Generation of predictive pharmacophore model for SARS-coronavirus main proteinase.
Pharmacophore-based virtual screening is an effective, inexpensive and fast approach to discovering useful starting points for drug discovery. In this study, we developed a pharmacophore model for the main proteinase of severe acute respiratory syndrome coronavirus (SARS-CoV). Then we used this pharmacophore model to search NCI 3D database including 250, 251 compounds and identified 30 existing drugs containing the pharmacophore query. Among them are six compounds that already exhibited anti-SARS-CoV activity experimentally. This means that our pharmacophore model can lead to the discovery of potent anti-SARS-CoV inhibitors or promising lead compounds for further SARS-CoV main proteinase inhibitor development.
Zhang
Xue Wu
XW
Department of Bioinformatics, HKU-Pasteur Research Center, 8 Sassoon Road, Pokfulam, Hong Kong. xwzhang@hkucc.hku.hk
Yap
Yee Leng
YL
Altmeyer
Ralf M
RM
D000998:Antiviral Agents / Q000737:chemistry; D003198:Computer Simulation; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008958:Models, Molecular; D018716:Molecular Mimicry; D010447:Peptide Hydrolases / Q000737:chemistry*; D010455:Peptides / Q000737:chemistry; D011684:Purine Nucleosides / Q000737:chemistry; D011741:Pyrimidine Nucleosides / Q000737:chemistry; D045473:Severe acute respiratory syndrome-related coronavirus / Q000201:enzymology*; D014764:Viral Proteins / Q000737:chemistry
D000998:Antiviral Agents; D010455:Peptides; D011684:Purine Nucleosides; D011741:Pyrimidine Nucleosides; D014764:Viral Proteins; D010447:Peptide Hydrolases