{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Pennica C"],"funding":["Wellcome Trust","Biotechnology and Biological Sciences Research Council"],"pagination":["168060"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7617523"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["435(14)"],"pubmed_abstract":["In 2019, we released Missense3D which identifies stereochemical features that are disrupted by a missense variant, such as introducing a buried charge. Missense3D analyses the effect of a missense variant on a single structure and thus may fail to identify as damaging surface variants disrupting a protein interface i.e., a protein-protein interaction (PPI) site. Here we present Missense3D-PPI designed to predict missense variants at PPI interfaces. Our development dataset comprised of 1,279 missense variants (pathogenic n = 733, benign n = 546) in 434 proteins and 545 experimental structures of PPI complexes. Benchmarking of Missense3D-PPI was performed after dividing the dataset in training (320 benign and 320 pathogenic variants) and testing (226 benign and 413 pathogenic). Structural fe"],"journal":["Journal of molecular biology"],"pubmed_title":["Missense3D-PPI: A Web Resource to Predict the Impact of Missense Variants at Protein Interfaces Using 3D Structural Data."],"pmcid":["PMC7617523"],"funding_grant_id":["BB/T010487/1","218242","BB/P023959/1","218242/Z/19/Z","104955","104955/Z/14/Z"],"pubmed_authors":["Islam SA","Pennica C","Hanna G","David A","Sternberg MJE"],"additional_accession":[]},"is_claimable":false,"name":"Missense3D-PPI: A Web Resource to Predict the Impact of Missense Variants at Protein Interfaces Using 3D Structural Data.","description":"In 2019, we released Missense3D which identifies stereochemical features that are disrupted by a missense variant, such as introducing a buried charge. Missense3D analyses the effect of a missense variant on a single structure and thus may fail to identify as damaging surface variants disrupting a protein interface i.e., a protein-protein interaction (PPI) site. Here we present Missense3D-PPI designed to predict missense variants at PPI interfaces. Our development dataset comprised of 1,279 missense variants (pathogenic n = 733, benign n = 546) in 434 proteins and 545 experimental structures of PPI complexes. Benchmarking of Missense3D-PPI was performed after dividing the dataset in training (320 benign and 320 pathogenic variants) and testing (226 benign and 413 pathogenic). Structural fe","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jul","modification":"2026-06-02T12:01:24.567Z","creation":"2025-07-10T03:08:32.272Z"},"accession":"S-EPMC7617523","cross_references":{"pubmed":["37356905"],"doi":["10.1016/j.jmb.2023.168060"]}}