biostudies-literatureHan YNational Natural Science Foundation of Chinagiaf114https://www.ebi.ac.uk/biostudies/studies/S-EPMC12486388biostudies-literatureUnknown14<h4>Background</h4>Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Accurate identification of protein-protein interaction sites is critical for a comprehensive understanding of protein functions and pathological mechanisms. However, conventional experimental approaches for detecting PPIs are often time-consuming and labor-intensive, thereby motivating the development of efficient computational methods to identify PPI sites.<h4>Results</h4>In this work, we propose a novel graph neural network-based method (called MGMA-PPIS) to predict PPI sites by adopting multiview graph embedding and multiscale attention fusion. MGMA-PPIS integrates global node features extracted by an equivariant graph neural network and multiscale local node features extracted bGigaScienceMGMA-PPIS: Predicting the protein-protein interaction site with multiview graph embedding and multiscale attention fusion.PMC1248638862,173,27162,473,312Zhang QQHan YShi MHZhang SWfalseMGMA-PPIS: Predicting the protein-protein interaction site with multiview graph embedding and multiscale attention fusion.<h4>Background</h4>Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Accurate identification of protein-protein interaction sites is critical for a comprehensive understanding of protein functions and pathological mechanisms. However, conventional experimental approaches for detecting PPIs are often time-consuming and labor-intensive, thereby motivating the development of efficient computational methods to identify PPI sites.<h4>Results</h4>In this work, we propose a novel graph neural network-based method (called MGMA-PPIS) to predict PPI sites by adopting multiview graph embedding and multiscale attention fusion. MGMA-PPIS integrates global node features extracted by an equivariant graph neural network and multiscale local node features extracted b2025-01-01T00:00:00Z2025 Jan2026-06-04T01:47:29.875Z2026-05-04T03:13:53.861ZS-EPMC124863884103201810.1093/gigascience/giaf114