盆地跳跃作为生物大分子表征的通用和通用优化框架

Brian S. Olson, I. Hashmi, Kevin Molloy, Amarda Shehu
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引用次数: 69

摘要

自引入以来,盆地跳跃(BH)框架已被证明对具有多变量和多模态的非线性优化问题非常有用。应用范围很广,从几何中的填充问题到统计物理中分子状态的表征。BH在计算结构生物学中重新出现,因为它能够根据局部极小值获得蛋白质能量表面的粗粒度表示。在本文中,我们证明了BH框架是通用的和通用的,由于其在高维变量空间中采样最小值的基本能力,可以解决与蛋白质结构、组装和运动表征相关的问题。我们展示了BH中主要组件的具体实现如何在从头计算蛋白质结构预测和刚性蛋白质-蛋白质对接的背景下获得最先进的结果的算法实现。我们还表明,BH可以映射与蛋白质分子中连接各种稳定功能相关状态的运动相关的中间最小值,从而作为表征连接这些状态的过渡轨迹的第一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Basin Hopping as a General and Versatile Optimization Framework for the Characterization of Biological Macromolecules
Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization of transition trajectories connecting these states.
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