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arXiv 1806.06804cond-mat.soft
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生物分子弹性网络模型中的粗粒度最优选址

Optimal coarse-grained site selection in elastic network models of biomolecules

Patrick Diggins, Changjiang Liu, Markus Deserno, Raffaello Potestio

摘要:

弹性网络模型是基于简单结构的生物分子表示,其中原子通过短程谐波势相互作用,以极低的计算成本提供了对分子内部动力学和机械特性的深入了解。它们的效率和有效性使其成为计算机辅助研究蛋白质的关键工具,几年来,也是核酸的关键工具。一般来说,粗粒位点,即所有原子结构映射到的那些有效力中心,是基于直觉规则构建的:蛋白质的典型选择是仅保留每个氨基酸的C $_α$原子。然而,与更仔细的选择相比,仅依赖于原子类型而不是其嵌入的局部属性的映射策略可能是次优的。在这里,我们提出了一种策略,其中原子的子集,每个原子被映射到一个独特的粗粒度的网站的模型,选择在随机搜索,旨在优化成本函数。后者被认为是一个简单的措施之间的一致性的谐波近似的弹性网络模型和谐波模型通过精确集成的丢弃的自由度。该方法适用于两个代表性的结构非常不同类型的生物分子:蛋白质腺苷酸激酶和RNA分子腺嘌呤核糖开关。我们的分析量化了算法驱动的粗粒度站点选择对模型属性的实质性影响。

英文摘要:

Elastic network models, simple structure-based representations of biomolecules where atoms interact via short-range harmonic potentials, provide great insight into a molecule's internal dynamics and mechanical properties at extremely low computational cost. Their efficiency and effectiveness have made them a pivotal instrument in the computer-aided study of proteins and, since a few years, also of nucleic acids. In general, the coarse-grained sites, i.e. those effective force centres onto which the all-atom structure is mapped, are constructed based on intuitive rules: a typical choice for proteins is to retain only the C$_α$ atoms of each amino acid. However, a mapping strategy relying only on the atom type and not the local properties of its embedding can be suboptimal compared to a more careful selection. Here we present a strategy in which the subset of atoms, each of which is mapped onto a unique coarse-grained site of the model, is selected in a stochastic search aimed at optimising a cost function. The latter is taken to be a simple measure of the consistency between the harmonic approximation of an elastic network model and the harmonic model obtained through exact integration of the discarded degrees of freedom. The method is applied to two representatives of structurally very different types of biomolecules: the protein Adenylate kinase and the RNA molecule adenine riboswitch. Our analysis quantifies the substantial impact that an algorithm-driven selection of coarse-grained sites can have on a model's properties.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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