Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions.
Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions.
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DOI：
10.1021/ct300978b
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发表时间：
2013-04-09
影响因子：
5.5
通讯作者：
Berneche, Simon
中科院分区：
文献类型：
--
作者：
Wojtas-Niziurski, Wojciech;Meng, Yilin;Roux, Benoit;Berneche, Simon
描述生物分子构象变化的平均力势是决定生物分子系统功能的中心量。计算依赖于三个或更多反应坐标的过程的能量景观可能需要大量的计算能力，使得一些多维计算实际上是不可能的。在这里，我们提出了一个有效的自动伞抽样策略计算多维潜力的平均力。该方法通过反馈机制逐步学习多维空间的哪些区域值得探索，并自动生成一组适合系统的伞形采样窗口。自学习自适应伞形采样方法首先解释与说明性的例子的基础上简化的模型系统，然后应用到两个非平凡的情况：五肽甲硫氨酸脑啡肽在溶液中的构象平衡和离子渗透的KcsA钾通道。用这种方法，它表明，一个显着更少的伞窗口需要采用的自由能景观在最相关的地区没有任何损失的准确性进行表征。
The potential of mean force describing conformational changes of biomolecules is a central quantity that determines the function of biomolecular systems. Calculating an energy landscape of a process that depends on three or more reaction coordinates might require a lot of computational power, making some of multidimensional calculations practically impossible. Here, we present an efficient automatized umbrella sampling strategy for calculating multidimensional potential of mean force. The method progressively learns by itself, through a feedback mechanism, which regions of a multidimensional space are worth exploring and automatically generates a set of umbrella sampling windows that is adapted to the system. The self-learning adaptive umbrella sampling method is first explained with illustrative examples based on simplified reduced model systems, and then applied to two non-trivial situations: the conformational equilibrium of the pentapeptide Met-enkephalin in solution and ion permeation in the KcsA potassium channel. With this method, it is demonstrated that a significant smaller number of umbrella windows needs to be employed to characterize the free energy landscape over the most relevant regions without any loss in accuracy.
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影响因子：
56.9
作者：
Doyle, DA;Cabral, JM;MacKinnon, R
通讯作者：
MacKinnon, R
影响因子：
4.1
作者：
MEZEI, M
通讯作者：
MEZEI, M
影响因子：
4.4
作者：
Maragliano, Luca;Fischer, Alexander;Ciccotti, Giovanni
通讯作者：
Ciccotti, Giovanni
影响因子：
2.9
作者：
HOOVER, WG
通讯作者：
HOOVER, WG
影响因子：
4.4
作者：
FELLER, SE;ZHANG, YH;BROOKS, BR
通讯作者：
BROOKS, BR