Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing 100124, China.
J Chem Inf Model. 2021 Feb 22;61(2):921-937. doi: 10.1021/acs.jcim.0c01178. Epub 2021 Jan 26.
Dynamical properties of proteins play an essential role in their function exertion. The elastic network model (ENM) is an effective and efficient tool in characterizing the intrinsic dynamical properties encoded in biomacromolecule structures. The Gaussian network model (GNM) and anisotropic network model (ANM) are the two often-used ENM models. Here, we introduce an equally weighted multiscale ENM (equally weighted mENM) based on the original mENM (denoted as mENM), in which fitting weights of Kirchhoff/Hessian matrixes in mENM are removed since they neglect the details of pairwise interactions. Then, we perform its comparison with the mENM, traditional ENM, and parameter-free ENM (pfENM) in reproducing dynamical properties for the six representative proteins whose molecular dynamics (MD) trajectories are available in http://mmb.pcb.ub.es/MoDEL/. In the results, for B-factor prediction, mENM performs best, while the equally weighted mENM performs also well, better than the traditional ENM and pfENM models. As to the dynamical cross-correlation map calculation, mENM performs worst, while the results produced from the equally weighted mENM and pfENM models are close to those from MD trajectories with the latter a little better than the former. Furthermore, encouragingly, the equally weighted mANM displays the best performance in capturing the functional motional modes, followed by pfANM and traditional ANM models, while the mANM fails in all the cases. This work is helpful for strengthening the understanding of the elastic network model and provides a valuable guide for researchers to utilize the model to explore protein dynamics.
蛋白质的动力学性质在其功能发挥中起着至关重要的作用。弹性网络模型（ENM）是一种有效和高效的工具，可用于描述生物大分子结构中编码的固有动力学性质。高斯网络模型（GNM）和各向异性网络模型（ANM）是两种常用的 ENM 模型。在这里，我们引入了一种基于原始 mENM 的等权重多尺度 ENM（简称等权重 mENM），其中去除了 mENM 中 Kirchhoff/Hessian 矩阵的拟合权重，因为它们忽略了成对相互作用的细节。然后，我们将其与 mENM、传统 ENM 和无参数 ENM（pfENM）在六个具有分子动力学（MD）轨迹的代表性蛋白质的动力学性质再现方面进行比较，这些轨迹可在 http://mmb.pcb.ub.es/MoDEL/ 上获得。在结果中，对于 B 因子预测，mENM 表现最好，而等权重 mENM 表现也很好，优于传统 ENM 和 pfENM 模型。至于动力学互相关图的计算，mENM 表现最差，而等权重 mENM 和 pfENM 模型产生的结果与 MD 轨迹非常接近，后者比前者略好。此外，令人鼓舞的是，等权重 mANM 在捕捉功能运动模式方面表现最佳，其次是 pfANM 和传统 ANM 模型，而 mANM 在所有情况下都失败了。这项工作有助于加强对弹性网络模型的理解，并为研究人员利用该模型探索蛋白质动力学提供了有价值的指导。
