{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li Y"],"funding":["Natural Science Foundation of Henan Province of China","Major Science and Technology Projects in Henan Province","National Natural Science Foundation of China","Key Research and Development Projects in Henan Province","Key Scientific and Technological Project of Henan Province"],"pagination":["bbaf079"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11879409"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["26(2)"],"pubmed_abstract":["Motivation
Protein-protein interactions play a fundamental role in biological systems. Accurate detection of protein-protein interaction sites (PPIs) remains a challenge. And, the methods of PPIs prediction based on biological experiments are expensive. Recently, a lot of computation-based methods have been developed and made great progress. However, current computational methods only focus on one form of protein, using only protein spatial conformation or primary sequence. And, the protein's natural hierarchical structure is ignored.Results
In this study, we propose a novel network architecture, HSSPPI, through hierarchical and spatial-sequential modeling of protein for PPIs prediction. In this network, we represent protein as a hierarchical graph, in which a node in the p"],"journal":["Briefings in bioinformatics"],"pubmed_title":["HSSPPI: hierarchical and spatial-sequential modeling for PPIs prediction."],"pmcid":["PMC11879409"],"funding_grant_id":["241111210500","201400210400","232300421117","252102211042","62371423 and 61572444"],"pubmed_authors":["Tian Z","Li Y","Zhou Q","Lu S","Nan X","Zhang S"],"additional_accession":[]},"is_claimable":false,"name":"HSSPPI: hierarchical and spatial-sequential modeling for PPIs prediction.","description":"Motivation
Protein-protein interactions play a fundamental role in biological systems. Accurate detection of protein-protein interaction sites (PPIs) remains a challenge. And, the methods of PPIs prediction based on biological experiments are expensive. Recently, a lot of computation-based methods have been developed and made great progress. However, current computational methods only focus on one form of protein, using only protein spatial conformation or primary sequence. And, the protein's natural hierarchical structure is ignored.Results
In this study, we propose a novel network architecture, HSSPPI, through hierarchical and spatial-sequential modeling of protein for PPIs prediction. In this network, we represent protein as a hierarchical graph, in which a node in the p","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2026-06-01T11:16:14.747Z","creation":"2025-04-07T11:30:56.959Z"},"accession":"S-EPMC11879409","cross_references":{"pubmed":["40037640"],"doi":["10.1093/bib/bbaf079"]}}