AEG-PPIS: A Dual-Branch Protein-protein Interaction Site Predictor Based on Augmented Graph Attention Network and Equivariant Graph Neural Network

The identification of protein-protein interaction sites (PPIS) plays a crucial role in understanding the mechanisms of biological processes. Traditional biological experimental methods for PPIS prediction are both expensive and time-consuming, developing computational methods can effectively reduce costs. However, existing approaches often focus on single-scale features and pay little attention to spatial neighborhood features, leading to unsatisfactory prediction performance. To address these challenges, we propose a dual-branch PPIS predictor (AEG-PPIS) based on augmented graph attention network (AGAT) and E(n) equivariant graph neural network (EGNN). AEG-PPIS extracts global features through EGNN, ensuring rotational and translational invariance of the protein graph. For local feature extraction, it employs an enhanced Augmented Graph Attention Network, which integrates initial node features, previous layer outputs, and features extracted by GraphSAGE using residual connections and identity mapping. Our model realizes the modeling of multi-scale information. Comparative experimental results show that the performance of AEG-PPIS is better than that of state-of-the-art methods. Ablation experiments and case studies demonstrate the effectiveness of AEG-PPIS in predicting PPIS.