EGCPPIS: learning hierarchical equivariant graph representations with contrastive integration for protein-protein interaction site identification.

ABSTRACT:

Background

Protein-protein interactions regulate the dynamic operation of intracellular molecular networks, serving as the molecular basis for revealing protein functions and disease mechanisms. Recently, several computational methods for predicting protein-protein interaction sites (PPIs) have been presented as alternatives to costly and labor-intensive traditional experiments. However, existing methods generally ignore the inherent hierarchical structure of protein chains. Furthermore, the equivariance of graph structure during spatial transformations is often neglected when applying graph neural networks to modeling. Therefore, accurately identifying PPIs remains a challenging task.

Results

In this work, we propose an end-to-end GNN-based computational method, EGCPPIS, fo

SUBMITTER: Sun G 

PROVIDER: S-EPMC12751822 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature