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Population shuffling between ground and high energy excited states.

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Sabo,  T. M.
Department of NMR-based Structural Biology, MPI for biophysical chemistry, Max Planck Society;

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Lee,  D.
Department of NMR-based Structural Biology, MPI for biophysical chemistry, Max Planck Society;

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引用

Sabo, T. M., Trent, J. O., & Lee, D. (2015). Population shuffling between ground and high energy excited states. Protein Science, 24(11), 1714-1719. doi:10.1002/pro.2797.


引用: https://hdl.handle.net/11858/00-001M-0000-0029-0705-C
要旨
Stochastic processes powered by thermal energy lead to protein motions traversing time-scales from picoseconds to seconds. Fundamental to protein functionality is the utilization of these dynamics for tasks such as catalysis, folding, and allostery. A hierarchy of motion is hypothesized to connect and synergize fast and slow dynamics toward performing these essential activities. Population shuffling predicts a top-down temporal hierarchy, where slow time-scale conformational interconversion leads to a shuffling of the free energy landscape for fast time-scale events. Until now, population shuffling was only applied to interconverting ground states. Here, we extend the framework of population shuffling to be applicable for a system interconverting between low energy ground and high energy excited states, such as the SH3 domain mutants G48M and A39V/N53P/V55L from the Fyn tyrosine kinase, providing another tool for accessing the structural dynamics of high energy excited states. Our results indicate that the higher energy gauche- rotameric state for the leucine (2) dihedral angle contributes significantly to the distribution of rotameric states in both the major and minor forms of the SH3 domain. These findings are corroborated with unrestrained molecular dynamics (MD) simulations on both the major and minor states of the SH3 domain demonstrating high correlations between experimental and back-calculated leucine (2) rotameric populations. Taken together, we demonstrate how fast time-scale rotameric side-chain population distributions can be extracted from slow time-scale conformational exchange data further extending the scope and the applicability of the population shuffling model.