Enhanced Protein Network Representation with Explicit Structural Binding for Protein-Protein Interaction Prediction.

Abstract

Protein-protein interactions (PPIs) are fundamental molecular events in the human body, playing a pivotal role in disease treatment and intervention. However, existing approaches for protein representation often rely on simplistic PPI network models, which face two key challenges: (i) neglecting explicit residue-based binding relationships critical to protein interactions, and (ii) failing to integrate residue-level binding data with protein interaction networks, limiting their ability to uncover the binding mechanisms of PPIs. To address these issues, we propose an Enhanced protein network representation framework with Explicit structural binding information for improved PPI prediction, named E $^{2}$ PPI. Specifically, E $^{2}$ PPI extracts residue-level interactions between paired proteins using both single-protein structural analysis and inter-protein binding representation modules. To seamlessly integrate residue-level binding data with the semantics of protein interactions, we introduce an enhanced protein network representation module. This design enables the model to capture the interaction and binding mechanisms of PPIs, thereby improving its classification performance. Benchmark experiments demonstrate that E $^{2}$ PPI outperforms state-of-the-art models, especially for few-shot and novel proteins, showcasing its superior generalization capabilities.