序参量分析中的动力学解释：来自理论模型和无序肽的教训
Accounting for the kinetics in order parameter analysis: lessons from theoretical models and a disordered peptide
摘要：
分子模拟以及单分子实验已被广泛分析的序参量，后者代表候选探针的相关自由度。尽管这种方法非常直观，但越来越多的证据表明，这种描述是不准确的，导致状态和错误动力学的模糊定义。为了克服这些限制，一个框架，利用序参量波动与复杂网络分析的影响。这种方法源于单分子时间轨迹分析的最新进展，考虑到每个时间点周围的波动，以区分具有相似的序参量值但不同动力学的状态。具有类似波动的快照被用作过渡网络的节点，将其聚类为状态提供了所研究系统的精确马尔可夫状态模型。应用的方法与噪声的序参量，以及一个无序的肽的动力学的理论模型说明了建立精确的描述分子过程的唯一基础上的序参量时间序列，而不使用任何补充信息的可能性。
英文摘要：
Molecular simulations as well as single molecule experiments have been widely analyzed in terms order parameters, the latter representing candidate probes for the relevant degrees of freedom. Notwithstanding this approach is very intuitive, mounting evidence showed that such description is not accurate, leading to ambiguous definitions of states and wrong kinetics. To overcome these limitations a framework making use of order parameter fluctuations in conjunction with complex network analysis is investigated. Derived from recent advances in the analysis of single molecule time traces, this approach takes into account of the fluctuations around each time point to distinguish between states that have similar values of the order parameter but different dynamics. Snapshots with similar fluctuations are used as nodes of a transition network, the clusterization of which into states provides accurate Markov-State-Models of the system under study. Application of the methodology to theoretical models with a noisy order parameter as well as the dynamics of a disordered peptide illustrates the possibility to build accurate descriptions of molecular processes on the sole basis of order parameter time series without using any supplementary information.
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