完整後設資料紀錄
DC 欄位語言
dc.contributor.authorChu, Jhih-Weien_US
dc.contributor.authorYang, Hawen_US
dc.date.accessioned2018-08-21T05:54:04Z-
dc.date.available2018-08-21T05:54:04Z-
dc.date.issued2017-01-01en_US
dc.identifier.issn0144-235Xen_US
dc.identifier.urihttp://dx.doi.org/10.1080/0144235X.2017.1283885en_US
dc.identifier.urihttp://hdl.handle.net/11536/145545-
dc.description.abstractThe importance of how a protein reconfigures its structure to achieve its function has long been appreciated; yet, the progress in our fundamental understanding of protein dynamics does not seem to be commensurate with the rapid advances in experimental techniques and ever increasing computational prowess. In this review, we attempt to look at this issue based on quantitative characterisations that go beyond simply determining the kinetics rates or only allowing qualitative statements about conformational states. We summarise the theoretical basis for determining from experimental data the kinetics and the structural elements of protein conformational dynamics. The two kinetics elements include the apparent potential of mean force and the intra-molecular diffusion coefficient along a coordinate defined by the pair of single-molecule Forster-type resonance energy transfer reporters that are chemically attached to the protein. We show that it is now possible to resolve the relative contributions of these two kinetics elements when discussing the physical origin of the protein's conformation-reconfiguration rate changes due to mutation or interaction with chemical effectors or with other proteins. The structural element refers to the orthogonal conformational modes that give rise to the intrinsic conformational motions of the protein, and could allow a comparative study among proteins from different families. To achieve these, it is essential that experimental data be rigorously analysed and integrated with molecular simulations - which include molecular dynamics simulations, coarse-grained modelling, and enhanced sampling. In turn, the close interplay between computation and experiment through this new direction could accelerate the discovery of predictive models.en_US
dc.language.isoen_USen_US
dc.subjectTrajectory entropyen_US
dc.subjectBayesian inferenceen_US
dc.subjectcontinuous stochastic processen_US
dc.subjectmissing dataen_US
dc.subjectstructural imputationen_US
dc.subjectTanner-Wong algorithmen_US
dc.titleIdentifying the structural and kinetic elements in protein large-amplitude conformational motionsen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/0144235X.2017.1283885en_US
dc.identifier.journalINTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRYen_US
dc.citation.volume36en_US
dc.citation.spage185en_US
dc.citation.epage227en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.department分子醫學與生物工程研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.contributor.departmentInstitute of Molecular Medicine and Bioengineeringen_US
dc.identifier.wosnumberWOS:000401930800001en_US
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