{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:27:49Z","timestamp":1774380469534,"version":"3.50.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976001"],"award-info":[{"award-number":["61976001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Projects of University Excellent Talents Support Plan of Anhui Provincial Department of Education","award":["gxyqZD2021089"],"award-info":[{"award-number":["gxyqZD2021089"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>In recent years, the end-to-end deep learning method for single-chain protein structure prediction has achieved high accuracy. For example, the state-of-the-art method AlphaFold, developed by Google, has largely increased the accuracy of protein structure predictions to near experimental accuracy in some of the cases. At the same time, there are few methods that can evaluate the quality of protein complexes at the residue level. In particular, evaluating the quality of residues at the interface of protein complexes can lead to a wide range of applications, such as protein function analysis and drug design. In this paper, we introduce a new deep graph neural network-based method ComplexQA, to evaluate the local quality of interfaces for protein complexes by utilizing the residue-level structural information in 3D space and the sequence-level constraints.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We benchmark our method to other state-of-the-art quality assessment approaches on the HAF2 and DBM55-AF2 datasets (high-quality structural models predicted by AlphaFold-Multimer), and the BM5 docking dataset. The experimental results show that our proposed method achieves better or similar performance compared with other state-of-the-art methods, especially on difficult targets which only contain a few acceptable models. Our method is able to suggest a score for each interfac e residue, which demonstrates a powerful assessment tool for the ever-increasing number of protein complexes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability<\/jats:title>\n                  <jats:p>https:\/\/github.com\/Cao-Labs\/ComplexQA.git. Contact: caora@plu.edu<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbad287","type":"journal-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T12:24:45Z","timestamp":1699273485000},"source":"Crossref","is-referenced-by-count":10,"title":["ComplexQA: a deep graph learning approach for protein complex structure assessment"],"prefix":"10.1093","volume":"24","author":[{"given":"Lei","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, AnHui University , Hefei, 230601, Anhui , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, AnHui University , Hefei, 230601, Anhui , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Saint Louis University , Saint. Louis, 63103, MO , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Si","sequence":"additional","affiliation":[{"name":"Division of Computing and Software Systems, University of Washington Bothell , Bothell, 98011, WA , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, AnHui University , Hefei, 230601, Anhui , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renzhi","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Humanities, Pacific Lutheran University , Tacoma, 98447, WA , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"issue":"1","key":"2023110612244151400_ref1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/nmeth.2289","article-title":"Interactome3d: adding structural details to protein networks","volume":"10","author":"Mosca","year":"2013","journal-title":"Nat Methods"},{"key":"2023110612244151400_ref2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/978-1-4939-0366-5_2","article-title":"Raptorx server: a resource for template-based protein structure modeling","author":"K\u00e4llberg","year":"2014","journal-title":"Methods Mol Biol"},{"issue":"12","key":"2023110612244151400_ref3","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1002\/prot.25697","article-title":"Protein tertiary structure modeling driven by deep learning and contact distance prediction in casp13","volume":"87","author":"Hou","year":"2019","journal-title":"Proteins"},{"key":"2023110612244151400_ref4","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/978-1-0716-0708-4_2","article-title":"The multicom protein structure prediction server empowered by deep learning and contact distance prediction","author":"Hou","year":"2020","journal-title":"Methods Mol Biol"},{"key":"2023110612244151400_ref5","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1002\/prot.25007","article-title":"Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: a casp-capri experiment","volume":"84","author":"Lensink","year":"2016","journal-title":"Proteins"},{"issue":"9","key":"2023110612244151400_ref6","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1038\/nprot.2011.367","article-title":"Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using prism","volume":"6","author":"Tuncbag","year":"2011","journal-title":"Nat Protoc"},{"issue":"9","key":"2023110612244151400_ref7","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1002\/prot.24736","article-title":"Structural templates for comparative protein docking","volume":"83","author":"Anishchenko","year":"2015","journal-title":"Proteins"},{"issue":"12","key":"2023110612244151400_ref8","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"},{"issue":"W1","key":"2023110612244151400_ref9","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"},{"issue":"suppl_2","key":"2023110612244151400_ref10","doi-asserted-by":"crossref","first-page":"W233","DOI":"10.1093\/nar\/gkn216","article-title":"The rosettadock server for local protein\u2013protein docking","volume":"36","author":"Lyskov","year":"2008","journal-title":"Nucleic Acids Res"},{"issue":"W1","key":"2023110612244151400_ref11","doi-asserted-by":"crossref","first-page":"W542","DOI":"10.1093\/nar\/gkw340","article-title":"Interevdock: a docking server to predict the structure of protein\u2013protein interactions using evolutionary information","volume":"44","author":"Jinchao","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"2023110612244151400_ref12","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1016\/j.str.2019.03.018","article-title":"Coupling molecular dynamics and deep learning to mine protein conformational space","volume":"27","author":"Degiacomi","year":"2019","journal-title":"Structure"},{"issue":"2","key":"2023110612244151400_ref13","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"},{"issue":"7873","key":"2023110612244151400_ref14","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":"2023110612244151400_ref15","first-page":"2021","article-title":"Protein complex prediction with alphafold-multimer","author":"Evans","year":"2022","journal-title":"BioRxiv"},{"issue":"1","key":"2023110612244151400_ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms100","article-title":"Af2complex predicts direct physical interactions in multimeric proteins with deep learning","volume":"13","author":"Gao","year":"2022","journal-title":"Nat Commun"},{"issue":"1","key":"2023110612244151400_ref17","first-page":"1","article-title":"Improved prediction of protein-protein interactions using alphafold2","volume":"13","author":"Bryant","year":"2022","journal-title":"Nat Commun"},{"key":"2023110612244151400_ref18","first-page":"1","article-title":"Colabfold: making protein folding accessible to all","author":"Mirdita","year":"2022","journal-title":"Nat Methods"},{"issue":"3","key":"2023110612244151400_ref19","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1093\/bioinformatics\/btaa714","article-title":"Graphqa: protein model quality assessment using graph convolutional networks","volume":"37","author":"Baldassarre","year":"2021","journal-title":"Bioinformatics"},{"issue":"1","key":"2023110612244151400_ref20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-016-1405-y","article-title":"Deepqa: improving the estimation of single protein model quality with deep belief networks","volume":"17","author":"Cao","year":"2016","journal-title":"BMC Bioinformatics"},{"issue":"16","key":"2023110612244151400_ref21","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1093\/bioinformatics\/btx222","article-title":"Svmqa: support\u2013vector-machine-based protein single-model quality assessment","volume":"33","author":"Manavalan","year":"2017","journal-title":"Bioinformatics"},{"issue":"1","key":"2023110612244151400_ref22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-15-120","article-title":"Smoq: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines","volume":"15","author":"Cao","year":"2014","journal-title":"BMC Bioinformatics"},{"issue":"6","key":"2023110612244151400_ref23","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1002\/prot.25278","article-title":"Voromqa: assessment of protein structure quality using interatomic contact areas","volume":"85","author":"Olechnovi\u010d","year":"2017","journal-title":"Proteins"},{"issue":"12","key":"2023110612244151400_ref24","doi-asserted-by":"crossref","first-page":"i262","DOI":"10.1093\/bioinformatics\/btw257","article-title":"Finding correct protein\u2013protein docking models using proqdock","volume":"32","author":"Basu","year":"2016","journal-title":"Bioinformatics"},{"issue":"7","key":"2023110612244151400_ref25","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1093\/bioinformatics\/btz870","article-title":"Protein docking model evaluation by 3d deep convolutional neural networks","volume":"36","author":"Wang","year":"2020","journal-title":"Bioinformatics"},{"issue":"1","key":"2023110612244151400_ref26","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":"2023110612244151400_ref27","first-page":"402","article-title":"Protein docking model evaluation by graph neural networks","author":"Wang","year":"2021","journal-title":"Front Mol Biosci"},{"issue":"Supplement_1","key":"2023110612244151400_ref28","doi-asserted-by":"crossref","first-page":"i308","DOI":"10.1093\/bioinformatics\/btad203","article-title":"A gated graph transformer for protein complex structure quality assessment and its performance in casp15","volume":"39","author":"Chen","year":"2023","journal-title":"Bioinformatics"},{"issue":"9","key":"2023110612244151400_ref29","doi-asserted-by":"crossref","first-page":"2444","DOI":"10.1093\/bioinformatics\/btac120","article-title":"Trscore: a 3d repvgg-based scoring method for ranking protein docking models","volume":"38","author":"Guo","year":"2022","journal-title":"Bioinformatics"},{"issue":"suppl_2","key":"2023110612244151400_ref30","doi-asserted-by":"crossref","first-page":"W310","DOI":"10.1093\/nar\/gkl206","article-title":"Gramm-x public web server for protein\u2013protein docking","volume":"34","author":"Tovchigrechko","year":"2006","journal-title":"Nucleic Acids Res"},{"issue":"9","key":"2023110612244151400_ref31","doi-asserted-by":"crossref","first-page":"e24657","DOI":"10.1371\/journal.pone.0024657","article-title":"Accelerating protein docking in zdock using an advanced 3d convolution library","volume":"6","author":"Pierce","year":"2011","journal-title":"PloS One"},{"issue":"1","key":"2023110612244151400_ref32","doi-asserted-by":"crossref","first-page":"bbab384","DOI":"10.1093\/bib\/bbab384","article-title":"Zoomqa: residue-level protein model accuracy estimation with machine learning on sequential and 3d structural features","volume":"23","author":"Hippe","year":"2022","journal-title":"Brief Bioinform"},{"issue":"21","key":"2023110612244151400_ref33","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.1093\/bioinformatics\/btt473","article-title":"Lddt: a local superposition-free score for comparing protein structures and models using distance difference tests","volume":"29","author":"Mariani","year":"2013","journal-title":"Bioinformatics"},{"key":"2023110612244151400_ref34","article-title":"Dproq: a gated-graph transformer for protein complex structure assessment","author":"Chen","year":"2022"},{"issue":"22","key":"2023110612244151400_ref35","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1093\/bioinformatics\/btn497","article-title":"Dockground protein\u2013protein docking decoy set","volume":"24","author":"Liu","year":"2008","journal-title":"Bioinformatics"},{"issue":"1","key":"2023110612244151400_ref36","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The protein data bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res"},{"key":"2023110612244151400_ref37","article-title":"Docking models for docking benchmark 4, 5 and capri score_set","author":"Geng","year":"2019"},{"issue":"19","key":"2023110612244151400_ref38","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"},{"issue":"6","key":"2023110612244151400_ref39","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1093\/bioinformatics\/btt038","article-title":"Swarmdock: a server for flexible protein\u2013protein docking","volume":"29","author":"Torchala","year":"2013","journal-title":"Bioinformatics"},{"key":"2023110612244151400_ref40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3535508.3545558","article-title":"Deep graph learning to estimate protein model quality using structural constraints from multiple sequence alignments","volume-title":"Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","author":"Rahbar","year":"2022"},{"key":"2023110612244151400_ref41","article-title":"Zoomscore: residue-level protein complex assessment with machine learning on sequential and 3d structural features","author":"Wang","year":"2022","journal-title":"CASP15 Abstract Book"},{"issue":"11","key":"2023110612244151400_ref42","doi-asserted-by":"crossref","first-page":"3163","DOI":"10.1002\/prot.24678","article-title":"Score_set: a capri benchmark for scoring protein complexes","volume":"82","author":"Lensink","year":"2014","journal-title":"Proteins"},{"issue":"8","key":"2023110612244151400_ref43","doi-asserted-by":"crossref","first-page":"e0161879","DOI":"10.1371\/journal.pone.0161879","article-title":"Dockq: a quality measure for protein-protein docking models","volume":"11","author":"Basu","year":"2016","journal-title":"PloS One"},{"issue":"8","key":"2023110612244151400_ref44","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1016\/j.bpj.2011.09.012","article-title":"Goap: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction","volume":"101","author":"Zhou","year":"2011","journal-title":"Biophys J"},{"issue":"1","key":"2023110612244151400_ref45","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1002\/prot.21920","article-title":"A combination of rescoring and refinement significantly improves protein docking performance","volume":"72","author":"Pierce","year":"2008","journal-title":"Proteins"},{"issue":"2","key":"2023110612244151400_ref46","doi-asserted-by":"crossref","first-page":"e2017525118","DOI":"10.1073\/pnas.2017525118","article-title":"Deeptracer for fast de novo cryo-em protein structure modeling and special studies on cov-related complexes","volume":"118","author":"Pfab","year":"2021","journal-title":"Proc Natl Acad Sci"},{"issue":"2","key":"2023110612244151400_ref47","doi-asserted-by":"crossref","first-page":"e1542","DOI":"10.1002\/wcms.1542","article-title":"Artificial intelligence advances for de novo molecular structure modeling in cryo-electron microscopy","volume":"12","author":"Si","year":"2022","journal-title":"Wiley Interdiscip Rev Comput Mol Sci"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/6\/bbad287\/52713558\/bbad287.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/6\/bbad287\/52713558\/bbad287.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T12:25:38Z","timestamp":1699273538000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad287\/7334440"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":47,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,9,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad287","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,11,1]]},"published":{"date-parts":[[2023,9,22]]},"article-number":"bbad287"}}