{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:59:06Z","timestamp":1774673946740,"version":"3.50.1"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T00:00:00Z","timestamp":1636502400000},"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":["R01GM089753"],"award-info":[{"award-number":["R01GM089753"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Inter-protein (interfacial) contact prediction is very useful for in silico structural characterization of protein\u2013protein interactions. Although deep learning has been applied to this problem, its accuracy is not as good as intra-protein contact prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose a new deep learning method GLINTER (Graph Learning of INTER-protein contacts) for interfacial contact prediction of dimers, leveraging a rotational invariant representation of protein tertiary structures and a pretrained language model of multiple sequence alignments. Tested on the 13th and 14th CASP-CAPRI datasets, the average top L\/10 precision achieved by GLINTER is 54% on the homodimers and 52% on all the dimers, much higher than 30% obtained by the latest deep learning method DeepHomo on the homodimers and 15% obtained by BIPSPI on all the dimers. Our experiments show that GLINTER-predicted contacts help improve selection of docking decoys.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The software is available at https:\/\/github.com\/zw2x\/glinter. The datasets are available at https:\/\/github.com\/zw2x\/glinter\/data.<\/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\/btab761","type":"journal-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T08:23:59Z","timestamp":1636100639000},"page":"947-953","source":"Crossref","is-referenced-by-count":65,"title":["Deep graph learning of inter-protein contacts"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5986-1150","authenticated-orcid":false,"given":"Ziwei","family":"Xie","sequence":"first","affiliation":[{"name":"Toyota Technological Institute at Chicago , Chicago, IL 60637, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7111-4839","authenticated-orcid":false,"given":"Jinbo","family":"Xu","sequence":"additional","affiliation":[{"name":"Toyota Technological Institute at Chicago , Chicago, IL 60637, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"key":"2023020108524182100_btab761-B1","doi-asserted-by":"crossref","first-page":"e92721","DOI":"10.1371\/journal.pone.0092721","article-title":"Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners","volume":"9","author":"Baldassi","year":"2014","journal-title":"PLoS One"},{"key":"2023020108524182100_btab761-B2","doi-asserted-by":"crossref","first-page":"12180","DOI":"10.1073\/pnas.1606762113","article-title":"Inferring interaction partners from protein sequences","volume":"113","author":"Bitbol","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020108524182100_btab761-B3","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1038\/msb4100203","article-title":"Accurate prediction of protein\u2013protein interactions from sequence alignments using a Bayesian method","volume":"4","author":"Burger","year":"2008","journal-title":"Mol. Syst. Biol"},{"key":"2023020108524182100_btab761-B4","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1126\/science.aaw6718","article-title":"Protein interaction networks revealed by proteome coevolution","volume":"365","author":"Cong","year":"2019","journal-title":"Science"},{"key":"2023020108524182100_btab761-B5","doi-asserted-by":"crossref","first-page":"2580","DOI":"10.1093\/bioinformatics\/btab154","article-title":"Protein interaction interface region prediction by geometric deep learning","volume":"37","author":"Dai","year":"2021","journal-title":"Bioinformatics"},{"key":"2023020108524182100_btab761-B6","year":"2019"},{"key":"2023020108524182100_btab761-B7","author":"Derevyanko","year":"2019"},{"key":"2023020108524182100_btab761-B8","article-title":"Protein interface prediction using graph convolutional networks","author":"Fout","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"2023020108524182100_btab761-B9","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":"2023020108524182100_btab761-B10","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1093\/bioinformatics\/btz496","article-title":"iScore: a novel graph kernel-based function for scoring protein\u2013protein docking models","volume":"36","author":"Geng","year":"2020","journal-title":"Bioinformatics"},{"key":"2023020108524182100_btab761-B11","doi-asserted-by":"crossref","first-page":"12186","DOI":"10.1073\/pnas.1607570113","article-title":"Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis","volume":"113","author":"Gueudr\u00e9","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020108524182100_btab761-B12","doi-asserted-by":"crossref","first-page":"i802","DOI":"10.1093\/bioinformatics\/bty573","article-title":"Predicting protein\u2013protein interactions through sequence-based deep learning","volume":"34","author":"Hashemifar","year":"2018","journal-title":"Bioinformatics"},{"key":"2023020108524182100_btab761-B13","author":"He","year":"2016"},{"key":"2023020108524182100_btab761-B14","doi-asserted-by":"crossref","first-page":"e03430","DOI":"10.7554\/eLife.03430","article-title":"Sequence co-evolution gives 3D contacts and structures of protein complexes","volume":"3","author":"Hopf","year":"2014","journal-title":"eLife"},{"key":"2023020108524182100_btab761-B15","doi-asserted-by":"publisher","author":"Jing","year":"2021","DOI":"10.1038\/s43588-021-00098-9"},{"key":"2023020108524182100_btab761-B16","first-page":"24","article-title":"High accuracy protein structure prediction using deep learning","volume":"22","author":"Jumper","year":"2020","journal-title":"Fourteenth Crit. Assess. Tech. Protein Struct. Predict"},{"key":"2023020108524182100_btab761-B17","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2023020108524182100_btab761-B18","author":"Kingma","year":"2014"},{"key":"2023020108524182100_btab761-B19","doi-asserted-by":"crossref","first-page":"e1004580","DOI":"10.1371\/journal.pcbi.1004580","article-title":"Local geometry and evolutionary conservation of protein surfaces reveal the multiple recognition patches in protein\u2013protein interactions","volume":"11","author":"Laine","year":"2015","journal-title":"PLoS Comput. Biol"},{"key":"2023020108524182100_btab761-B20","doi-asserted-by":"crossref","first-page":"e155","DOI":"10.1371\/journal.pcbi.0020155","article-title":"3D complex: a structural classification of protein complexes","volume":"2","author":"Levy","year":"2006","journal-title":"PLoS Comput. Biol"},{"key":"2023020108524182100_btab761-B21","doi-asserted-by":"crossref","first-page":"189","DOI":"10.12688\/f1000research.7931.1","article-title":"FreeSASA: an open source C library for solvent accessible surface area calculations","volume":"5","author":"Mitternacht","year":"2016","journal-title":"F1000Res"},{"key":"2023020108524182100_btab761-B22","doi-asserted-by":"crossref","first-page":"e83","DOI":"10.1093\/nar\/gkp318","article-title":"MM-align: a quick algorithm for aligning multiple-chain protein complex structures using iterative dynamic programming","volume":"37","author":"Mukherjee","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2023020108524182100_btab761-B23","doi-asserted-by":"crossref","first-page":"e02030","DOI":"10.7554\/eLife.02030","article-title":"Robust and accurate prediction of residue\u2013residue interactions across protein interfaces using evolutionary information","volume":"3","author":"Ovchinnikov","year":"2014","journal-title":"eLife"},{"key":"2023020108524182100_btab761-B24","doi-asserted-by":"crossref","first-page":"3313","DOI":"10.1093\/bioinformatics\/btz122","article-title":"Protein model quality assessment using 3D oriented convolutional neural networks","volume":"35","author":"Pag\u00e9s","year":"2019","journal-title":"Bioinformatics"},{"key":"2023020108524182100_btab761-B25","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":"2023020108524182100_btab761-B26","doi-asserted-by":"publisher","author":"Quadir","year":"2021","DOI":"10.1038\/s41598-021-91827-7"},{"key":"2023020108524182100_btab761-B27","author":"Rao","year":"2021"},{"key":"2023020108524182100_btab761-B28","doi-asserted-by":"crossref","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020108524182100_btab761-B29","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1002\/(SICI)1097-0282(199603)38:3<305::AID-BIP4>3.0.CO;2-Y","article-title":"Reduced surface: an efficient way to compute molecular surfaces","volume":"38","author":"Sanner","year":"1996","journal-title":"Biopolymers"},{"key":"2023020108524182100_btab761-B30","author":"Sanyal","year":"2020"},{"key":"2023020108524182100_btab761-B31","doi-asserted-by":"crossref","first-page":"3128","DOI":"10.1093\/bioinformatics\/btu500","article-title":"CCMpred\u2013fast and precise prediction of protein residue\u2013residue contacts from correlated mutations","volume":"30","author":"Seemayer","year":"2014","journal-title":"Bioinformatics"},{"key":"2023020108524182100_btab761-B32","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1038\/s41586-019-1923-7","article-title":"Improved protein structure prediction using potentials from deep learning","volume":"577","author":"Senior","year":"2020","journal-title":"Nature"},{"key":"2023020108524182100_btab761-B33","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1186\/s12859-019-3019-7","article-title":"HH-suite3 for fast remote homology detection and deep protein annotation","volume":"20","author":"Steinegger","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2023020108524182100_btab761-B34","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1093\/bioinformatics\/btaa003","article-title":"UDSMProt: universal deep sequence models for protein classification","volume":"36","author":"Strodthoff","year":"2020","journal-title":"Bioinformatics"},{"key":"2023020108524182100_btab761-B35","first-page":"15272","author":"Sverrisson","year":"2020"},{"key":"2023020108524182100_btab761-B36","article-title":"End-to-end learning on 3D protein structure for interface prediction","volume":"32","author":"Townshend","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"2023020108524182100_btab761-B37","doi-asserted-by":"crossref","first-page":"E2662","DOI":"10.1073\/pnas.1615068114","article-title":"Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis","volume":"114","author":"Uguzzoni","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020108524182100_btab761-B38","doi-asserted-by":"crossref","first-page":"e07454","DOI":"10.7554\/eLife.07454","article-title":"Contacts-based prediction of binding affinity in protein\u2013protein complexes","volume":"4","author":"Vangone","year":"2015","journal-title":"eLife"},{"key":"2023020108524182100_btab761-B39","doi-asserted-by":"crossref","first-page":"e1005324","DOI":"10.1371\/journal.pcbi.1005324","article-title":"Accurate de novo prediction of protein contact map by ultra-deep learning model","volume":"13","author":"Wang","year":"2017","journal-title":"PLoS Comput. Biol"},{"key":"2023020108524182100_btab761-B40","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1073\/pnas.0805923106","article-title":"Identification of direct residue contacts in protein-protein interaction by message passing","volume":"106","author":"Weigt","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020108524182100_btab761-B41","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1006\/jmbi.1998.2401","article-title":"Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation 1 1Edited by J","volume":"285","author":"Word","year":"1999","journal-title":"Thornton. J. Mol. Biol"},{"key":"2023020108524182100_btab761-B42","doi-asserted-by":"crossref","first-page":"16856","DOI":"10.1073\/pnas.1821309116","article-title":"Distance-based protein folding powered by deep learning","volume":"116","author":"Xu","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023020108524182100_btab761-B43","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1038\/s42256-021-00348-5","article-title":"Improved protein structure prediction by deep learning irrespective of co-evolution information","volume":"3","author":"Xu","year":"2021","journal-title":"Nature Machine Intelligence"},{"key":"2023020108524182100_btab761-B44","doi-asserted-by":"crossref","first-page":"W365","DOI":"10.1093\/nar\/gkx407","article-title":"HDOCK: a web server for protein\u2013protein and protein\u2013DNA\/RNA docking based on a hybrid strategy","volume":"45","author":"Yan","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023020108524182100_btab761-B45","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab038","article-title":"Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes","author":"Yan","year":"2021","journal-title":"Brief. Bioinform,"},{"key":"2023020108524182100_btab761-B46","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":"2023020108524182100_btab761-B47","doi-asserted-by":"crossref","first-page":"2302","DOI":"10.1093\/nar\/gki524","article-title":"TM-align: a protein structure alignment algorithm based on the TM-score","volume":"33","author":"Zhang","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2023020108524182100_btab761-B48","author":"Zhou","year":"2018"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab761\/41121423\/btab761.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/4\/947\/49008498\/btab761.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/4\/947\/49008498\/btab761.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T15:12:56Z","timestamp":1675264376000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/4\/947\/6424887"}},"subtitle":[],"editor":[{"given":"Jan","family":"Gorodkin","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,11,10]]},"references-count":48,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,1,27]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab761","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.08.14.456342","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,2,15]]},"published":{"date-parts":[[2021,11,10]]}}}