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Here, the authors report a computational workflow that incorporates a novel pocket generation method. First, the closed protein pocket is expanded by repeatedly filling virtual atoms during molecular dynamics (MD) simulations. Second, after ligand docking toward the prepared pocket structures, binding mode candidates are ranked by MD\/Molecular Mechanics Poisson\u2010Boltzmann Surface Area. The authors validated our workflow using CDK2 kinase, which has an especially\u2010closed ATP\u2010binding pocket in the apo\u2010form, and several inhibitors. The crystallographic pose coincided with the top\u2010ranked docking pose for 59% (34\/58) of the compounds and was within the top five\u2010ranked ones for 88% (51\/58), while those estimated by a conventional prediction protocol were 9% (5\/58) and 50% (29\/58), respectively. Our study demonstrates that the prediction accuracy is significantly improved by preceding pocket expansion, leading to generation of conformationally\u2010diverse binding mode candidates. \u00a9 2018 Wiley Periodicals, Inc.<\/jats:p>","DOI":"10.1002\/jcc.25715","type":"journal-article","created":{"date-parts":[[2018,12,5]],"date-time":"2018-12-05T02:27:58Z","timestamp":1543976878000},"page":"2679-2689","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Improving the Accuracy of Protein\u2010Ligand Binding Mode Prediction Using a Molecular Dynamics\u2010Based Pocket Generation Approach"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7810-4905","authenticated-orcid":false,"given":"Mitsugu","family":"Araki","sequence":"first","affiliation":[{"name":"Graduate School of Medicine Kyoto University 53 Shogoin\u2010Kawaharacho, Sakyo\u2010ku Kyoto 606\u20108507 Japan"},{"name":"RIKEN 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