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dc.contributor.authorLee, Seho-
dc.contributor.authorSeok, Chaok-
dc.contributor.authorPark, Hahnbeom-
dc.date.accessioned2024-01-19T09:33:31Z-
dc.date.available2024-01-19T09:33:31Z-
dc.date.created2023-03-23-
dc.date.issued2023-05-
dc.identifier.issn0192-8651-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/113780-
dc.description.abstractCryo-electron microscopy (cryo-EM) is gaining large attention for high-resolution protein structure determination in solutions. However, a very high percentage of cryo-EM structures correspond to resolutions of 3-5 & Aring;, making the structures difficult to be used in in silico drug design. In this study, we analyze how useful cryo-EM protein structures are for in silico drug design by evaluating ligand docking accuracy. From realistic cross-docking scenarios using medium resolution (3-5 & Aring;) cryo-EM structures and a popular docking tool Autodock-Vina, only 20% of docking succeeded, when the success rate doubles in the same kind of cross-docking but using high-resolution (< 2 & Aring;) crystal structures instead. We decipher the reason for failures by decomposing the contribution from resolution-dependent and independent factors. The heterogeneity in the protein side-chain and backbone conformations is identified as the major resolution-dependent factor causing docking difficulty from our analysis, while intrinsic receptor flexibility mainly comprises the resolution independent factor. We demonstrate the flexibility implementation in current ligand docking tools is able to rescue only a portion of failures (10%), and the limited performance was majorly due to potential structural errors than conformational changes. Our work suggests the strong necessity of more robust method developments on ligand docking and EM modeling techniques in order to fully utilize cryo-EM structures for in silico drug design.-
dc.languageEnglish-
dc.publisherJohn Wiley & Sons Inc.-
dc.titleBenchmarking applicability of medium-resolution cryo-EM protein structures for structure-based drug design-
dc.typeArticle-
dc.identifier.doi10.1002/jcc.27091-
dc.description.journalClass1-
dc.identifier.bibliographicCitationJournal of Computational Chemistry, v.44, no.14, pp.1360 - 1364-
dc.citation.titleJournal of Computational Chemistry-
dc.citation.volume44-
dc.citation.number14-
dc.citation.startPage1360-
dc.citation.endPage1364-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.identifier.wosid000940818400001-
dc.identifier.scopusid2-s2.0-85149898037-
dc.relation.journalWebOfScienceCategoryChemistry, Multidisciplinary-
dc.relation.journalResearchAreaChemistry-
dc.type.docTypeArticle; Early Access-
dc.subject.keywordPlusFIT-
dc.subject.keywordPlusATOMIC STRUCTURES-
dc.subject.keywordPlusMOLPROBITY-
dc.subject.keywordPlusDOCKING-
dc.subject.keywordAuthorstructure-based drug design-
dc.subject.keywordAuthorcomputer-aided drug design-
dc.subject.keywordAuthorcryo-EM structure-
dc.subject.keywordAuthorligand docking-
dc.subject.keywordAuthorprotein structure prediction-
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