Zhang Weihong, Howell Steven C, Wright David W, Heindel Andrew, Qiu Xiangyun, Chen Jianhan, Curtis Joseph E
Kansas State University, Manhattan, KS, USA.
NIST Center for Neutron Research, 100 Bureau Drive, Gaithersburg, MD, USA.
J Mol Graph Model. 2017 May;73:179-190. doi: 10.1016/j.jmgm.2017.02.010. Epub 2017 Feb 23.
We describe a general method to use Monte Carlo simulation followed by torsion-angle molecular dynamics simulations to create ensembles of structures to model a wide variety of soft-matter biological systems. Our particular emphasis is focused on modeling low-resolution small-angle scattering and reflectivity structural data. We provide examples of this method applied to HIV-1 Gag protein and derived fragment proteins, TraI protein, linear B-DNA, a nucleosome core particle, and a glycosylated monoclonal antibody. This procedure will enable a large community of researchers to model low-resolution experimental data with greater accuracy by using robust physics based simulation and sampling methods which are a significant improvement over traditional methods used to interpret such data.
我们描述了一种通用方法，即先使用蒙特卡罗模拟，再进行扭转角分子动力学模拟，以创建结构集合，从而对各种软物质生物系统进行建模。我们特别强调对低分辨率小角散射和反射率结构数据进行建模。我们提供了该方法应用于HIV-1 Gag蛋白及其衍生片段蛋白、TraI蛋白、线性B-DNA、核小体核心颗粒和糖基化单克隆抗体的示例。通过使用基于强大物理学的模拟和采样方法，此过程将使大量研究人员能够更准确地对低分辨率实验数据进行建模，这相较于用于解释此类数据的传统方法有显著改进。
