背景:在SARS-CoV-2的复制中,主要蛋白酶(Mpro/3CLpro)具有重要意义。它在许多新型冠状病毒变异中保守,并且没有已知的人类蛋白酶共享其切割位点。因此,3CLpro是一个理想的目标。在报告中,我们通过工作流程筛选了SARS-CoV-2Mpro的5种潜在抑制剂(1543,2308,3717,5606和9000).MM-GBSA结合自由能的计算表明,五种潜在抑制剂中的三种(1543、2308、5606)对SARS-CoV-2的Mpro具有与X77相似的抑制剂作用。总之,该手稿为Mpro抑制剂的设计奠定了基础。
方法:在虚拟筛选阶段,我们使用基于结构的虚拟筛选(Qvina2.1)和基于配体的虚拟筛选(AncPhore).在分子动力学模拟部分,我们使用Amber14SB+GAFF力场对复合物进行了100ns的分子动力学模拟(Gromacs2021.5),并根据模拟轨迹进行了MM-GBSA结合自由能计算。
BACKGROUND: In the replication of SARS-CoV-2, the main protease (Mpro/3CLpro) is significant. It is conserved in a number of novel coronavirus variations, and no known human proteases share its cleavage sites. Therefore, 3CLpro is an ideal target. In the report, we screened five potential inhibitors (1543, 2308, 3717, 5606, and 9000) of SARS-CoV-2 Mpro through a workflow. The calculation of MM-GBSA binding free energy showed that three of the five potential inhibitors (1543, 2308, 5606) had similar inhibitor effects to X77 against Mpro of SARS-CoV-2. In conclusion, the manuscript lays the groundwork for the design of Mpro inhibitors.
METHODS: In the virtual screening phase, we used structure-based virtual screening (Qvina2.1) and ligand-based virtual screening (AncPhore). In the molecular dynamic simulation part, we used the Amber14SB + GAFF force field to perform molecular dynamic simulation of the complex for 100 ns (Gromacs2021.5) and performed MM-GBSA binding free energy calculation according to the simulation trajectory.