WExplore: Hierarchical Exploration of High-Dimensional Spaces Using the Weighted Ensemble Algorithm

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• 作者：Alex Dickson ; Charles L. Brooks ; III
• 刊名：Journal of Physical Chemistry B
• 出版年：2014
• 出版时间：April 3, 2014
• 年：2014
• 卷：118
• 期：13
• 页码：3532-3542
• 全文大小：750K
• 年卷期：v.118,no.13(April 3, 2014)
• ISSN：1520-5207

文摘

As most relevant motions in biomolecular systems are inaccessible to conventional molecular dynamics simulations, algorithms that enhance sampling of rare events are indispensable. Increasing interest in intrinsically disordered systems and the desire to target ensembles of protein conformations (rather than single structures) in drug development motivate the need for enhanced sampling algorithms that are not limited to 鈥渢wo-basin鈥?problems, and can efficiently determine structural ensembles. For systems that are not well-studied, this must often be done with little or no information about the dynamics of interest. Here we present a novel strategy to determine structural ensembles that uses dynamically defined sampling regions that are organized in a hierarchical framework. It is based on the weighted ensemble algorithm, where an ensemble of copies of the system (鈥渞eplicas鈥? is directed to new regions of configuration space through merging and cloning operations. The sampling hierarchy allows for a large number of regions to be defined, while using only a small number of replicas that can be balanced over multiple length scales. We demonstrate this algorithm on two model systems that are analytically solvable and examine the 10-residue peptide chignolin in explicit solvent. The latter system is analyzed using a configuration space network, and novel hydrogen bonds are found that facilitate folding.