標題: Identifying the structural and kinetic elements in protein large-amplitude conformational motions
作者: Chu, Jhih-Wei
Yang, Haw
生物科技學系
生物資訊及系統生物研究所
分子醫學與生物工程研究所
Department of Biological Science and Technology
Institude of Bioinformatics and Systems Biology
Institute of Molecular Medicine and Bioengineering
關鍵字: Trajectory entropy;Bayesian inference;continuous stochastic process;missing data;structural imputation;Tanner-Wong algorithm
公開日期: 1-一月-2017
摘要: The 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.
URI: http://dx.doi.org/10.1080/0144235X.2017.1283885
http://hdl.handle.net/11536/145545
ISSN: 0144-235X
DOI: 10.1080/0144235X.2017.1283885
期刊: INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY
Volume: 36
起始頁: 185
結束頁: 227
顯示於類別:期刊論文