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An Efficient Timer and Sizer of Biomacromolecular Motions
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An Efficient Timer and Sizer of Biomacromolecular Motions

Justin Chan, Kazuhiro Takemura, Hong-Rui Lin, Kai-Chun Chang, Yuan-Yu Chang, Yasumasa Joti, Akio KitaoLee-Wei Yang
Structure, 卷.28(2), 頁碼.259-269.e8
02/2020
PMID: 31780433

摘要

anharmonicity elastic network model (ENM) molecular dynamics simulations order parameter power spectrum principal component analysis (PCA) protein dynamics ribosome time-correlation function Wiener-Khintchine theorem Structural Biology Molecular Biology
Life ticks as fast as how proteins move. Computationally expensive molecular dynamics simulation has been the only theoretical tool to gauge the time and sizes of these motions, though barely to their slowest ends. Here, we convert a computationally cheap elastic network model (ENM) into a molecular timer and sizer to gauge the slowest functional motions of structured biomolecules. Quasi-harmonic analysis, fluctuation profile matching, and the Wiener-Khintchine theorem are used to define the “time periods,” t, for anharmonic principal components (PCs), which are validated by nuclear magnetic resonance (NMR) order parameters. The PCs with their respective “time periods” are mapped to the eigenvalues (λ ) of the corresponding ENM modes. Thus, the power laws t(ns) = 56.1λ and σ (Å ) = 32.7λ can be established allowing the characterization of the timescales of NMR-resolved conformers, crystallographic anisotropic displacement parameters, and important ribosomal motions, as well as motional sizes of the latter. Biological processes are often governed by protein functional dynamics that correspond to the large conformational transition and long timescales that cannot be practically studied using current simulation methodologies. We instead use the elastic network model (ENM), an efficient spring-bead model, and calibrate its predicted timescale and size of protein conformational changes with all-atom simulation data, resulting in time and space power laws with the only input being the ENM eigenvalues.

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https://doi.org/10.1016/j.str.2019.10.020檢視
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