Rapallo Arnaldo
CNR-Istituto di Scienze e Tecnologie Chimiche "Giulio Natta" (SCITEC), via A. Corti 12, Milano I-20133, Italy.
J Chem Theory Comput. 2025 Jul 22;21(14):6711-6728. doi: 10.1021/acs.jctc.5c00540. Epub 2025 Jul 11.
The coupling of time-lagged independent component analysis (TICA) with the Markov state model (MSM) technique has become a well-established route to study dynamics in complex molecular systems. Identification of the slow modes relevant to the molecular functions, quantification of the characteristic times involved in the slow dynamics, and prediction of dynamic properties are the basic frame of application of such methods. Among the current research developments in the field, great activity is devoted to the formulation of methods to improve approximation of the leading eigenfunctions of the transfer operator of a dynamical system from trajectory data and to include memory effects into MSM analysis. Along these lines of research, various developments are proposed here, in the framework of TICA-MSM approaches: a criterion to select dynamically informative intramolecular distances and a method to use them to build optimal nonlinear basis sets for TICA (upper-order TICA) are presented to overcome the limitations of linear approximations to the transfer operator eigenfunctions. Then, a fractional, non-Markovian process is introduced to deal with anomalous dynamic regimes characterized by nonexponential relaxations. The fractional process is described in terms of a time derivative of noninteger order α > 0 in the master equation of the temporal evolution of the states' probabilities, which replaces the exponential decay in time, typical of Markovian processes, with Mittag-Leffler functions in the temporal variable. This kind of temporal dependency is more appropriate to capture the characteristics of anomalous dynamics, often observed in proteins and peptides by both experiments and simulations. The theory is cast in a form that the researchers are familiar with when applying MSM analysis, allowing direct manipulations over the transition probability matrix. Moreover, the technique allows us to check whether the dynamics encoded in the molecular dynamics (MD) trajectory occur in an anomalous regime or not, and, in case, permits to quantify and treat the anomaly by identifying the appropriate fractional order α of the non-Markovian process. Purely Markovian dynamic regimes are special cases of the proposed theory and can be recovered by letting α = 1. The benchmark MD trajectories of chignolin (a), villin (b), and Trp-cage (c) proteins, provided by D.E. Shaw Research (DESRES), are revisited in light of the proposed developments, and cases (a) and (c) show that the ability to describe the system dynamics in terms of fractional non-Markovian processes is necessary to obtain a more accurate qualitative and quantitative picture of molecular dynamics occurring in anomalous regimes.
将时间滞后独立成分分析(TICA)与马尔可夫状态模型(MSM)技术相结合,已成为研究复杂分子系统动力学的一条成熟途径。识别与分子功能相关的慢模式、量化慢动力学中涉及的特征时间以及预测动力学性质,是此类方法应用的基本框架。在该领域当前的研究进展中,大量的研究致力于制定方法,以改进从轨迹数据逼近动力系统转移算子的主导本征函数,并将记忆效应纳入MSM分析。沿着这些研究方向,本文在TICA-MSM方法框架下提出了各种进展:提出了一种选择动态信息丰富的分子内距离的标准,以及一种使用这些距离为TICA构建最优非线性基集(高阶TICA)的方法,以克服对转移算子本征函数线性近似的局限性。然后,引入了一个分数阶非马尔可夫过程来处理以非指数弛豫为特征的反常动力学 regime。在状态概率时间演化的主方程中,分数阶过程用非整数阶α>0的时间导数来描述,它用时间变量中的米塔格-莱夫勒函数取代了马尔可夫过程典型的时间指数衰减。这种时间依赖性更适合捕捉反常动力学的特征,这种特征在蛋白质和肽的实验和模拟中经常观察到。该理论以研究人员在应用MSM分析时熟悉的形式呈现,允许对转移概率矩阵进行直接操作。此外,该技术使我们能够检查分子动力学(MD)轨迹中编码的动力学是否发生在反常 regime中,如果是,则允许通过识别非马尔可夫过程的适当分数阶α来量化和处理反常情况。纯马尔可夫动力学 regime是所提出理论的特殊情况,可以通过令α = 1来恢复。根据所提出的进展,重新审视了由D.E. Shaw Research(DESRES)提供的奇诺林(a)、维林(b)和色氨酸笼(c)蛋白的基准MD轨迹,案例(a)和(c)表明,用分数阶非马尔可夫过程描述系统动力学的能力对于获得反常 regime中分子动力学更准确的定性和定量描述是必要的。