{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T19:58:13Z","timestamp":1775764693510,"version":"3.50.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["2R01GM098977"],"award-info":[{"award-number":["2R01GM098977"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Protein\u2013protein interactions drive wide-ranging molecular processes, and characterizing at the atomic level how proteins interact (beyond just the fact that they interact) can provide key insights into understanding and controlling this machinery. Unfortunately, experimental determination of three-dimensional protein complex structures remains difficult and does not scale to the increasingly large sets of proteins whose interactions are of interest. Computational methods are thus required to meet the demands of large-scale, high-throughput prediction of how proteins interact, but unfortunately, both physical modeling and machine learning methods suffer from poor precision and\/or recall.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In order to improve performance in predicting protein interaction interfaces, we leverage the best properties of both data- and physics-driven methods to develop a unified Geometric Deep Neural Network, \u2018PInet\u2019 (Protein Interface Network). PInet consumes pairs of point clouds encoding the structures of two partner proteins, in order to predict their structural regions mediating interaction. To make such predictions, PInet learns and utilizes models capturing both geometrical and physicochemical molecular surface complementarity. In application to a set of benchmarks, PInet simultaneously predicts the interface regions on both interacting proteins, achieving performance equivalent to or even much better than the state-of-the-art predictor for each dataset. Furthermore, since PInet is based on joint segmentation of a representation of a protein surfaces, its predictions are meaningful in terms of the underlying physical complementarity driving molecular recognition.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>PInet scripts and models are available at https:\/\/github.com\/FTD007\/PInet.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab154","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T20:12:06Z","timestamp":1614715926000},"page":"2580-2588","source":"Crossref","is-referenced-by-count":107,"title":["Protein interaction interface region prediction by geometric deep learning"],"prefix":"10.1093","volume":"37","author":[{"given":"Bowen","family":"Dai","sequence":"first","affiliation":[{"name":"Computer Science Department, Dartmouth , Hanover, NH 03755, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1860-0912","authenticated-orcid":false,"given":"Chris","family":"Bailey-Kellogg","sequence":"additional","affiliation":[{"name":"Computer Science Department, Dartmouth , Hanover, NH 03755, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"2023051609205830500_btab154-B1","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1002\/prot.24479","article-title":"Pairpred: partner-specific prediction of interacting residues from sequence and structure","volume":"82","author":"Afsar Minhas","year":"2014","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023051609205830500_btab154-B2","author":"Bahdanau","year":"2014"},{"key":"2023051609205830500_btab154-B3","doi-asserted-by":"crossref","first-page":"10037","DOI":"10.1073\/pnas.181342398","article-title":"Electrostatics of nanosystems: application to microtubules and the ribosome","volume":"98","author":"Baker","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051609205830500_btab154-B4","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1107\/S0907444902003451","article-title":"The protein data bank","volume":"58","author":"Berman","year":"2002","journal-title":"Acta Crystallogr. D Biol. Crystallogr"},{"key":"2023051609205830500_btab154-B5","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1038\/s41586-019-0879-y","article-title":"Commonality despite exceptional diversity in the baseline human antibody repertoire","volume":"566","author":"Briney","year":"2019","journal-title":"Nature"},{"key":"2023051609205830500_btab154-B6","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1093\/bioinformatics\/btg371","article-title":"Cluspro: an automated docking and discrimination method for the prediction of protein complexes","volume":"20","author":"Comeau","year":"2004","journal-title":"Bioinformatics"},{"key":"2023051609205830500_btab154-B7","author":"DeLano","year":"2002"},{"key":"2023051609205830500_btab154-B8","doi-asserted-by":"crossref","first-page":"W522","DOI":"10.1093\/nar\/gkm276","article-title":"Pdb2pqr: expanding and upgrading automated preparation of biomolecular structures for molecular simulations","volume":"35","author":"Dolinsky","year":"2007","journal-title":"Nucleic Acids Res"},{"key":"2023051609205830500_btab154-B9","doi-asserted-by":"crossref","first-page":"D1140","DOI":"10.1093\/nar\/gkt1043","article-title":"Sabdab: the structural antibody database","volume":"42","author":"Dunbar","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023051609205830500_btab154-B10","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/nbt785","article-title":"Flow-cytometric isolation of human antibodies from a nonimmune Saccharomyces cerevisiae surface display library","volume":"21","author":"Feldhaus","year":"2003","journal-title":"Nat. Biotechnol"},{"key":"2023051609205830500_btab154-B11","first-page":"6530","author":"Fout","year":"2017"},{"key":"2023051609205830500_btab154-B12","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1038\/s41592-019-0666-6","article-title":"Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning","volume":"17","author":"Gainza","year":"2020","journal-title":"Nat. Methods"},{"key":"2023051609205830500_btab154-B13","doi-asserted-by":"crossref","first-page":"e29023","DOI":"10.7554\/eLife.29023","article-title":"Computationally-driven identification of antibody epitopes","volume":"6","author":"Hua","year":"2017","journal-title":"Elife"},{"key":"2023051609205830500_btab154-B14","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1093\/bioinformatics\/btq003","article-title":"Cd-hit suite: a web server for clustering and comparing biological sequences","volume":"26","author":"Huang","year":"2010","journal-title":"Bioinformatics"},{"key":"2023051609205830500_btab154-B15","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1002\/prot.22106","article-title":"Protein\u2013protein docking benchmark version 3.0","volume":"73","author":"Hwang","year":"2008","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023051609205830500_btab154-B16","first-page":"2017","author":"Jaderberg","year":"2015"},{"key":"2023051609205830500_btab154-B17","author":"Kipf","year":"2017"},{"key":"2023051609205830500_btab154-B18","doi-asserted-by":"crossref","first-page":"2288","DOI":"10.1093\/bioinformatics\/btu190","article-title":"Improving b-cell epitope prediction and its application to global antibody-antigen docking","volume":"30","author":"Krawczyk","year":"2014","journal-title":"Bioinformatics"},{"key":"2023051609205830500_btab154-B19","doi-asserted-by":"crossref","first-page":"e1002829","DOI":"10.1371\/journal.pcbi.1002829","article-title":"Reliable b cell epitope predictions: impacts of method development and improved benchmarking","volume":"8","author":"Kringelum","year":"2012","journal-title":"PLoS Comput. Biol"},{"key":"2023051609205830500_btab154-B20","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/0022-2836(82)90515-0","article-title":"A simple method for displaying the hydropathic character of a protein","volume":"157","author":"Kyte","year":"1982","journal-title":"J. Mol. Biol"},{"key":"2023051609205830500_btab154-B21","first-page":"946","author":"Lawrence","year":"1993"},{"key":"2023051609205830500_btab154-B22","first-page":"922","author":"Maturana","year":"2015"},{"key":"2023051609205830500_btab154-B23","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1093\/bioinformatics\/btq302","article-title":"Applying the na\u00efve Bayes classifier with kernel density estimation to the prediction of protein\u2013protein interaction sites","volume":"26","author":"Murakami","year":"2010","journal-title":"Bioinformatics"},{"key":"2023051609205830500_btab154-B24","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/S0022-2836(05)80134-2","article-title":"Scop: a structural classification of proteins database for the investigation of sequences and structures","volume":"247","author":"Murzin","year":"1995","journal-title":"J. Mol. Biol"},{"key":"2023051609205830500_btab154-B25","doi-asserted-by":"crossref","first-page":"W331","DOI":"10.1093\/nar\/gki585","article-title":"Prism: protein interactions by structural matching","volume":"33","author":"Ogmen","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2023051609205830500_btab154-B26","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1145\/571647.571648","article-title":"Shape distributions","volume":"21","author":"Osada","year":"2002","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"2023051609205830500_btab154-B27","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1093\/bioinformatics\/btu097","article-title":"Zdock server: interactive docking prediction of protein\u2013protein complexes and symmetric multimers","volume":"30","author":"Pierce","year":"2014","journal-title":"Bioinformatics"},{"key":"2023051609205830500_btab154-B28","doi-asserted-by":"crossref","first-page":"3996","DOI":"10.1093\/bioinformatics\/btaa263","article-title":"Learning context-aware structural representations to predict antigen and antibody binding interfaces","volume":"36","author":"Pittala","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051609205830500_btab154-B29","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1002\/prot.21248","article-title":"Prediction-based fingerprints of protein\u2013protein interactions","volume":"66","author":"Porollo","year":"2007","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023051609205830500_btab154-B30","first-page":"652","author":"Qi","year":"2017"},{"key":"2023051609205830500_btab154-B31","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1093\/bioinformatics\/bty647","article-title":"Bipspi: a method for the prediction of partner-specific protein\u2013protein interfaces","volume":"35","author":"Sanchez-Garcia","year":"2019","journal-title":"Bioinformatics"},{"key":"2023051609205830500_btab154-B32","doi-asserted-by":"crossref","first-page":"W363","DOI":"10.1093\/nar\/gki481","article-title":"Patchdock and symmdock: servers for rigid and symmetric docking","volume":"33","author":"Schneidman-Duhovny","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2023051609205830500_btab154-B33","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.str.2014.02.003","article-title":"Using a combined computational-experimental approach to predict antibody-specific b cell epitopes","volume":"22","author":"Sela-Culang","year":"2014","journal-title":"Structure"},{"key":"2023051609205830500_btab154-B34","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.coviro.2015.03.012","article-title":"Antibody specific epitope prediction\u2014emergence of a new paradigm","volume":"11","author":"Sela-Culang","year":"2015","journal-title":"Curr. Opin. Virol"},{"key":"2023051609205830500_btab154-B35","doi-asserted-by":"crossref","first-page":"e42","DOI":"10.1371\/journal.pcbi.0030042","article-title":"Deciphering protein\u2013protein interactions. Part I. Experimental techniques and databases","volume":"3","author":"Shoemaker","year":"2007","journal-title":"PLoS Comput. Biol"},{"key":"2023051609205830500_btab154-B36","first-page":"15642","author":"Townshend","year":"2019"},{"key":"2023051609205830500_btab154-B37","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1002\/prot.25219","article-title":"New additions to the clusPro server motivated by CAPRI","volume":"85","author":"Vajda","year":"2017","journal-title":"Proteins Struct. Funct. Bioinf"},{"key":"2023051609205830500_btab154-B38","author":"Vinyals","year":"2015"},{"key":"2023051609205830500_btab154-B39","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1016\/j.jmb.2015.07.016","article-title":"Updates to the integrated protein\u2013protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2","volume":"427","author":"Vreven","year":"2015","journal-title":"J. Mol. Biol"},{"key":"2023051609205830500_btab154-B40","doi-asserted-by":"crossref","first-page":"4111","DOI":"10.1021\/jm048957q","article-title":"The pdbbind database: methodologies and updates","volume":"48","author":"Wang","year":"2005","journal-title":"J. Med. Chem"},{"key":"2023051609205830500_btab154-B41","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1038\/nprot.2016.180","article-title":"Modeling and docking of antibody structures with Rosetta","volume":"12","author":"Weitzner","year":"2017","journal-title":"Nat. Protoc"},{"key":"2023051609205830500_btab154-B42","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1038\/s41596-020-0312-x","article-title":"The hdock server for integrated protein\u2013protein docking","volume":"15","author":"Yan","year":"2020","journal-title":"Nat. Protoc"},{"key":"2023051609205830500_btab154-B43","doi-asserted-by":"crossref","first-page":"W432","DOI":"10.1093\/nar\/gky420","article-title":"Complexcontact: a web server for inter-protein contact prediction using deep learning","volume":"46","author":"Zeng","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023051609205830500_btab154-B44","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1038\/nature11503","article-title":"Structure-based prediction of protein\u2013protein interactions on a genome-wide scale","volume":"490","author":"Zhang","year":"2012","journal-title":"Nature"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab154\/36683666\/btab154.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/17\/2580\/50339126\/btab154.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/17\/2580\/50339126\/btab154.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T09:24:50Z","timestamp":1684229090000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/17\/2580\/6162157"}},"subtitle":[],"editor":[{"given":"Yann","family":"Ponty","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":44,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2021,9,9]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab154","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,9,1]]},"published":{"date-parts":[[2021,3,8]]}}}