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首页> 外文期刊>The Journal of Chemical Physics >Relaxation mode analysis and Markov state relaxation mode analysis for chignolin in aqueous solution near a transition temperature
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Relaxation mode analysis and Markov state relaxation mode analysis for chignolin in aqueous solution near a transition temperature

机译：接近转变温度的chignolin的弛豫模式分析和马尔可夫状态弛豫模式分析

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It is important to extract reaction coordinates or order parameters from protein simulations in order to investigate the local minimum-energy states and the transitions between them. The most popular method to obtain such data is principal component analysis, which extracts modes of large conformational fluctuations around an average structure. We recently applied relaxation mode analysis for protein systems, which approximately estimates the slow relaxation modes and times from a simulation and enables investigations of the dynamic properties underlying the structural fluctuations of proteins. In this study, we apply this relaxation mode analysis to extract reaction coordinates for a system in which there are large conformational changes such as those commonly observed in protein folding/unfolding. We performed a 750-ns simulation of chignolin protein near its folding transition temperature and observed many transitions between the most stable, misfolded, intermediate, and unfolded states. We then applied principal component analysis and relaxation mode analysis to the system. In the relaxation mode analysis, we could automatically extract good reaction coordinates. The free-energy surfaces provide a clearer understanding of the transitions not only between local minimum-energy states but also between the folded and unfolded states, even though the simulation involved large conformational changes. Moreover, we propose a new analysis method called Markov state relaxation mode analysis. We applied the new method to states with slow relaxation, which are defined by the free-energy surface obtained in the relaxation mode analysis. Finally, the relaxation times of the states obtained with a simple Markov state model and the proposed Markov state relaxation mode analysis are compared and discussed. (C) 2015 AIP Publishing LLC.
机译：从蛋白质模拟中提取反应坐标或有序参数很重要，以便研究局部最小能量状态及其之间的过渡。获取此类数据的最流行方法是主成分分析，该方法提取了围绕平均结构的较大构象波动的模式。我们最近对蛋白质系统应用了松弛模式分析，它可以通过模拟近似估算慢速松弛模式和时间，并能够研究蛋白质结构波动背后的动态特性。在这项研究中，我们应用这种松弛模式分析来提取系统的反应坐标，在该系统中，构象变化很大，例如在蛋白质折叠/解折叠中通常会观察到。我们在折叠温度附近对其进行了750 ns的chignolin蛋白模拟，观察到了最稳定，折叠错误，中间和未折叠状态之间的许多过渡。然后，我们将主成分分析和松弛模式分析应用于系统。在松弛模式分析中，我们可以自动提取良好的反应坐标。即使模拟中涉及大的构象变化，自由能表面也不仅使局部最小能量状态之间的过渡，而且使折叠状态和展开状态之间的过渡更清晰地理解。此外，我们提出了一种新的分析方法，称为马尔可夫状态弛豫模式分析。我们将新方法应用于慢弛豫状态，该状态由弛豫模式分析中获得的自由能表面定义。最后，比较并讨论了使用简单马尔可夫状态模型获得的状态的弛豫时间以及提出的马尔可夫状态弛豫模式分析。 （C）2015 AIP Publishing LLC。

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