TY - JOUR
T1 - Identifying the structural and kinetic elements in protein large-amplitude conformational motions
AU - Chu, Jhih Wei
AU - Yang, Haw
N1 - Funding Information:
This work is supported by the ‘Global Networking Talent 3.0 Plan’ of the National Chiao Tung Universitynd the Ministry of Education, Taiwan R.O.C., and by Princeton University U.S.A. The funding from the Ministry of Science and Technology of Taiwan, ROC, via [grant number 103-2628-M-009-003-MY3] is also acknowledged.
Publisher Copyright:
© 2017, Informa UK Limited, trading as Taylor & Francis Group.
PY - 2017/4/3
Y1 - 2017/4/3
N2 - 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 Förster-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.
AB - 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 Förster-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.
KW - Bayesian inference
KW - Continuous stochastic process
KW - Missing data
KW - Structural imputation
KW - Tanner–Wong algorithm
KW - Trajectory entropy
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U2 - 10.1080/0144235X.2017.1283885
DO - 10.1080/0144235X.2017.1283885
M3 - Article
AN - SCOPUS:85034867939
SN - 0144-235X
VL - 36
SP - 185
EP - 227
JO - International Reviews in Physical Chemistry
JF - International Reviews in Physical Chemistry
IS - 2
ER -