{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:06Z","timestamp":1772138046724,"version":"3.50.1"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2021,6,29]],"date-time":"2021-06-29T00:00:00Z","timestamp":1624924800000},"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":["61773346"],"award-info":[{"award-number":["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":[[2021,12,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages: The first is a modal exploration stage, in which a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. The second is a modal maintaining stage, where an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on a large set of 320 non-redundant proteins, where MMpred obtains models with TM-score\u22650.5 on 291 cases, which is 28% higher than that of Rosetta guided with the same set of distance constraints. In addition, on 320 benchmark proteins, the enhanced version of MMpred (E-MMpred) has 167 targets better than trRosetta when the best of five models are evaluated. The average TM-score of the best model of E-MMpred is 0.732, which is comparable to trRosetta (0.730).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code and executable are freely available at https:\/\/github.com\/iobio-zjut\/MMpred.<\/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\/btab484","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T15:21:07Z","timestamp":1624893667000},"page":"4350-4356","source":"Crossref","is-referenced-by-count":32,"title":["MMpred: a distance-assisted multimodal conformation sampling for\n                    <i>de novo<\/i>\n                    protein structure prediction"],"prefix":"10.1093","volume":"37","author":[{"given":"Kai-Long","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology , Hangzhou 310023, China"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology , Hangzhou 310023, China"}]},{"given":"Xiao-Gen","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI 48109-2218, USA"}]},{"given":"Jian-Zhong","family":"Su","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University , Wenzhou 325011, China"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI 48109-2218, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7815-5884","authenticated-orcid":false,"given":"Gui-Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology , Hangzhou 310023, China"}]}],"member":"286","published-online":{"date-parts":[[2021,6,29]]},"reference":[{"key":"2023061310491577300_btab484-B1","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1002\/prot.25787","article-title":"A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments","volume":"87","author":"Abriata","year":"2019","journal-title":"Proteins"},{"key":"2023061310491577300_btab484-B2","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":"2023061310491577300_btab484-B3","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.1126\/science.1113801","article-title":"Toward high-resolution de novo structure prediction for small proteins","volume":"309","author":"Bradley","year":"2005","journal-title":"Science"},{"key":"2023061310491577300_btab484-B4","doi-asserted-by":"crossref","first-page":"2728","DOI":"10.1038\/nprot.2007.406","article-title":"Version 1.2 of the crystallography and NMR system","volume":"2","author":"Brunger","year":"2007","journal-title":"Nat. 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