{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T21:04:59Z","timestamp":1766610299352,"version":"3.41.2"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,11,28]],"date-time":"2021-11-28T00:00:00Z","timestamp":1638057600000},"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\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["62173304","61773346"],"award-info":[{"award-number":["62173304","61773346"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Project of Zhejiang Provincial Natural Science Foundation of China","award":["LZ20F030002"],"award-info":[{"award-number":["LZ20F030002"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFE0126100"],"award-info":[{"award-number":["2019YFE0126100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Meta contact, which combines different contact maps into one to improve contact prediction accuracy and effectively reduce the noise from a single contact map, is a widely used method. However, protein structure prediction using meta contact cannot fully exploit the information carried by original contact maps. In this work, a multi contact-based folding method under the evolutionary algorithm framework, MultiCFold, is proposed. In MultiCFold, the thorough information of different contact maps is directly used by populations to guide protein structure folding. In addition, noncontact is considered as an effective supplement to contact information and can further assist protein folding. MultiCFold is tested on a set of 120 nonredundant proteins, and the average TM-score and average RMSD reach 0.617 and 5.815\u00a0\u00c5, respectively. Compared with the meta contact-based method, MetaCFold, average TM-score and average RMSD have a 6.62 and 8.82% improvement. In particular, the import of noncontact information increases the average TM-score by 6.30%. Furthermore, MultiCFold is compared with four state-of-the-art methods of CASP13 on the 24 FM targets, and results show that MultiCFold is significantly better than other methods after the full-atom relax procedure.<\/jats:p>","DOI":"10.1093\/bib\/bbab463","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T19:52:42Z","timestamp":1634154762000},"source":"Crossref","is-referenced-by-count":4,"title":["Multi contact-based folding method for<i>de novo<\/i>protein structure prediction"],"prefix":"10.1093","volume":"23","author":[{"given":"Minghua","family":"Hou","sequence":"first","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunxiang","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaogen","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guijun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,11,28]]},"reference":[{"key":"2022012000290051700_ref1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1126\/science.1065659","article-title":"Protein structure prediction and structural genomics","volume":"294","author":"Baker","year":"2001","journal-title":"Science"},{"key":"2022012000290051700_ref2","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.sbi.2008.02.004","article-title":"Progress and challenges in protein structure prediction","volume":"18","author":"Zhang","year":"2008","journal-title":"Curr Opin Struct Biol"},{"key":"2022012000290051700_ref3","doi-asserted-by":"crossref","first-page":"e1008865","DOI":"10.1371\/journal.pcbi.1008865","article-title":"Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks","volume":"17","author":"Li","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"2022012000290051700_ref4","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1002\/prot.25423","article-title":"Assessment of hard target modeling in CASP12 reveals an emerging role of alignment-based contact prediction methods","volume":"86","author":"Abriata","year":"2018","journal-title":"Proteins"},{"key":"2022012000290051700_ref5","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1002\/prot.25407","article-title":"Assessment of contact predictions in CASP12: co-evolution and deep learning coming of age","volume":"86","author":"Schaarschmidt","year":"2018","journal-title":"Proteins"},{"key":"2022012000290051700_ref6","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1002\/prot.25819","article-title":"Assessing the accuracy of contact predictions in CASP13","volume":"87","author":"Shrestha","year":"2019","journal-title":"Proteins"},{"key":"2022012000290051700_ref7","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1002\/prot.25792","article-title":"Deep-learning contact-map guided protein structure prediction in CASP13","volume":"87","author":"Zheng","year":"2019","journal-title":"Proteins"},{"key":"2022012000290051700_ref8","doi-asserted-by":"crossref","first-page":"3506","DOI":"10.1093\/bioinformatics\/btv472","article-title":"Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning","volume":"31","author":"Ma","year":"2015","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref9","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":"2022012000290051700_ref10","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1002\/prot.25779","article-title":"Prediction of interresidue contacts with DeepMetaPSICOV in CASP13","volume":"87","author":"Kandathil","year":"2019","journal-title":"Proteins"},{"key":"2022012000290051700_ref11","doi-asserted-by":"crossref","first-page":"4039","DOI":"10.1093\/bioinformatics\/bty481","article-title":"Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks","volume":"34","author":"Hanson","year":"2018","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref12","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1073\/pnas.1914677117","article-title":"Improved protein structure prediction using predicted interresidue orientations","volume":"117","author":"Yang","year":"2020","journal-title":"Proc Natl Acad Sci"},{"key":"2022012000290051700_ref13","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1093\/bioinformatics\/btx781","article-title":"DNCON2: improved protein contact prediction using two-level deep convolutional neural networks","volume":"34","author":"Adhikari","year":"2018","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref14","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s42256-019-0130-4","article-title":"AmoebaContact and GDFold as a pipeline for rapid de novo protein structure prediction","volume":"2","author":"Mao","year":"2020","journal-title":"Nat Mach Intell"},{"key":"2022012000290051700_ref15","doi-asserted-by":"crossref","first-page":"4862","DOI":"10.1093\/bioinformatics\/btz422","article-title":"AlphaFold at CASP13","volume":"35","author":"AlQuraishi","year":"2019","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref16","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1093\/bioinformatics\/btz863","article-title":"DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins","volume":"36","author":"Zhang","year":"2020","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref17","first-page":"770"},{"key":"2022012000290051700_ref18","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"2022012000290051700_ref19","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans Signal Process"},{"key":"2022012000290051700_ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2020.3000758","article-title":"De novo protein structure prediction by coupling contact with distance profile","author":"Peng","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2022012000290051700_ref21","doi-asserted-by":"crossref","first-page":"2443","DOI":"10.1093\/bioinformatics\/btz943","article-title":"CGLFold: a contact-assisted de novo protein structure prediction using global exploration and loop perturbation sampling algorithm","volume":"36","author":"Liu","year":"2020","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref22","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1109\/TCBB.2018.2873691","article-title":"Secondary structure and contact guided differential evolution for protein structure prediction","volume":"17","author":"Zhang","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2022012000290051700_ref23","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0092197","article-title":"De novo structure prediction of globular proteins aided by sequence variation-derived contacts","volume":"9","author":"Kosciolek","year":"2014","journal-title":"PLoS One"},{"key":"2022012000290051700_ref24","doi-asserted-by":"crossref","first-page":"4647","DOI":"10.1093\/bioinformatics\/btz291","article-title":"ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks","volume":"35","author":"Li","year":"2019","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref25","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1093\/bioinformatics\/btx164","article-title":"NeBcon: protein contact map prediction using neural network training coupled with na\u00efve Bayes classifiers","volume":"33","author":"He","year":"2017","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref26","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1002\/prot.24829","article-title":"CONFOLD: residue-residue contact-guided ab initio protein folding","volume":"83","author":"Adhikari","year":"2015","journal-title":"Proteins"},{"key":"2022012000290051700_ref27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-018-2032-6","article-title":"CONFOLD2: improved contact-driven ab initio protein structure modeling","volume":"19","author":"Adhikari","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2022012000290051700_ref28","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0028766","article-title":"Protein 3D structure computed from evolutionary sequence variation","volume":"6","author":"Marks","year":"2011","journal-title":"PLoS One"},{"key":"2022012000290051700_ref29","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1107\/S0907444998003254","article-title":"Crystallography &amp; NMR system: a new software suite for macromolecular structure determination, Acta crystallographica. Section D","volume":"54","author":"Br\u00fcnger","year":"1998","journal-title":"Biol Crystallogr"},{"key":"2022012000290051700_ref30","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1093\/bioinformatics\/btu791","article-title":"MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins","volume":"31","author":"Jones","year":"2015","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref31","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1093\/bioinformatics\/btr638","article-title":"PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments","volume":"28","author":"Jones","year":"2012","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref32","doi-asserted-by":"crossref","first-page":"3128","DOI":"10.1093\/bioinformatics\/btu500","article-title":"CCMpred\u2014fast and precise prediction of protein residue\u2013residue contacts from correlated mutations","volume":"30","author":"Seemayer","year":"2014","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref33","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1186\/1471-2105-15-85","article-title":"FreeContact: fast and free software for protein contact prediction from residue co-evolution","volume":"15","author":"Kaj\u00e1n","year":"2014","journal-title":"BMC Bioinformatics"},{"key":"2022012000290051700_ref34","doi-asserted-by":"crossref","first-page":"i75","DOI":"10.1093\/bioinformatics\/bti1004","article-title":"Three-stage prediction of protein \u03b2-sheets by neural networks, alignments and graph algorithms","volume":"21","author":"Cheng","year":"2005","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref35","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/1471-2105-8-113","article-title":"Improved residue contact prediction using support vector machines and a large feature set","volume":"8","author":"Cheng","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2022012000290051700_ref36","first-page":"924","article-title":"A comprehensive assessment of sequence-based and template-based methods for protein contact prediction","volume-title":"Bioinformatics","author":"Wu","year":"2008"},{"key":"2022012000290051700_ref37","first-page":"209","volume-title":"Abstract of CASP11 Experiment","author":"Yang","year":"2014"},{"key":"2022012000290051700_ref38","doi-asserted-by":"crossref","first-page":"3308","DOI":"10.1093\/bioinformatics\/bty341","article-title":"High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features","volume":"34","author":"Jones","year":"2018","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref39","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/s00232-014-9648-x","article-title":"Automated procedure for contact-map-based protein structure reconstruction","volume":"247","author":"Konopka","year":"2014","journal-title":"J Membr Biol"},{"key":"2022012000290051700_ref40","first-page":"536","article-title":"Underestimation-assisted global-local cooperative differential evolution and the application to protein structure prediction","volume":"24","author":"Zhou","year":"2019","journal-title":"IEEE Trans Evol Comput"},{"key":"2022012000290051700_ref41","doi-asserted-by":"crossref","first-page":"2730","DOI":"10.1109\/TCYB.2017.2710626","article-title":"Abstract convex underestimation assisted multistage differential evolution","volume":"47","author":"Zhou","year":"2017","journal-title":"IEEE Trans Cybern"},{"key":"2022012000290051700_ref42","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1109\/TCYB.2018.2801287","article-title":"Differential evolution with underestimation-based multimutation strategy","volume":"49","author":"Zhou","year":"2018","journal-title":"IEEE Trans Cybern"},{"key":"2022012000290051700_ref43","doi-asserted-by":"crossref","first-page":"15930","DOI":"10.1073\/pnas.1905068116","article-title":"Assembling multidomain protein structures through analogous global structural alignments","volume":"116","author":"Zhou","year":"2019","journal-title":"Proc Natl Acad Sci"},{"key":"2022012000290051700_ref44","article-title":"Protein structure prediction using Rosetta","volume-title":"Methods in Enzymology","author":"Rohl","year":"2004"},{"key":"2022012000290051700_ref45","doi-asserted-by":"crossref","first-page":"D304","DOI":"10.1093\/nar\/gkt1240","article-title":"SCOPe: structural classification of proteins\u2014extended, integrating SCOP and ASTRAL data and classification of new structures","volume":"42","author":"Fox","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2022012000290051700_ref46","doi-asserted-by":"crossref","first-page":"D475","DOI":"10.1093\/nar\/gky1134","article-title":"SCOPe: classification of large macromolecular structures in the structural classification of proteins\u2014extended database","volume":"47","author":"Chandonia","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2022012000290051700_ref47","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref48","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":"2022012000290051700_ref49","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab500","volume-title":"Bioinformatics","author":"Xia","year":"2021"},{"key":"2022012000290051700_ref50","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab484","volume-title":"Bioinformatics","author":"Zhao","year":"2021"},{"key":"2022012000290051700_ref51","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1002\/prot.20264","article-title":"Scoring function for automated assessment of protein structure template quality","volume":"57","author":"Zhang","year":"2004","journal-title":"Proteins"},{"key":"2022012000290051700_ref52","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1093\/bioinformatics\/btq066","article-title":"How significant is a protein structure similarity with TM-score= 0.5?","volume":"26","author":"Xu","year":"2010","journal-title":"Bioinformatics"},{"key":"2022012000290051700_ref53","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1002\/prot.25810","article-title":"Analysis of distance-based protein structure prediction by deep learning in CASP13","volume":"87","author":"Xu","year":"2019","journal-title":"Proteins"},{"key":"2022012000290051700_ref54","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"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab463\/42230777\/bbab463.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab463\/42230777\/bbab463.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T03:03:31Z","timestamp":1699671811000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab463\/6445108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,28]]},"references-count":54,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1,17]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab463","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"type":"print","value":"1467-5463"},{"type":"electronic","value":"1477-4054"}],"subject":[],"published-other":{"date-parts":[[2022,1]]},"published":{"date-parts":[[2021,11,28]]},"article-number":"bbab463"}}