Hierarchical Graph Representation Learning for Protein-Protein Interaction Site Prediction.

Precisely identifying protein-binding locations is essential for advancing protein engineering and pharmaceutical discovery. However, due to the intrinsically hierarchical nature of protein data, extracting deep representations from such data remains a difficult task. Furthermore, issues such as class imbalance and uneven topological distribution of interaction sites limit further improvements in prediction performance. To tackle these issues, we present HGRL-PPIS, a novel hierarchical graph representation learning approach for predicting protein-protein interaction sites. Specifically, our framework first constructs a three-tier graph encompassing a protein-level graph, residue-level and atomic-level graphs. A Transformer-based module is then employed to enable efficient cross-hierarchical information propagation. To mitigate the class imbalance caused by the sparse and uneven distribution of binding sites, we introduce a prototypical graph neural network training strategy that balances data distribution and enhances prediction accuracy. Our experiments reveal that HGRL-PPIS achieves superior performance over competing methods on multiple benchmark datasets, enabling more reliable detection of protein-protein binding residues.