Markov methods for hierarchical coarse-graining of large protein dynamics
Markov methods for hierarchical coarse-graining of large protein dynamics
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DOI：
10.1089/cmb.2007.r015
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发表时间：
2007-07-01
影响因子：
1.7
通讯作者：
Bahar, Ivet
中科院分区：
文献类型：
--
作者：
Chennubhotla, Chakra;Bahar, Ivet
近年来，弹性网络模型（ENM），特别是高斯网络模型（GNM）已被广泛用于深入了解蛋白质的机制。由于在大蛋白质动力学模式分解中保留原子细节的困难，将ENM扩展到超分子组装体提出了计算挑战。在这里，我们提出了一种新的方法来解决这个问题。我们依赖的前提是，蛋白质机器（网络）的所有残基必须相互通信，并以协调的方式运作，以成功地执行其功能。为了深入了解残基之间的信息传递机制，我们研究了网络通信的马尔可夫模型。使用马尔可夫链的角度来看，我们映射的全原子网络表示到一个层次的ENM的分辨率下降，在减少的空间（S）进行分析的主要通信（或动态）模式和重建的详细模型，以最小的信息损失。层次结构的不同级别处的通信属性本质上由网络拓扑定义。这种新的表示有几个特点，包括：软聚类的蛋白质结构到随机相干区域，从而提供了一个有用的评估元素作为枢纽和/或发射机在传播信息/相互作用;自动计算的接触矩阵ENM在每个层次的层次结构，以方便计算高斯和各向异性波动动力学。我们说明了效用的层次分解，提供了一个有见地的描述的超分子机械的伴侣GroEL-GroES的方法。
Elastic network models (ENMs) and, in particular, the Gaussian Network Model (GNM) have been widely used in recent years to gain insights into the machinery of proteins. The extension of ENMs to supramolecular assemblies presents computational challenges, because of the difficulty in retaining atomic details in mode decomposition of large protein dynamics. Here, we present a novel approach to address this problem. We rely on the premise that, all the residues of the protein machinery (network) must communicate with each other and operate in a coordinated manner to perform their function successfully. To gain insight into the mechanism of information transfer between residues, we study a Markov model of network communication. Using the Markov chain perspective, we map the full-atom network representation into a hierarchy of ENMs of decreasing resolution, perform analysis of dominant communication (or dynamic) patterns in reduced space(s) and reconstruct the detailed models with minimal loss of information. The communication properties at different levels of the hierarchy are intrinsically defined by the network topology. This new representation has several features, including: soft clustering of the protein structure into stochastically coherent regions thus providing a useful assessment of elements serving as hubs and/or transmitters in propagating information/interaction; automatic computation of the contact matrices for ENMs at each level of the hierarchy to facilitate computation of both Gaussian and anisotropic fluctuation dynamics. We illustrate the utility of the hierarchical decomposition in providing an insightful description of the supramolecular machinery by applying the methodology to the chaperonin GroEL-GroES.