{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:42:44Z","timestamp":1753875764467,"version":"3.41.2"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"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 Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LZ20F030002","62073219","61725302","61773346","62173304","Q22F027777","GZ21461030004"],"award-info":[{"award-number":["LZ20F030002","62073219","61725302","61773346","62173304","Q22F027777","GZ21461030004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With the rapid progress of deep learning in cryo-electron microscopy and protein structure prediction, improving the accuracy of the protein structure model by using a density map and predicted contact\/distance map through deep learning has become an urgent need for robust methods. Thus, designing an effective protein structure optimization strategy based on the density map and predicted contact\/distance map is critical to improving the accuracy of structure refinement. In this article, a protein structure optimization method based on the density map and predicted contact\/distance map by deep-learning technology was proposed in accordance with the result of matching between the density map and the initial model. Physics- and knowledge-based energy functions, integrated with Cryo-EM density map data and deep-learning data, were used to optimize the protein structure in the simulation. The dynamic confidence score was introduced to the iterative process for choosing whether it is a density map or a contact\/distance map to dominate the movement in the simulation to improve the accuracy of refinement. The protocol was tested on a large set of 224 non-homologous membrane proteins and generated 214 structural models with correct folds, where 4.5% of structural models were generated from structural models with incorrect folds. Compared with other state-of-the-art methods, the major advantage of the proposed methods lies in the skills for using density map and contact\/distance map in the simulation, as well as the new energy function in the re-assembly simulations. Overall, the results demonstrated that this strategy is a valuable approach and ready to use for atomic-level structure refinement using cryo-EM density map and predicted contact\/distance map.<\/jats:p>","DOI":"10.1093\/bib\/bbac026","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T12:08:37Z","timestamp":1643112517000},"source":"Crossref","is-referenced-by-count":1,"title":["Accurate flexible refinement for atomic-level protein structure using cryo-EM density maps and deep learning"],"prefix":"10.1093","volume":"23","author":[{"given":"Biao","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology"}]},{"given":"Dong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan"}]},{"given":"Hong-Bin","family":"Shen","sequence":"additional","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China"}]},{"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"}]}],"member":"286","published-online":{"date-parts":[[2022,2,11]]},"reference":[{"key":"2022031506402268100_ref1","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1038\/s42256-021-00348-5","article-title":"Improved protein structure prediction by deep learning irrespective of co-evolution information","volume":"3","author":"Xu","year":"2021","journal-title":"Nat Mach 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