Abstract：
An explicit analytic solution is given for the Langevin equation applied to the Gaussian Network Model of a protein subjected to both a random and a deterministic periodic force. Synchronous and asynchronous components of time correlation functions are derived and an expression for phase differences in the time correlations of residue pairs is obtained. The synchronous component enables the determination of dynamic communities within the protein structure. The asynchronous component reveals causality, where the time correlation function between residues i and j differs depending on whether i is observed before j or vice versa, resulting in directional information flow. Driver and driven residues in the allosteric process of cyclophilin A and human NAD-dependent isocitrate dehydrogenase are determined by a perturbation-scanning technique. Factors affecting phase differences between fluctuations of residues, such as network topology, connectivity, and residue centrality, are identified. Within the constraints of the isotropic Gaussian Network Model, our results show that asynchronicity increases with viscosity and distance between residues, decreases with increasing connectivity, and decreases with increasing levels of eigenvector centrality.
摘要：
对于应用于蛋白质的高斯网络模型的Langevin方程，给出了明确的解析解。随机和确定性周期力。推导了时间相关函数的同步和异步分量，并获得了残差对的时间相关性中的相位差表达式。同步组件能够确定蛋白质结构内的动态群落。异步组件揭示了因果关系，其中，残基i和j之间的时间相关函数取决于是否在j之前观察到i或反之亦然，导致定向信息流。亲环蛋白A和人NAD依赖性异柠檬酸脱氢酶的变构过程中的驱动和驱动残基是通过扰动扫描技术确定的。影响残留物波动之间相位差的因素，如网络拓扑，连通性,和残留物中心性，被识别。在各向同性高斯网络模型的约束下，我们的结果表明，异步性随着粘度和残留物之间的距离而增加，随着连接性的增加而减少，并随着特征向量中心性水平的增加而减小。
