{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:06:08Z","timestamp":1769630768466,"version":"3.49.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["GM136422"],"award-info":[{"award-number":["GM136422"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["S10OD026825"],"award-info":[{"award-number":["S10OD026825"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["AI134678"],"award-info":[{"award-number":["AI134678"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS1901191"],"award-info":[{"award-number":["IIS1901191"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DBI2030790"],"award-info":[{"award-number":["DBI2030790"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["MTM2025426"],"award-info":[{"award-number":["MTM2025426"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https:\/\/zhanglab.ccmb.med.umich.edu\/LOMETS\/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep learning threading methods. For the first time, we have extended LOMETS3 to handle multi-domain proteins and to construct full-length models with gradient-based optimizations. Starting from a FASTA-formatted sequence, LOMETS3 performs four steps of domain boundary prediction, domain-level template identification, full-length template\/model assembly and structure-based function prediction. The output of LOMETS3 contains (i) top-ranked templates from LOMETS3 and its component threading programs, (ii) up to 5 full-length structure models constructed by L-BFGS (limited-memory Broyden\u2013Fletcher\u2013Goldfarb\u2013Shanno algorithm)\u00a0optimization, (iii) the 10 closest Protein Data Bank (PDB) structures to the target, (iv) structure-based functional predictions, (v) domain partition and assembly results, and (vi) the domain-level threading results, including items (i)\u2013(iii) for each identified domain. LOMETS3 was tested in large-scale benchmarks and the blind CASP14\u00a0(14th Critical Assessment of Structure Prediction) experiment, where the overall template recognition and function prediction accuracy is significantly beyond its predecessors and other state-of-the-art threading approaches, especially for hard targets without homologous templates in the PDB. Based on the improved developments, LOMETS3 should help significantly advance the capability of broader biomedical community for template-based protein structure and function modelling.<\/jats:p>","DOI":"10.1093\/nar\/gkac248","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:18:52Z","timestamp":1649935132000},"page":"W454-W464","source":"Crossref","is-referenced-by-count":38,"title":["LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation"],"prefix":"10.1093","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2984-9003","authenticated-orcid":false,"given":"Wei","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor , MI 48109 ,","place":["USA"]}]},{"given":"Qiqige","family":"Wuyun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Michigan State University , East Lansing , MI 48824 ,","place":["USA"]}]},{"given":"Xiaogen","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor , MI 48109 ,","place":["USA"]}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor , MI 48109 ,","place":["USA"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5821-4226","authenticated-orcid":false,"given":"Lydia","family":"Freddolino","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor , MI 48109 ,","place":["USA"]},{"name":"Department of Biological Chemistry, University of Michigan , Ann Arbor , MI 48109 ,","place":["USA"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2739-1916","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor , MI 48109 ,","place":["USA"]},{"name":"Department of Biological Chemistry, University of Michigan , Ann Arbor , MI 48109 ,","place":["USA"]}]}],"member":"286","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"2025010909012717400_B1","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":"2025010909012717400_B2","doi-asserted-by":"crossref","first-page":"5011","DOI":"10.1038\/s41467-021-25316-w","article-title":"Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions","volume":"12","author":"Mortuza","year":"2021","journal-title":"Nat. Commun."},{"key":"2025010909012717400_B3","doi-asserted-by":"crossref","first-page":"100014","DOI":"10.1016\/j.crmeth.2021.100014","article-title":"Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations","volume":"1","author":"Zheng","year":"2021","journal-title":"Cell Rep. Methods"},{"key":"2025010909012717400_B4","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1126\/science.abj8754","article-title":"Accurate prediction of protein structures and interactions using a three-track neural network","volume":"373","author":"Baek","year":"2021","journal-title":"Science"},{"key":"2025010909012717400_B5","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":"2025010909012717400_B6","doi-asserted-by":"crossref","first-page":"100870","DOI":"10.1016\/j.jbc.2021.100870","article-title":"Toward the solution of the protein structure prediction problem","volume":"297","author":"Pearce","year":"2021","journal-title":"J. Biol. Chem."},{"key":"2025010909012717400_B7","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1126\/science.1853201","article-title":"A method to identify protein sequences that fold into a known three-dimensional structure","volume":"253","author":"Bowie","year":"1991","journal-title":"Science"},{"key":"2025010909012717400_B8","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1093\/bioinformatics\/bti125","article-title":"Protein homology detection by HMM\u2013HMM comparison","volume":"21","author":"S\u00f6ding","year":"2005","journal-title":"Bioinformatics"},{"key":"2025010909012717400_B9","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1002\/prot.21945","article-title":"MUSTER: improving protein sequence profile\u2013profile alignments by using multiple sources of structure information","volume":"72","author":"Wu","year":"2008","journal-title":"Proteins"},{"key":"2025010909012717400_B10","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":"2025010909012717400_B11","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1002\/prot.26171","article-title":"High-accuracy protein structure prediction in CASP14","volume":"89","author":"Pereira","year":"2021","journal-title":"Proteins"},{"key":"2025010909012717400_B12","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1002\/prot.26193","article-title":"Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14","volume":"89","author":"Zheng","year":"2021","journal-title":"Proteins"},{"key":"2025010909012717400_B13","doi-asserted-by":"crossref","first-page":"e1007411","DOI":"10.1371\/journal.pcbi.1007411","article-title":"Detecting distant-homology protein structures by aligning deep neural-network based contact maps","volume":"15","author":"Zheng","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"2025010909012717400_B14","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1002\/prot.26254","article-title":"DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins","volume":"90","author":"Bhattacharya","year":"2022","journal-title":"Proteins"},{"key":"2025010909012717400_B15","doi-asserted-by":"crossref","first-page":"2684","DOI":"10.1093\/bioinformatics\/btx217","article-title":"EigenTHREADER: analogous protein fold recognition by efficient contact map threading","volume":"33","author":"Buchan","year":"2017","journal-title":"Bioinformatics"},{"key":"2025010909012717400_B16","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1126\/science.aah4043","article-title":"Protein structure determination using metagenome sequence data","volume":"355","author":"Ovchinnikov","year":"2017","journal-title":"Science"},{"key":"2025010909012717400_B17","doi-asserted-by":"crossref","first-page":"3375","DOI":"10.1093\/nar\/gkm251","article-title":"LOMETS: a local meta-threading-server for protein structure prediction","volume":"35","author":"Wu","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"2025010909012717400_B18","doi-asserted-by":"crossref","first-page":"W429","DOI":"10.1093\/nar\/gkz384","article-title":"LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins","volume":"47","author":"Zheng","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"2025010909012717400_B19","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1002\/prot.26211","article-title":"Protein inter-residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14","volume":"89","author":"Li","year":"2021","journal-title":"Proteins"},{"key":"2025010909012717400_B20","doi-asserted-by":"crossref","first-page":"W291","DOI":"10.1093\/nar\/gkx366","article-title":"COFACTOR: improved protein function prediction by combining structure, sequence and protein\u2013protein interaction information","volume":"45","author":"Zhang","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"2025010909012717400_B21","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.1093\/bioinformatics\/btaa217","article-title":"FUpred: detecting protein domains through deep-learning-based contact map prediction","volume":"36","author":"Zheng","year":"2020","journal-title":"Bioinformatics"},{"key":"2025010909012717400_B22","doi-asserted-by":"crossref","first-page":"i247","DOI":"10.1093\/bioinformatics\/btt209","article-title":"ThreaDom: extracting protein domain boundary information from multiple threading alignments","volume":"29","author":"Xue","year":"2013","journal-title":"Bioinformatics"},{"key":"2025010909012717400_B23","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. U.S.A."},{"key":"2025010909012717400_B24","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1016\/j.str.2011.09.022","article-title":"Atomic-level protein structure refinement using fragment-guided molecular dynamics conformation sampling","volume":"19","author":"Zhang","year":"2011","journal-title":"Structure"},{"key":"2025010909012717400_B25","doi-asserted-by":"crossref","first-page":"3758","DOI":"10.1093\/bioinformatics\/btaa234","article-title":"FASPR: an open-source tool for fast and accurate protein side-chain packing","volume":"36","author":"Huang","year":"2020","journal-title":"Bioinformatics"},{"key":"2025010909012717400_B26","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":"2025010909012717400_B27","doi-asserted-by":"crossref","first-page":"D1096","DOI":"10.1093\/nar\/gks966","article-title":"BioLiP: a semi-manually curated database for biologically relevant ligand\u2013protein interactions","volume":"41","author":"Yang","year":"2013","journal-title":"Nucleic Acids Res."},{"key":"2025010909012717400_B28","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1093\/bioinformatics\/btt578","article-title":"FFAS-3D: improving fold recognition by including optimized structural features and template re-ranking","volume":"30","author":"Xu","year":"2014","journal-title":"Bioinformatics"},{"key":"2025010909012717400_B29","doi-asserted-by":"crossref","first-page":"e1004343","DOI":"10.1371\/journal.pcbi.1004343","article-title":"Automatic prediction of protein 3D structures by probabilistic multi-template homology modeling","volume":"11","author":"Meier","year":"2015","journal-title":"PLoS Comput. Biol."},{"key":"2025010909012717400_B30","doi-asserted-by":"crossref","first-page":"e1003500","DOI":"10.1371\/journal.pcbi.1003500","article-title":"MRFalign: protein homology detection through alignment of Markov random fields","volume":"10","author":"Ma","year":"2014","journal-title":"PLoS Comput. Biol."},{"key":"2025010909012717400_B31","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1002\/prot.20308","article-title":"Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments","volume":"58","author":"Zhou","year":"2005","journal-title":"Proteins"},{"key":"2025010909012717400_B32","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":"2025010909012717400_B33","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":"2025010909012717400_B34","doi-asserted-by":"crossref","first-page":"5.6.1","DOI":"10.1002\/cpbi.3","article-title":"Comparative protein structure modeling using MODELLER","volume":"54","author":"Webb","year":"2016","journal-title":"Curr. Protoc. Bioinformatics"},{"key":"2025010909012717400_B35","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1002\/prot.21675","article-title":"Assessment of predictions submitted for the CASP7 domain prediction category","volume":"69","author":"Tress","year":"2007","journal-title":"Proteins"},{"key":"2025010909012717400_B36","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.1093\/bioinformatics\/bty973","article-title":"ConDo: protein domain boundary prediction using coevolutionary information","volume":"35","author":"Hong","year":"2019","journal-title":"Bioinformatics"},{"key":"2025010909012717400_B37","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/1471-2105-12-43","article-title":"DoBo: protein domain boundary prediction by integrating evolutionary signals and machine learning","volume":"12","author":"Eickholt","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2025010909012717400_B38","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. U.S.A."},{"key":"2025010909012717400_B39","doi-asserted-by":"crossref","first-page":"3045","DOI":"10.1093\/bioinformatics\/btp536","article-title":"QuickGO: a web-based tool for Gene Ontology searching","volume":"25","author":"Binns","year":"2009","journal-title":"Bioinformatics"},{"key":"2025010909012717400_B40","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1093\/nar\/28.1.304","article-title":"The ENZYME database in 2000","volume":"28","author":"Bairoch","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"2025010909012717400_B41","doi-asserted-by":"crossref","first-page":"e2110828118","DOI":"10.1073\/pnas.2110828118","article-title":"Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction","volume":"118","author":"Yang","year":"2021","journal-title":"Proc. Natl Acad. Sci. U.S.A."}],"container-title":["Nucleic Acids Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/nar\/article-pdf\/50\/W1\/W454\/44379395\/gkac248.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/nar\/article-pdf\/50\/W1\/W454\/44379395\/gkac248.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T09:02:12Z","timestamp":1736413332000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/nar\/article\/50\/W1\/W454\/6568492"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,14]]},"references-count":41,"journal-issue":{"issue":"W1","published-print":{"date-parts":[[2022,7,5]]}},"URL":"https:\/\/doi.org\/10.1093\/nar\/gkac248","relation":{},"ISSN":["0305-1048","1362-4962"],"issn-type":[{"value":"0305-1048","type":"print"},{"value":"1362-4962","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,7,5]]},"published":{"date-parts":[[2022,4,14]]}}}