利用天然产物库虚拟筛选方法设计严重急性呼吸综合征冠状病毒蛋白酶抑制剂
SARS-CoV protease inhibitors design using virtual screening method from natural products libraries.
作者信息
Liu Bing, Zhou Jiaju
机构信息
State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences Beijing 100080, People's Republic of China.
出版信息
J Comput Chem. 2005 Apr 15;26(5):484-90. doi: 10.1002/jcc.20186.
Abstract
Two natural products databases, the marine natural products database (MNPD) and the traditional Chinese medicines database (TCMD), were used to find novel structures of potent SARS-CoV protease inhibitors through virtual screening. Before the procedure, the databases were filtered by Lipinski's ROF and Xu's extension rules. The results were analyzed by statistic methods to eliminate the bias in target-based database screening toward higher molecular weight compounds for enhancing the hit rate. Eighteen lead compounds were recommended by the screening procedure. They were useful for experimental scientists in prioritizing drug candidates and studying the interaction mechanism. The binding mechanism was also analyzed between the best screening compound and the SARS protein.
摘要
利用两个天然产物数据库，即海洋天然产物数据库（MNPD）和中药数据库（TCMD），通过虚拟筛选来寻找强效SARS-CoV蛋白酶抑制剂的新结构。在进行该过程之前，依据Lipinski的ROF规则和徐氏扩展规则对数据库进行筛选。采用统计方法对结果进行分析，以消除基于靶点的数据库筛选中对高分子量化合物的偏向，从而提高命中率。筛选程序推荐了18种先导化合物。它们有助于实验科学家对候选药物进行优先级排序并研究相互作用机制。还分析了最佳筛选化合物与SARS蛋白之间的结合机制。
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