Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 30010, Taiwan.
Institute of Bioinformatics and Systems Biology, Department of Biological Science and Technology, Institute of Molecular Medicine and Bioengineering, Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
J Chem Phys. 2022 Jun 28;156(24):245105. doi: 10.1063/5.0095107.
The effects of inter-residue interactions on protein collective motions are analyzed by comparing two elastic network models (ENM)-structural contact ENM (SC-ENM) and molecular dynamics (MD)-ENM-with the edge weights computed from an all-atom MD trajectory by structure-mechanics statistical learning. A theoretical framework is devised to decompose the eigenvalues of ENM Hessian into contributions from individual springs and to compute the sensitivities of positional fluctuations and covariances to spring constant variation. Our linear perturbation approach quantifies the response mechanisms as softness modulation and orientation shift. All contacts of C positions in SC-ENM have an identical spring constant by fitting the profile of root-of-mean-squared-fluctuation calculated from an all-atom MD simulation, and the same trajectory data are also used to compute the specific spring constant of each contact as an MD-ENM edge weight. We illustrate that the soft-mode reorganization can be understood in terms of gaining weights along the structural contacts of low elastic strengths and loosing magnitude along those of high rigidities. With the diverse mechanical strengths encoded in protein dynamics, MD-ENM is found to have more pronounced long-range couplings and sensitivity responses with orientation shift identified as a key player in driving the specific residues to have high sensitivities. Furthermore, the responses of perturbing the springs of different residues are found to have asymmetry in the action-reaction relationship. In understanding the mutation effects on protein functional properties, such as long-range communications, our results point in the directions of collective motions as a major effector.
通过比较两种弹性网络模型（ENM）——结构接触 ENM（SC-ENM）和分子动力学（MD）-ENM，分析了残基间相互作用对蛋白质整体运动的影响，其中边缘权重是通过结构力学统计学习从全原子 MD 轨迹计算得到的。设计了一个理论框架，将 ENM Hessian 的特征值分解为单个弹簧的贡献，并计算位置波动和协方差对弹簧常数变化的敏感性。我们的线性微扰方法通过调制柔软度和改变方向来量化响应机制。在 SC-ENM 中，C 位的所有接触都具有相同的弹簧常数，方法是拟合从全原子 MD 模拟中计算得到的均方根波动轮廓，并且相同的轨迹数据也用于计算每个接触的特定弹簧常数，作为 MD-ENM 的边缘权重。我们表明，软模式重组可以通过增加低弹性强度结构接触的权重和减少高刚性结构接触的权重来理解。由于蛋白质动力学中具有不同的力学强度，因此 MD-ENM 被发现具有更明显的长程耦合和敏感性响应，并且方向变化被确定为驱动特定残基具有高敏感性的关键因素。此外，还发现扰动不同残基弹簧的响应在作用-反作用关系中具有不对称性。在理解突变对蛋白质功能特性的影响（如长程通讯）方面，我们的结果表明，集体运动是主要的效应因素。
