Theoretical Molecular Biophysics, Department of Physics, Freie Universität Berlin, Arnimallee 14, D-14195 Berlin, Germany.
Laboratory of Biomolecular Research, Department of Biology and Chemistry, Paul Scherrer Institut, ETH Zürich, 5303 Villigen-PSI, Switzerland.
J Chem Inf Model. 2021 Nov 22;61(11):5692-5707. doi: 10.1021/acs.jcim.1c00827. Epub 2021 Oct 20.
Dynamic hydrogen-bond networks provide proteins with structural plasticity required to translate signals such as ligand binding into a cellular response or to transport ions and larger solutes across membranes and, thus, are of central interest to understand protein reaction mechanisms. Here, we present C-Graphs, an efficient tool with graphical user interface that analyzes data sets of static protein structures or of independent numerical simulations to identify conserved, vs unique, hydrogen bonds and hydrogen-bond networks. For static structures, which may belong to the same protein or to proteins with different sequences, C-Graphs uses a clustering algorithm to identify sites of the hydrogen-bond network where waters are conserved among the structures. Using C-Graphs, we identify an internal protein-water hydrogen-bond network common to static structures of visual rhodopsins and adenosine A2A G protein-coupled receptors (GPCRs). Molecular dynamics simulations of a visual rhodopsin indicate that the conserved hydrogen-bond network from static structure can recruit dynamic hydrogen bonds and extend throughout most of the receptor. We release with this work the code for C-Graphs and its graphical user interface.
动态氢键网络为蛋白质提供了结构可塑性，使它们能够将信号（如配体结合）转化为细胞反应，或者将离子和更大的溶质运输穿过膜，因此，理解蛋白质反应机制是至关重要的。在这里，我们介绍了 C-Graphs，这是一种带有图形用户界面的高效工具，可以分析静态蛋白质结构或独立数值模拟的数据集，以识别保守的、独特的氢键和氢键网络。对于静态结构，它们可能属于同一蛋白质或具有不同序列的蛋白质，C-Graphs 使用聚类算法来识别氢键网络中水分子在结构之间保守的位点。使用 C-Graphs，我们确定了视觉视紫红质和腺苷 A2A G 蛋白偶联受体（GPCR）的静态结构中共同存在的内部蛋白质-水氢键网络。视觉视紫红质的分子动力学模拟表明，来自静态结构的保守氢键网络可以招募动态氢键，并延伸到受体的大部分区域。我们在此工作中发布了 C-Graphs 的代码及其图形用户界面。
