Calandrini Vania, Abergel Daniel, Kneller Gerald R
Centre de Biophysique Moléculaire, CNRS, Rue Charles Sadron, 45071 Orléans, France.
J Chem Phys. 2008 Apr 14;128(14):145102. doi: 10.1063/1.2894844.
Nuclear magnetic resonance (NMR) has proven to be the most valuable tool for investigating internal dynamics of proteins. In this perspective, the interpretation of NMR relaxation data eventually relies on a model of the motions. In this article, we propose to compare two radically different approaches that aim at describing internal dynamics in proteins. It is shown that the correlation functions predicted by a network of coupled rotators can be interpreted in terms of a heuristic approach based on fractional Brownian dynamics for each of the vectors in the network. Our results are interpreted in terms of the probability distributions of relaxation modes in both processes, the median of which turns out to be the relevant quantity for the comparison of both models.
核磁共振（NMR）已被证明是研究蛋白质内部动力学最有价值的工具。从这个角度来看，NMR弛豫数据的解释最终依赖于运动模型。在本文中，我们提议比较两种截然不同的方法，它们旨在描述蛋白质中的内部动力学。结果表明，耦合转子网络预测的相关函数可以根据基于分数布朗动力学的启发式方法对网络中的每个向量进行解释。我们根据两个过程中弛豫模式的概率分布来解释我们的结果，其中位数被证明是比较这两种模型的相关量。
