-Peptide Binding Prediction

Peptides are attractive candidates for drug development because of their low toxicity and relatively small binding interfaces, making accurate protein-peptide binding prediction crucial. In this study, we propose a flexible transformer-based framework with mutual attention that integrates protein pocket structural information and can be instantiated with different pocket-structure encoders. Within this unified framework, we systematically compare three encoders: an attention-based SE(3)-Transformer, a geometric graph neural network ProtGVP, and the large-scale structure-based pretrained model ESM-IF1. Using rigorous data partitioning with strict separation of training and test sets, we show that incorporating pocket structural information consistently improves binding prediction over sequence-only models, with GVP-GNN providing particularly effective pocket representations and structure-based variants exhibiting superior robustness on previously unseen data.