Title

Repurposing of known anti-virals as potential inhibitors for SARS-CoV-2 main protease using molecular docking analysis
 

Authors

Mohammed Hakmi¹, El Mehdi Bouricha¹, Ilham Kandoussi¹, Jaouad El Harti² & Azeddine Ibrahimi¹*

Affiliation

¹Medical Biotechnology Laboratory (MedBiotech), Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Morocco; ²Therapeutic Chemistry Laboratory, Medical Biotechnology Laboratory (MedBiotech), Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, Morocco

Email

Pr. Azeddine Ibrahimi - E-mail: a.ibrahimi@um5s.net.ma; *Corresponding author

Article Type

Research Article

Date

Received March 24, 2020; Revised March 30, 2020; Accepted April 1, 2020; Published April 30, 2020

The new SARS-CoV-2 coronavirus is the causative agent of the COVID-19 pandemic outbreak that affected more than 190 countries worldwide with more than 292,000 confirmed cases and over 12,700 deaths. There is at the moment no vaccine or effective treatment for this disease which constitutes a serious global health problem. It is of interest to use a structure based virtual screening approach for the identification of potential inhibitors of the main protease of SARS-CoV-2 (Mpro) from antiviral drugs used to treat other viral disease such as human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infections. The crystallographic structure with PDB ID: 6LU7 of Mpro in complex with the inhibitor N3 was used as model in the virtual screening of 33 protease inhibitors collected from the ChEMBL chemical database using standard molecular docking analysis (AutoDock Vina tool) followed by ranking and selection of compounds based on their binding affinity. We report 10 candidates with optimal binding features to the active site of the protease for further consideration to fight the COVID-19 pandemic and the care for the infected persons.

Keywords

COVID-19, SARS-Cov-2, protease, inhibitors, virtual screening

Citation

Hakmi et al. 16(4): 301-306 (2020)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.