Virtual screening of novel noncovalent inhibitors for SARS-CoV 3C-like proteinase

Zhenming Liu, Changkang Huang, Keqiang Fan, Ping Wei, Hao Chen, Shiyong Liu, Jianfeng Pei, Lei Shi, Bo Li, Kun Yang, Ying Liu, Luhua Lai

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


The SARS coronavirus 3C-like proteinase is considered as a potential drug design target for the treatment of severe acute respiratory syndrome (SARS). Owing to the lack of available drugs for the treatment of SARS, the discovery of inhibitors for SARS coronavirus 3C-like proteinase that can potentially be optimized as drugs appears to be highly desirable. We have built a "flexible" three-dimensional model for SARS 3C-like proteinase by homology modeling and multicanonical molecular dynamics method and used the model for virtual screening of chemical databases. After Dock procedures, strategies including pharmocophore model, consensus scoring, and "drug-like" filters were applied in order to accelerate the process and improve the success rate of virtual docking screening hit lists. Forty compounds were purchased and tested by HPLC and colorimetric assay against SARS 3C-like proteinase. Three of them including calmidazolium, a wellknown antagonist of calmodulin, were found to inhibit the enzyme with an apparent Ki from 61 to 178 μM. These active compounds and their binding modes provide useful information for understanding the binding sites and for further selective drug design against SARS and other coronavirus.

Original languageEnglish (US)
Pages (from-to)10-17
Number of pages8
JournalJournal of Chemical Information and Modeling
Issue number1
StatePublished - 2005
Externally publishedYes

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences


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