Li Chun-Biu, Yang Haw, Komatsuzaki Tamiki
Nonlinear Sciences Laboratory, Department of Earth and Planetary Sciences, Faculty of Science, Kobe University, Nada, Kobe 657-8501, Japan.
Proc Natl Acad Sci U S A. 2008 Jan 15;105(2):536-41. doi: 10.1073/pnas.0707378105. Epub 2008 Jan 4.
Conformational dynamics of proteins can be interpreted as itinerant motions as the protein traverses from one state to another on a complex network in conformational space or, more generally, in state space. Here we present a scheme to extract a multiscale state space network (SSN) from a single-molecule time series. Analysis by this method enables us to lift degeneracy--different physical states having the same value for a measured observable--as much as possible. A state or node in the network is defined not by the value of the observable at each time but by a set of subsequences of the observable over time. The length of the subsequence can tell us the extent to which the memory of the system is able to predict the next state. As an illustration, we investigate the conformational fluctuation dynamics probed by single-molecule electron transfer (ET), detected on a photon-by-photon basis. We show that the topographical features of the SSNs depend on the time scale of observation; the longer the time scale, the simpler the underlying SSN becomes, leading to a transition of the dynamics from anomalous diffusion to normal Brownian diffusion.
蛋白质的构象动力学可被解释为巡回运动，因为蛋白质在构象空间或更一般地在状态空间的复杂网络上从一种状态转变为另一种状态。在此，我们提出一种从单分子时间序列中提取多尺度状态空间网络（SSN）的方案。通过这种方法进行分析，能够使我们尽可能地消除简并性——即不同物理状态对于所测量的可观测量具有相同的值。网络中的一个状态或节点不是由每次的可观测量值来定义，而是由可观测量随时间的一组子序列来定义。子序列的长度能够告诉我们系统记忆预测下一状态的程度。作为示例，我们研究了通过单分子电子转移（ET）探测到的构象涨落动力学，该动力学是逐光子检测的。我们表明，SSN的拓扑特征取决于观测的时间尺度；时间尺度越长，潜在的SSN就变得越简单，从而导致动力学从反常扩散转变为正常布朗扩散。
