Edge-aware GAT-based protein binding sites prediction.

Accurate identification of protein binding sites is crucial for understanding biomolecular interaction mechanisms and for the rational design of drug targets. Traditional predictive methods often struggle to balance prediction accuracy with computational efficiency when capturing complex spatial conformations. To address this challenge, we propose an Edge-aware Graph Attention Network (Edge-aware GAT) model for the fine-grained prediction of binding sites across various biomolecules, including proteins, DNA/RNA, ions, ligands, and lipids. Our method constructs atom-level graphs and integrates multidimensional structural features, including geometric descriptors, DSSP-derived secondary structure, and relative solvent accessibility (RSA), to generate spatially aware embedding vectors. By incorporating interatomic distances and unit direction vectors as geometric edge features within the attention mechanism, the model enhances local structural representation without claiming strict SE(3)- or E(3)-equivariance. On benchmark datasets, our model achieves a ROC-AUC of 0.93 for protein-protein binding site prediction and shows competitive performance among the evaluated structure-based atomic-level models. The use of geometry-aware edge attention and residue-level attention pooling further improves both binding site localization and the capture of local structural details. Visualizations using PyMOL confirm the model's practical utility and interpretability. To facilitate community access and application, we have deployed a publicly accessible web server at http://119.45.201.89:5000/. In summary, our approach offers an efficient structure-based framework that balances prediction accuracy, generalization, and interpretability for identifying functional sites in proteins.