Department of Chemistry, Yale University, 225 Prospect Street, New Haven, Connecticut 06520, USA.
J Chem Phys. 2023 Jun 7;158(21). doi: 10.1063/5.0140453.
Many biological processes are regulated by allosteric mechanisms that communicate with distant sites in the protein responsible for functionality. The binding of a small molecule at an allosteric site typically induces conformational changes that propagate through the protein along allosteric pathways regulating enzymatic activity. Elucidating those communication pathways from allosteric sites to orthosteric sites is, therefore, essential to gain insights into biochemical processes. Targeting the allosteric pathways by mutagenesis can allow the engineering of proteins with desired functions. Furthermore, binding small molecule modulators along the allosteric pathways is a viable approach to target reactions using allosteric inhibitors/activators with temporal and spatial selectivity. Methods based on network theory can elucidate protein communication networks through the analysis of pairwise correlations observed in molecular dynamics (MD) simulations using molecular descriptors that serve as proxies for allosteric information. Typically, single atomic descriptors such as α-carbon displacements are used as proxies for allosteric information. Therefore, allosteric networks are based on correlations revealed by that descriptor. Here, we introduce a Python software package that provides a comprehensive toolkit for studying allostery from MD simulations of biochemical systems. MDiGest offers the ability to describe protein dynamics by combining different approaches, such as correlations of atomic displacements or dihedral angles, as well as a novel approach based on the correlation of Kabsch-Sander electrostatic couplings. MDiGest allows for comparisons of networks and community structures that capture physical information relevant to allostery. Multiple complementary tools for studying essential dynamics include principal component analysis, root mean square fluctuation, as well as secondary structure-based analyses.
许多生物过程受到变构机制的调节,这些机制与负责功能的蛋白质中的远程位点进行通讯。小分子在变构位点的结合通常会诱导构象变化,这些变化沿着变构途径传播,从而调节酶活性。因此,阐明变构位点到正构位点的这些通讯途径对于深入了解生化过程至关重要。通过突变使变构途径失活可以使具有所需功能的蛋白质的工程设计成为可能。此外,沿着变构途径结合小分子调节剂是一种可行的方法,可以使用具有时间和空间选择性的变构抑制剂/激活剂来靶向反应。基于网络理论的方法可以通过分析使用分子描述符作为变构信息代理的分子动力学 (MD) 模拟中观察到的成对相关性来阐明蛋白质通讯网络。通常,使用单个原子描述符(如α-碳位移)作为变构信息的代理。因此,变构网络基于该描述符揭示的相关性。在这里,我们引入了一个 Python 软件包,该软件包为从生化系统的 MD 模拟中研究变构提供了全面的工具包。MDigest 提供了通过结合不同方法来描述蛋白质动力学的能力,例如原子位移或二面角的相关性,以及一种基于 Kabsch-Sander 静电耦合相关性的新方法。MDigest 允许对捕获与变构相关的物理信息的网络和社区结构进行比较。用于研究基本动力学的多个补充工具包括主成分分析、均方根波动以及基于二级结构的分析。