Retrieval-Augmented Residual Graph Neural Network for Protein-Protein Interaction Site Prediction.
Accurate prediction of protein-protein interaction sites (PPISs) plays a crucial role in understanding protein function, elucidating disease mechanisms, and facilitating drug target discovery. Although conventional approaches based on sequence or structural features have shown promising results, they still face several challenges. These challenges include oversmoothing in deep graph neural networks (GNNs) and poor generalization to domain-specific data. To address these issues, we propose RGLLA-PPIS, a novel multimodal prediction model that integrates retrieval-augmented learning and residual GNNs for PPIS identification. In RGLLA-PPIS, protein graphs are constructed by combining AlphaFold3 (AF3)-predicted protein structures with multiple sequence-derived features. To effectively capture both local and global spatial dependencies, the model employs equivariant GNN (EGNN) and GCN modules with residual connections, which help alleviate the oversmoothing problem and preserve node-level variability. Moreover, during prediction, we used the retrieval-augmented knowledge provided by the pretrained protein language model (PLM) Evolla and ChatGPT-4o to construct semantic priors to supplement potential functional site information and enhance the generalization capacity of the prediction model. Extensive experiments on benchmark datasets show that RGLLA-PPIS outperforms several state-of-the-art baselines in both accuracy and robustness. Furthermore, comparison with wet-lab results on a domain-specific protein system reveals a strong correspondence between experimental functional sites and the high-probability regions predicted by RGLLA-PPIS. This demonstrates the model's potential to guide real-world protein engineering tasks. The source code can be found at: https://github.com/MiJia-ID/RGLLA-PPIS.