Gaieb Zied, Morikis Dimitrios
Department of Bioengineering, University of California, Riverside 92521, USA.
Comput Struct Biotechnol J. 2017 Jan 14;15:131-137. doi: 10.1016/j.csbj.2017.01.001. eCollection 2017.
Structure and dynamics are essential elements of protein function. Protein structure is constantly fluctuating and undergoing conformational changes, which are captured by molecular dynamics (MD) simulations. We introduce a computational framework that provides a compact representation of the dynamic conformational space of biomolecular simulations. This method presents a systematic approach designed to reduce the large MD simulation spatiotemporal datasets into a manageable set in order to guide our understanding of how protein mechanics emerge from side chain organization and dynamic reorganization. We focus on the detection of side chain interactions that undergo rearrangements mediating global domain motions and vice versa. Side chain rearrangements are extracted from side chain interactions that undergo well-defined abrupt and persistent changes in distance time series using Gaussian mixture models, whereas global domain motions are detected using dynamic cross-correlation. Both side chain rearrangements and global domain motions represent the dynamic components of the protein MD simulation, and are both mapped into a network where they are connected based on their degree of coupling. This method allows for the study of allosteric communication in proteins by mapping out the protein dynamics into an intramolecular network to reduce the large simulation data into a manageable set of communities composed of coupled side chain rearrangements and global domain motions. This computational framework is suitable for the study of tightly packed proteins, such as G protein-coupled receptors, and we present an application on a seven microseconds MD trajectory of CC chemokine receptor 7 (CCR7) bound to its ligand CCL21.
结构与动力学是蛋白质功能的基本要素。蛋白质结构不断波动并经历构象变化，这些变化可通过分子动力学（MD）模拟捕捉。我们引入了一个计算框架，该框架可对生物分子模拟的动态构象空间提供紧凑表示。此方法提出了一种系统的途径，旨在将大型MD模拟时空数据集简化为可管理的集合，以指导我们理解蛋白质力学如何从侧链组织和动态重组中产生。我们专注于检测经历重排以介导全局结构域运动的侧链相互作用，反之亦然。使用高斯混合模型从距离时间序列中经历明确的突然且持续变化的侧链相互作用中提取侧链重排，而使用动态互相关检测全局结构域运动。侧链重排和全局结构域运动均代表蛋白质MD模拟的动态组成部分，并且都被映射到一个网络中，它们根据耦合程度相互连接。通过将蛋白质动力学映射到分子内网络，将大型模拟数据简化为由耦合的侧链重排和全局结构域运动组成的可管理的群落集合，此方法允许研究蛋白质中的变构通讯。这个计算框架适用于研究紧密堆积的蛋白质，如G蛋白偶联受体，我们展示了在与配体CCL21结合的CC趋化因子受体7（CCR7）的七微秒MD轨迹上的应用。
