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Entropy Transfer between Residue Pairs and Allostery in Proteins: Quantifying Allosteric Communication in Ubiquitin

机译:蛋白质中残留对和古代族菌之间的熵转移:泛素的颠覆颠覆沟通

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It has recently been proposed by Gunasakaran et al. that allostery may be an intrinsic property of all proteins. Here, we develop a computational method that can determine and quantify allosteric activity in any given protein. Based on Schreiber's transfer entropy formulation, our approach leads to an information transfer landscape for the protein that shows the presence of entropy sinks and sources and explains how pairs of residues communicate with each other using entropy transfer. The model can identify the residues that drive the fluctuations of others. We apply the model to Ubiquitin, whose allosteric activity has not been emphasized until recently, and show that there are indeed systematic pathways of entropy and information transfer between residues that correlate well with the activities of the protein. We use 600 nanosecond molecular dynamics trajectories for Ubiquitin and its complex with human polymerase iota and evaluate entropy transfer between all pairs of residues of Ubiquitin and quantify the binding susceptibility changes upon complex formation. We explain the complex formation propensities of Ubiquitin in terms of entropy transfer. Important residues taking part in allosteric communication in Ubiquitin predicted by our approach are in agreement with results of NMR relaxation dispersion experiments. Finally, we show that time delayed correlation of fluctuations of two interacting residues possesses an intrinsic causality that tells which residue controls the interaction and which one is controlled. Our work shows that time delayed correlations, entropy transfer and causality are the required new concepts for explaining allosteric communication in proteins.
机译:最近由Gunasakaran等人提出。仿生可能是所有蛋白质的内在性质。在这里,我们开发一种计算方法,可以确定和量化任何给定蛋白质中的变构活动。基于舍雷骨架的转移熵制剂,我们的方法导致蛋白质的信息转移景观,该蛋白质显示熵槽和源的存在,并解释了如何使用熵传递互相通信。该模型可以识别驱动他人波动的残留物。我们将模型应用于泛素,其崩解活动尚未强调,直到最近,表明存在熵的熵和信息转移,与蛋白质的活性相比好。我们使用600个纳秒分子动力学轨迹来遍布泛素及其复合物与人聚合酶IOTA,并评估泛素的所有残基之间的熵转移,并量化复杂的形成对结合易感性变化。在熵转移方面,我们解释了泛素的复杂形成型。通过我们的方法预测的泛素遍布泛素的变构沟通的重要残留符合NMR弛豫分散实验的结果。最后,我们表明,两个相互作用残留物的波动的时间延迟相关性具有内在的因果关系,告诉哪个残留物控制相互作用,并且控制该残留物。我们的工作表明,时间延迟相关性,熵转移和因果关系是用于解释蛋白质中的变构通信所需的新概念。

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