The Slowly Relaxing Local Structure Perspective of Protein Dynamics by NMR Relaxation

被引:2
|
作者
Meirovitch, Eva [1 ]
机构
[1] Bar Ilan Univ, Mina & Everard Goodman Fac Life Sci, IL-5290002 Ramat Gan, Israel
基金
以色列科学基金会;
关键词
molecular dynamics; NMR relaxation; proteins; stochastic models; time correlation functions; ELECTRON-SPIN-RESONANCE; MODEL-FREE APPROACH; MOLECULAR-DYNAMICS; ORDER PARAMETERS; LINE-SHAPES; TEMPERATURE-DEPENDENCE; COUPLING ANALYSIS; METHYL ROTATION; SRLS ANALYSIS; DOMAIN;
D O I
10.1002/ijch.201300090
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
NMR relaxation is a powerful method for elucidating structural dynamics. Standard stochastic dynamic models generate time correlation functions (TCFs) that feature physically well-defined parameters. We developed such a model, called the slowly relaxing local structure (SRLS) approach, for proteins. SRLS is a two-body (protein and probe) coupled-rotator approach. Given that the protein (featuring diffusion tensor, D-1) restricts the probe (featuring diffusion tensor, D-2), the two bodies are inherently coupled dynamically. This is substantiated by a local potential, u, associated with a local ordering tensor, S. SRLS allows for general tensorial properties of D-1, D-2, S and the magnetic NMR tensors, and a general form of u. The TCFs are multi-exponential, in accordance with the degree of generality of the various tensors. The traditional model-free (MF) method is based on a different conceptualization. According to it a mode-decoupling bi-exponential (one term for each rotator) TCF captures adequately the detectable features of structural dynamics. Hence, stochastic approaches are unnecessary. Here, we show that this (amply proven) oversimplification leads to physically vague constructs/composites as descriptors of structural dynamics. We illustrate misleading results obtained with MF when mode coupling, or S tensor asymmetry, dominate the analysis. Finally, we delineate the substantial advantage in using SRLS TCF as quantity to be compared with its atomistic molecular dynamicsbased counterpart.
引用
收藏
页码:47 / 59
页数:13
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