Perturbation-based Markovian Transmission Model for macromolecular machinery in cell.
Perturbation-based Markovian Transmission Model for macromolecular machinery in cell.
复制标题
细胞中大分子机械的基于扰动的马尔可夫传输模型。
DOI：
10.1109/iembs.2007.4353470
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Liang,Jie
中科院分区：
文献类型：
--
作者：
Lu,Hsiao-Mei;Liang,Jie
复杂系统的动力学研究是一个重要的问题，包括大分子络合物、分子相互作用网络和细胞功能模块。细胞机械中的大分子复合体可以被建模为一个连接的网络，就像Bahar和他的同事所证明的弹性或高斯网络模型一样。在这里，我们提出了基于微扰的马尔可夫传输模型来研究大分子机械中信号传输的动力学。初始摄动由马尔可夫过程传递，概率流的动力学用主方程解析求解。由于大分子络合物的尺寸很大，很难获得所有原子从第一个微扰到稳定状态的含时马氏动力学解析。为了克服这个问题，我们使用Krylov子空间方法降低了转移矩阵的复杂度。这种方法相当于对所有本征模进行积分，我们证明了它可以在合理的计算时间内为超大分子络合物的信号传输动力学问题提供一个全局精确的解。通过对所有剩余项施加一致扰动，我们给出了GroEL-Groes伴侣系统的动力学结果。我们能够识别实验中发现的重要残基，并提供一组预测的枢轴、信使和效应器残基，每个残基都具有不同的动态行为。进一步对ATP结合口袋表面的选择性扰动的结果确定了信号的最大概率流的路径。我们的方法也可以应用于其他大系统，例如病毒衣壳、核糖体和大变构蛋白。
The study of the dynamics of a complex system is an important problem that includes large macromolecular complexes, molecular interaction networks, and cell functional modules. Large macromolecular complexes in cellular machinery can be modeled as a connected network, as in the elastic or Gaussian network models as demonstrated by Bahar and colleagues. Here we propose the Perturbation-based Markovian Transmission Model for studying the dynamics of signal transmission in macromolecular machinery. The initial perturbation is transmitted by a Markovian processes, and the dynamics of the probability flow is analytically solved using the master equation. Due to the large size of macromolecular complexes, it is very difficult to obtain analytical time-dependent Markovian dynamics of all atoms from the first perturbation until stationary state. To overcome it, we decrease the level of complexity of the transition matrix using a Krylov subspace method. This method is equivalent to integrating all eigen modes, and we show it can provide a globally accurate solution to the dynamics problem of signal transmission for very large macromolecular complexes with reasonable computational time. We give results of the dynamics of the GroEL-GroES chaperone system by applying uniform perturbation to all residues. We are able to identify experimentally found important residues and provide a set of predicted pivot, messenger, and effector residues, each with distinct dynamic behavior. Further results of selective perturbation on the surface of ATP binding pocket identifies the path of maximal probability flow of signal. Our method can also be applied to other large systems, for example, virus capsid, ribosome, and large allosteric proteins.