A Data-Driven Perspective on the Hierarchical Assembly of Molecular Structures
A Data-Driven Perspective on the Hierarchical Assembly of Molecular Structures
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DOI：
10.1021/acs.jctc.7b00990
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发表时间：
2018-01-01
影响因子：
5.5
通讯作者：
Clementi, Cecilia
中科院分区：
文献类型：
--
作者：
Boninsegna, Lorenzo;Banisch, Ralf;Clementi, Cecilia
大分子体系是由大量的原子自由度组成的。有强有力的证据表明，在大的生物分子系统中发生的结构变化，在长时间尺度的动态可能会被捕获的模型比原子粗糙，虽然一个合适的或最佳的粗粒化是先验未知的。在这里，我们提出了一个系统的方法来学习一个粗糙的表示的大分子从微观模拟数据。特别是，有效的粗变量的定义是通过划分的结构（物理）空间和构象空间的自由度。在不同的亚稳态中形成动态相干态的微观粒子群的识别导致系统在空间和时间上的多尺度描述。这种方法的应用，两种蛋白质的折叠动力学提供了一个修订的观点的预结构化区域（foldons），联合收割机在蛋白质折叠过程中的经典思想，并提出了一个层次表征的组装过程中的折叠结构。
Macromolecular systems are composed of a very large number of atomic degrees of freedom. There is strong evidence suggesting that structural changes occurring in large biomolecular systems at long time scale dynamics may be captured by models coarser than atomistic, although a suitable or optimal coarse-graining is a priori unknown. Here we propose a systematic approach to learning a coarse representation of a macromolecule from microscopic simulation data. In particular, the definition of effective coarse variables is achieved by partitioning the degrees of freedom both in the structural (physical) space and in the conformational space. The identification of groups of microscopic particles forming dynamical coherent states in different metastable states leads to a multiscale description of the system, in space and time. The application of this approach to the folding dynamics of two proteins provides a revised view of the classical idea of prestructured regions (foldons) that combine during a protein-folding process and suggests a hierarchical characterization of the assembly process of folded structures.