Roth Christine-Andrea, Dreyfus Tom, Robert Charles H, Cazals Frédéric
Laboratoire De Biochimie Théorique, CNRS, UPR 9080, Univ Paris Diderot, Sorbonne Paris Cité, 13 Rue Pierre Et Marie Curie, Paris, 75005, France.
J Comput Chem. 2016 Mar 30;37(8):739-52. doi: 10.1002/jcc.24256. Epub 2015 Dec 29.
The number of local minima of the potential energy landscape (PEL) of molecular systems generally grows exponentially with the number of degrees of freedom, so that a crucial property of PEL exploration algorithms is their ability to identify local minima, which are low lying and diverse. In this work, we present a new exploration algorithm, retaining the ability of basin hopping (BH) to identify local minima, and that of transition based rapidly exploring random trees (T-RRT) to foster the exploration of yet unexplored regions. This ability is obtained by interleaving calls to the extension procedures of BH and T-RRT, and we show tuning the balance between these two types of calls allows the algorithm to focus on low lying regions. Computational efficiency is obtained using state-of-the art data structures, in particular for searching approximate nearest neighbors in metric spaces. We present results for the BLN69, a protein model whose conformational space has dimension 207 and whose PEL has been studied exhaustively. On this system, we show that the propensity of our algorithm to explore low lying regions of the landscape significantly outperforms those of BH and T-RRT.
分子系统势能面（PEL）的局部极小值数量通常随自由度数量呈指数增长，因此PEL探索算法的一个关键特性是其识别局部极小值的能力，这些局部极小值处于低位且具有多样性。在这项工作中，我们提出了一种新的探索算法，它保留了盆地跳跃（BH）识别局部极小值的能力，以及基于过渡的快速探索随机树（T - RRT）促进探索未探索区域的能力。这种能力是通过交错调用BH和T - RRT的扩展过程获得的，并且我们表明调整这两种类型调用之间的平衡可以使算法专注于低位区域。使用最先进的数据结构可获得计算效率，特别是用于在度量空间中搜索近似最近邻。我们给出了BLN69的结果，BLN69是一个蛋白质模型，其构象空间维度为207，并且其PEL已被详尽研究。在这个系统上，我们表明我们的算法探索势能面低位区域的倾向明显优于BH和T - RRT。
