Predicting protein-protein interaction sites based on dynamic perception mechanism within a hierarchical E(n)-equivariant graph.

Accurate prediction of protein-protein interaction sites is crucial to understanding biological processes, elucidating disease mechanisms, and accelerating drug discovery. Although graph neural network methods have shown potential in this field, but existing methods are limited by the static integrate multi-group features and insufficient perception of hierarchical 3D spatial geometric information, leading to insufficient predictive ability of orphan sites. To address these issues, this paper proposes a Dperception mechanism within a Hierarchical E(n)-equivariant Graph architecture (DHEG). DHEG introduces a dynamic feature importance perception mechanism that adaptively perceives the contextual inter-dependencies of features and assigns weights to feature groups based on their relevance to the interaction relationship. And a hierarchical gated architecture based on E(n)-equivariant graph neural networks that effectively captures protein 3D spatial structures while mitigating over-smoothing problems. The results show that DHEG achieves improvements in 11 of 13 key metrics, with an enhancement 8% in Matthews correlation coefficient, indicating that DHEG not only predicts more interaction sites but also does so with greater reliability. Furthermore, case studies and visualization analyzes show that DHEG aligns better with the biological mechanism and has excellent predictive capabilities for both orphan sites and continuous regions, demonstrating interpretability, and application potential.