<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Pennica C</submitter><funding>Wellcome Trust</funding><funding>Biotechnology and Biological Sciences Research Council</funding><pagination>168060</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7617523</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>435(14)</volume><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</pubmed_abstract><journal>Journal of molecular biology</journal><pubmed_title>Missense3D-PPI: A Web Resource to Predict the Impact of Missense Variants at Protein Interfaces Using 3D Structural Data.</pubmed_title><pmcid>PMC7617523</pmcid><funding_grant_id>BB/T010487/1</funding_grant_id><funding_grant_id>218242</funding_grant_id><funding_grant_id>BB/P023959/1</funding_grant_id><funding_grant_id>218242/Z/19/Z</funding_grant_id><funding_grant_id>104955</funding_grant_id><funding_grant_id>104955/Z/14/Z</funding_grant_id><pubmed_authors>Islam SA</pubmed_authors><pubmed_authors>Pennica C</pubmed_authors><pubmed_authors>Hanna G</pubmed_authors><pubmed_authors>David A</pubmed_authors><pubmed_authors>Sternberg MJE</pubmed_authors></additional><is_claimable>false</is_claimable><name>Missense3D-PPI: A Web Resource to Predict the Impact of Missense Variants at Protein Interfaces Using 3D Structural Data.</name><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</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Jul</publication><modification>2026-06-02T12:01:24.567Z</modification><creation>2025-07-10T03:08:32.272Z</creation></dates><accession>S-EPMC7617523</accession><cross_references><pubmed>37356905</pubmed><doi>10.1016/j.jmb.2023.168060</doi></cross_references></HashMap>