Using sketch-map coordinates to analyze and bias molecular dynamics simulations
Using sketch-map coordinates to analyze and bias molecular dynamics simulations
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DOI：
10.1073/pnas.1201152109
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发表时间：
2012-04-03
影响因子：
11.1
通讯作者：
Parrinello, Michele
中科院分区：
文献类型：
--
作者：
Tribello, Gareth A.;Ceriotti, Michele;Parrinello, Michele
当研究复杂的问题时，例如蛋白质的折叠，系统的粗粒度描述驱动我们的研究，并帮助我们合理化结果。通常情况下，通过对感兴趣的过程的化学直觉得出的集体变量（CV）可以达到这个目的。因为找到这些CV是任何研究中最困难的部分，所以我们最近开发了一种降维算法sketch-map，可以用来构建高维相空间的低维映射。在本文中，我们将讨论如何使用这些机器生成的CV来加速相空间的探索和重建自由能景观。要做到这一点，我们开发了一种形式主义，其中高维配置不再表示为低维的位置向量。相反，对于每个配置，我们计算一个概率分布，它有一个包含整个低维空间的域。为了构建一个偏置电位，我们利用一个类比与metadaptics和使用的轨迹，自适应地构建一个排斥，历史依赖的偏见，从对应于以前访问的配置的分布。这种势能迫使系统通过采用其分布不与偏置重叠的配置来探索更多的相空间。我们将此算法应用到一个小的模型蛋白质，并成功地复制的自由能表面，我们从一个并行回火计算。
When examining complex problems, such as the folding of proteins, coarse grained descriptions of the system drive our investigation and help us to rationalize the results. Oftentimes collective variables (CVs), derived through some chemical intuition about the process of interest, serve this purpose. Because finding these CVs is the most difficult part of any investigation, we recently developed a dimensionality reduction algorithm, sketch-map, that can be used to build a low-dimensional map of a phase space of high-dimensionality. In this paper we discuss how these machine-generated CVs can be used to accelerate the exploration of phase space and to reconstruct free-energy landscapes. To do so, we develop a formalism in which high-dimensional configurations are no longer represented by low-dimensional position vectors. Instead, for each configuration we calculate a probability distribution, which has a domain that encompasses the entirety of the low-dimensional space. To construct a biasing potential, we exploit an analogy with metadynamics and use the trajectory to adaptively construct a repulsive, history-dependent bias from the distributions that correspond to the previously visited configurations. This potential forces the system to explore more of phase space by making it desirable to adopt configurations whose distributions do not overlap with the bias. We apply this algorithm to a small model protein and succeed in reproducing the free-energy surface that we obtain from a parallel tempering calculation.