Multidimensional Langevin modeling of biomolecular dynamics
Multidimensional Langevin modeling of biomolecular dynamics
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DOI：
10.1063/1.3058436
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发表时间：
2009-01-21
影响因子：
4.4
通讯作者：
Stock, Gerhard
中科院分区：
文献类型：
--
作者：
Hegger, Rainer;Stock, Gerhard
提出了一种系统的计算方法来描述生物分子的降维构象动力学。该方法是基于（i）的高维分子动力学轨迹分解成几个“系统”和（许多）“浴”的自由度和（ii）的Langevin模拟所得到的模型。采用主成分分析，系统的尺寸被选择为使得它包含分子的所有缓慢的大振幅运动，而浴坐标仅考虑其高频波动。结果表明，一个足够大的尺寸的模型是必不可少的，以确保一个明确的时间尺度分离的系统和浴变量，这保证了无记忆Langevin方程的有效性。应用非线性时间序列分析的方法，提出了一种实用的Langevin算法，该算法对描述确定性漂移和随机驱动的多维Langevin向量场进行局部估计。采用800 ns的分子动力学模拟的折叠七丙氨酸在显式水，它表明，一个五维Langevin模型正确地再现了系统的结构和构象动力学。该方法的优点和局限性进行了详细讨论。
A systematic computational approach to describe the conformational dynamics of biomolecules in reduced dimensionality is presented. The method is based on (i) the decomposition of a high-dimensional molecular dynamics trajectory into a few "system" and (many) "bath" degrees of freedom and (ii) a Langevin simulation of the resulting model. Employing principal component analysis, the dimension of the system is chosen such that it contains all slow large-amplitude motions of the molecule, while the bath coordinates only account for its high-frequency fluctuations. It is shown that a sufficiently large dimension of the model is essential to ensure a clear time scale separation of system and bath variables, which warrants the validity of the memory-free Langevin equation. Applying methods from nonlinear time series analysis, a practical Langevin algorithm is presented which performs a local estimation of the multidimensional Langevin vector fields describing deterministic drift and stochastic driving. Adopting a 800 ns molecular dynamics simulation of the folding of heptaalanine in explicit water, it is shown that a five-dimensional Langevin model correctly reproduces the structure and conformational dynamics of the system. The virtues and limits of the approach are discussed in some detail.