{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T12:25:30Z","timestamp":1786796730198,"version":"build-2736575974"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008954","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000}}],"reference-count":34,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM089753"],"award-info":[{"award-number":["R01GM089753"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DBI1564955"],"award-info":[{"award-number":["DBI1564955"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Technology Research and Development of China","award":["2020AAA0103802"],"award-info":[{"award-number":["2020AAA0103802"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20227"],"award-info":[{"award-number":["U20A20227"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Protein structure prediction has been greatly improved by deep learning, but most efforts are devoted to template-free modeling. But very few deep learning methods are developed for TBM (template-based modeling), a popular technique for protein structure prediction. TBM has been studied extensively in the past, but its accuracy is not satisfactory when highly similar templates are not available.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This paper presents a new method NDThreader (New Deep-learning Threader) to address the challenges of TBM. NDThreader first employs DRNF (deep convolutional residual neural fields), which is an integration of deep ResNet (convolutional residue neural networks) and CRF (conditional random fields), to align a query protein to templates without using any distance information. Then NDThreader uses ADMM (alternating direction method of multipliers) and DRNF to further improve sequence-template alignments by making use of predicted distance potential. Finally, NDThreader builds 3D models from a sequence-template alignment by feeding it and sequence coevolution information into a deep ResNet to predict inter-atom distance distribution, which is then fed into PyRosetta for 3D model construction. Our experimental results show that NDThreader greatly outperforms existing methods such as CNFpred, HHpred, DeepThreader and CEthreader. NDThreader was blindly tested in CASP14 as a part of RaptorX server, which obtained the best average GDT score among all CASP14 servers on the 58 TBM targets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pcbi.1008954","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T13:56:28Z","timestamp":1620050188000},"page":"e1008954","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":37,"title":["Deep template-based protein structure prediction"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1615-3004","authenticated-orcid":true,"given":"Fandi","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7111-4839","authenticated-orcid":true,"given":"Jinbo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"key":"pcbi.1008954.ref001","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1002\/prot.25800","article-title":"Evaluation of template-based modeling in CASP13","volume":"87","author":"TI Croll","year":"2019","journal-title":"Proteins"},{"key":"pcbi.1008954.ref002","doi-asserted-by":"crossref","first-page":"W244","DOI":"10.1093\/nar\/gki408","article-title":"The HHpred interactive server for protein homology detection and structure prediction","volume":"33","author":"J S\u00f6ding","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1008954.ref003","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.1093\/bioinformatics\/btr350","article-title":"Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates","volume":"27","author":"Y Yang","year":"2011","journal-title":"Bioinformatics"},{"key":"pcbi.1008954.ref004","doi-asserted-by":"crossref","first-page":"i59","DOI":"10.1093\/bioinformatics\/bts213","article-title":"A conditional neural fields model for protein threading","volume":"28","author":"J Ma","year":"2012","journal-title":"Bioinformatics"},{"key":"pcbi.1008954.ref005","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":"J Xu","year":"2019","journal-title":"Proteins"},{"key":"pcbi.1008954.ref006","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":"J. 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