Institute for Fundamental Science and Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97403, USA.
J Chem Phys. 2021 Dec 28;155(24):244108. doi: 10.1063/5.0059688.
Molecular Dynamics (MD) simulations of proteins implicitly contain the information connecting the atomistic molecular structure and proteins' biologically relevant motion, where large-scale fluctuations are deemed to guide folding and function. In the complex multiscale processes described by MD trajectories, it is difficult to identify, separate, and study those large-scale fluctuations. This problem can be formulated as the need to identify a small number of collective variables that guide the slow kinetic processes. The most promising method among the ones used to study the slow leading processes in proteins' dynamics is the time-structure based on time-lagged independent component analysis (tICA), which identifies the dominant components in a noisy signal. Recently, we developed an anisotropic Langevin approach for the dynamics of proteins, called the anisotropic Langevin Equation for Protein Dynamics or LE4PD-XYZ. This approach partitions the protein's MD dynamics into mostly uncorrelated, wavelength-dependent, diffusive modes. It associates with each mode a free-energy map, where one measures the spatial extension and the time evolution of the mode-dependent, slow dynamical fluctuations. Here, we compare the tICA modes' predictions with the collective LE4PD-XYZ modes. We observe that the two methods consistently identify the nature and extension of the slowest fluctuation processes. The tICA separates the leading processes in a smaller number of slow modes than the LE4PD does. The LE4PD provides time-dependent information at short times and a formal connection to the physics of the kinetic processes that are missing in the pure statistical analysis of tICA.
分子动力学（MD）模拟蛋白隐含地包含了连接原子分子结构和蛋白生物相关运动的信息，其中大规模波动被认为指导了折叠和功能。在 MD 轨迹描述的复杂多尺度过程中，很难识别、分离和研究这些大规模波动。这个问题可以被表述为需要识别少数能够引导慢动力学过程的集体变量。在研究蛋白动力学中慢主导过程的最有前途的方法之一是基于时滞独立成分分析（tICA）的时间结构方法，它可以识别噪声信号中的主导成分。最近，我们开发了一种用于蛋白动力学的各向异性 Langevin 方法，称为蛋白动力学各向异性 Langevin 方程或 LE4PD-XYZ。该方法将蛋白的 MD 动力学分为大部分不相关、波长相关、扩散模式。它为每个模式关联一个自由能图，其中可以测量模式相关的、慢动力学波动的空间扩展和时间演化。在这里，我们将 tICA 模式的预测与集体 LE4PD-XYZ 模式进行了比较。我们观察到两种方法一致地识别了最慢波动过程的性质和扩展。tICA 比 LE4PD 用更少的慢模式分离出主导过程。LE4PD 提供了短时间内的时变信息，并与动力学过程的物理性质建立了正式联系，而 tICA 的纯统计分析则缺乏这种联系。
