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Effective Harmonic Potentials: Insights into the Internal Cooperativity and Sequence-Specificity of Protein Dynamics

机译:有效的谐波势:对内部动力学和蛋白质动力学的序列特异性的见解。

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摘要

The proper biological functioning of proteins often relies on the occurrence of coordinated fluctuations around their native structure, or on their ability to perform wider and sometimes highly elaborated motions. Hence, there is considerable interest in the definition of accurate coarse-grained descriptions of protein dynamics, as an alternative to more computationally expensive approaches. In particular, the elastic network model, in which residue motions are subjected to pairwise harmonic potentials, is known to capture essential aspects of conformational dynamics in proteins, but has so far remained mostly phenomenological, and unable to account for the chemical specificities of amino acids. We propose, for the first time, a method to derive residue- and distance-specific effective harmonic potentials from the statistical analysis of an extensive dataset of NMR conformational ensembles. These potentials constitute dynamical counterparts to the mean-force statistical potentials commonly used for static analyses of protein structures. In the context of the elastic network model, they yield a strongly improved description of the cooperative aspects of residue motions, and give the opportunity to systematically explore the influence of sequence details on protein dynamics.
机译:蛋白质的适当生物学功能通常取决于其天然结构周围协调波动的发生,或者取决于它们执行更广泛,有时甚至是高度复杂的运动的能力。因此,人们对定义蛋白质动力学的精确粗粒度描述的定义非常感兴趣,可以替代更昂贵的计算方法。尤其是,其中残基运动受到成对谐波电位影响的弹性网络模型可以捕获蛋白质构象动力学的基本方面,但是到目前为止,大部分仍然是现象学的,并且不能解释氨基酸的化学特异性。 。我们首次提出了一种从大量的NMR构象数据集的统计分析中得出残基和距离特定的有效谐波电位的方法。这些电势构成了通常用于静态蛋白质结构分析的平均力统计电势的动态对应物。在弹性网络模型的背景下,它们对残基运动的协作方面产生了极大的改进,并为系统地探索序列细节对蛋白质动力学的影响提供了机会。

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