Multiscale and global-local U-Net for protein-protein interaction site prediction

Precise prediction of protein-protein interaction sites (PPIS) is fundamental to deciphering cellular mechanisms and accelerating therapeutic discovery. Despite significant advancements in computational approaches, current methods frequently fail to integrate multiscale features that simultaneously capture global context and local interactions. We present Multiscale and Global-Local U-Net for Protein-Protein Interaction Site Prediction (MGU-PPIS), a novel architecture designed to address this critical limitation. Our model leverages a U-Net framework with implemented multi-level pooling to extract comprehensive multiscale features. Within each scale, we synergistically combine Transformer networks, Graph Convolutional Networks (GCNs), and Graph Attention Networks (GATs) to simultaneously capture global patterns and local structural motifs. We implement Laplacian positional encoding to effectively represent global protein structural characteristics. In our framework, proteins are conceptualized as graph structures where individual residues function as nodes and their spatial relationships define edges. The model processes information through an innovative two-stage U-Net architecture, where output features from the initial stage serve as refined inputs for the subsequent stage. This dual-stage design, coupled with our graph-based representation, enables MGU-PPIS to extract a rich spectrum of multiscale features encompassing both global context and local interactions at each scale. Comprehensive experimental validation demonstrates that MGU-PPIS significantly outperforms state-of-the-art methods in predictive accuracy. Beyond introducing a novel computational strategy for PPIS prediction, our work establishes a foundation for advances in protein functional analysis and structure-based drug design.