Biomolecular dynamics play an important role in numerous biological processes. Markov State Models (MSMs) provide a powerful approach to study these dynamic processes by predicting long time scale dynamics based on many short molecular dynamics (MD) simulations. In an MSM, protein dynamics are modeled as a kinetic process consisting of a series of Markovian transitions between different conformational states at discrete time intervals (called “lag time”). To achieve this, a master equation must be constructed with a sufficiently long lag time to allow interstate transitions to become truly Markovian. This imposes a major challenge for MSM studies of proteins since the lag time is bound by the length of relatively short MD simulations available to estimate the frequency of transitions. Here, we show how one can employ the generalized master equation formalism to obtain an exact description of protein conformational dynamics both at short and long time scales without the time resolution restrictions imposed by the MSM lag time. Using a simple kinetic model, alanine dipeptide, and WW domain, we demonstrate that it is possible to construct these quasi-Markov State Models (qMSMs) using MD simulations that are 5–10 times shorter than those required by MSMs. These qMSMs only contain a handful of metastable states and, thus, can greatly facilitate the interpretation of mechanisms associated with protein dynamics. A qMSM opens the door to the study of conformational changes of complex biomolecules where a Markovian model with a few states is often difficult to construct due to the limited length of available MD simulations.

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关于利用马尔可夫状态模型中的生物分子动力学记忆的优势。

生物分子动力学在众多生物过程中起着重要作用。马尔可夫状态模型（MSM）通过基于许多短分子动力学（MD）模拟来预测长时间尺度的动力学，提供了研究这些动力学过程的有力方法。在MSM中，蛋白质动力学被建模为动力学过程，该动力学过程由离散时间间隔（称为“滞后时间”）的不同构象状态之间的一系列马尔可夫跃迁组成。为此，必须构造一个具有足够长的滞后时间的主方程，以使状态间转换成为真正的马尔可夫式。这对蛋白质的MSM研究提出了一个重大挑战，因为滞后时间受相对较短的MD模拟（可用于估计过渡频率）的长度限制。这里，我们展示了如何利用广义主方程式来获得对蛋白质构象动力学的精确描述，无论是短期还是长期尺度，而不受MSM滞后时间强加的时间分辨率限制。使用简单的动力学模型，丙氨酸二肽和WW域，我们证明可以使用比MSM所需的时间短5-10倍的MD模拟来构建这些准马氏状态模型（qMSM）。这些qMSM仅包含少数亚稳态，因此可以极大地促进与蛋白质动力学相关的机制的解释。qMSM为研究复杂生物分子的构象变化打开了一扇大门，由于可用的MD模拟的时间有限，通常很难构建具有少数状态的马尔可夫模型。