Mobaraki Nabiollah, Hemmateenejad Bahram, Weikl Thomas R, Sakhteman Amirhossein
Chemistry Department, Shiraz University, Shiraz, Iran.
Chemistry Department, Shiraz University, Shiraz, Iran; Max Planck Institute of Colloids and Interfaces, Department of Theory and Bio-Systems, Potsdam, Germany.
J Mol Graph Model. 2019 Sep;91:186-193. doi: 10.1016/j.jmgm.2019.06.011. Epub 2019 Jun 15.
We present a detailed investigation of the effect of the crystallographic structure of the HIV-1 protease (PR) on the binding energy of different classes of inhibitors obtained from docking simulations. The crystal structures of 222 HIV-1 proteases (in wild-type and mutant forms) and 202 inhibitors were downloaded from appropriate databases. A cross-docking approach (docking of all 202 inhibitors to all 222 PR structures) using Autodock Vina was implemented. The protease structures were clustered using a Kohonen self-organization map analysis of the data matrix of docking energies. The obtained clusters of PRs were correlated with the x-y-z coordinates of the PR structures to identify structural segments underlying this clustering. The PR structures were clustered into 4 classes. One of these classes exhibits rather strong binding with almost all inhibitors, while another class exhibits rather weak binding. The remaining two classes are intermediate in binding strength. The selectivity ratio indices for the carbon-alpha atoms of the PR structures indicate that conformational motion of residues outside the binding pocket contributes significantly to the discrimination of the 4 classes.
我们对HIV-1蛋白酶（PR）的晶体结构对通过对接模拟获得的不同类别抑制剂结合能的影响进行了详细研究。从适当的数据库下载了222种HIV-1蛋白酶（野生型和突变型）的晶体结构以及202种抑制剂。采用Autodock Vina实施了一种交叉对接方法（将所有202种抑制剂对接至所有222种PR结构）。利用对接能量数据矩阵的Kohonen自组织映射分析对蛋白酶结构进行聚类。将获得的PR聚类与PR结构的x-y-z坐标相关联，以识别该聚类背后的结构片段。PR结构被聚类为4类。其中一类与几乎所有抑制剂表现出相当强的结合，而另一类表现出相当弱的结合。其余两类的结合强度处于中间水平。PR结构中碳α原子的选择性比率指数表明，结合口袋外残基的构象运动对这4类的区分有显著贡献。
