Efficient and Unbiased Sampling of Biomolecular Systems in the Canonical Ensemble: A Review of Self-Guided Langevin Dynamics.
Efficient and Unbiased Sampling of Biomolecular Systems in the Canonical Ensemble: A Review of Self-Guided Langevin Dynamics.
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规范系综中生物分子系统的高效和无偏采样：自引导朗之万动力学综述。
DOI：
10.1002/9781118197714.ch6
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发表时间：
2012
影响因子：
--
通讯作者：
Brooks,BernardR
中科院分区：
文献类型：
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作者：
Wu,Xiongwu;Damjanovic,Ana;Brooks,BernardR
构象搜索在模拟系统中是有问题的，其中填充状态要么被能量障碍或动力学瓶颈分开，要么分布在与重大构象变化相对应的距离上。在生物系统中，由于蛋白质或DNA等生物分子具有巨大的构象空间和众多的能垒，构象搜索非常具有挑战性。生物学相关事件，如蛋白质折叠[1]、配体结合、构象信号转导等，发生的时间远远超过目前现实模拟所能达到的时间。几十年来，大分子的构象搜索问题一直是人们努力研究的课题。有许多方法和方法，每种方法都有不同的优点和缺点，并且有几篇综述文章很好地调查了这些方法[3-9]。在众多高效构象搜索方法中，自导向分子动力学（self-guided molecular dynamics， SGMD）[10,11]和自导向朗格万动力学（self-guided Langevin dynamics， SGLD）[12-14]模拟方法比较独特。术语“自我引导”指的是在模拟过程中学习到的信息用于增强同一模拟的构象搜索的方式。这些方法的核心是利用力和动量的局部平均作为引导力，以加速障碍的跨越，同时也能保持规范系综。尽管这些方法已经在Norberg和Nilsson [8]， Tai [3]， Christen和Van Gunsteren[3]的研究中讨论过，但本章提供了一个更完整的描述，包括最近的发展。为了更好地理解SGLD与许多其他抽样和搜索方法的关系，有必要通过考虑以下九个问题对抽样方法进行分类，并将SGLD与其他方法进行比较：
Conformational search is problematic for simulation systems where populated states are either separated by energy barriers or kinetic bottlenecks or are spread across a distance that corresponds to significant conformational changes. In biological systems, conformational search is very challenging because biological molecules such as proteins or DNA have a huge conformational space and numerous energy barriers. Biological relevant events, such as protein folding [1], ligand binding, conformational signal transduction, and so on, occur in a timescale far exceeding that accessible by current realistic simulations [2]. The conformation search problem for macromolecules has been the subject of intense efforts for many decades. There are numerous methods and approaches, each with various strengths and weaknesses, and there are several review articles that survey these methods rather well [3–9]. Among the many methods for efficient conformational search, the self-guided molecular dynamic (SGMD)[10, 11] and the self-guided Langevin dynamics (SGLD)[12–14] simulation methods are somewhat unique. The term “self-guided” refers to the manner in which the information learned during a simulation is used to enhance the conformational search of the very same simulation. The core of these methods is the use of local averages of force and momentum as a guiding force that accelerates barrier crossing in a manner that can also preserve the canonical ensemble. Even though these methods have been discussed in studies by Norberg and Nilsson [8], Tai [9], and Christen and Van Gunsteren [3], this chapter presents a more complete description that includes recent developments. To better understand how SGLD relates to the many other sampling and search methods, it is worthwhile to categorize sampling methods by considering the following nine questions for which we contrast SGLD with alternative methods: