SARS-CoV protease inhibitors design using virtual screening method from natural products libraries.
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.
Liu
Bing
B
State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences Beijing 100080, People's Republic of China.
Zhou
Jiaju
J
D000465:Algorithms; D001688:Biological Products / Q000737:chemistry*; D020650:Combinatorial Chemistry Techniques; D000086782:Coronavirus 3C Proteases; D003546:Cysteine Endopeptidases; D016208:Databases, Factual; D015195:Drug Design; D004353:Drug Evaluation, Preclinical; D010450:Endopeptidases; D008516:Medicine, Chinese Traditional; D008958:Models, Molecular; D008968:Molecular Conformation; D015394:Molecular Structure; D011480:Protease Inhibitors / Q000737:chemistry*; D045473:Severe acute respiratory syndrome-related coronavirus / Q000201:enzymology*; D014764:Viral Proteins / Q000037:antagonists & inhibitors*
D001688:Biological Products; D011480:Protease Inhibitors; D014764:Viral Proteins; D010450:Endopeptidases; D003546:Cysteine Endopeptidases; D000086782:Coronavirus 3C Proteases