In silico prediction of SARS-CoV-2 main protease and polymerase inhibitors: 3D-Pharmacophore modelling

Abstract

The outbreak of the second severe acute respiratory syndrome coronavirus (SARS-CoV-2) known as COVID-19 has caused global concern. No effective vaccine or treatment to control the virus has been approved yet. Social distancing and precautionary protocols are still the only way to prevent person-to-person transmission. We hope to identify anti-COVID-19 activity of the existing drugs to overcome this pandemic as soon as possible. The present study used HEX and AutoDock Vina softwares to predict the affinity of about 100 medicinal structures toward the active site of 3-chymotrypsin-like protease (3Clpro) and RNA-dependent RNA polymerase (RdRp), separately. Afterwards, MOE software and the pharmacophore-derived query methodology were employed to determine the pharmacophore model of their inhibitors. Tegobuvir (19) and compound 45 showed the best binding affinity toward RdRp and 3Clpro of SARS-CoV-2 in silico, respectively. Tegobuvir -previously applied for hepatitis C virus- formed highly stable complex with uncommon binding pocket of RdRp (E total: −707.91 Kcal/mol) in silico. In addition to compound 45, tipranavir (28) and atazanavir (26) as FDA-approved HIV protease inhibitors were tightly interacted with the active site of SARS-CoV-2 main protease as well. Based on pharmacophore modelling, a good structural pattern for potent candidates against SARS-CoV-2 main enzymes is suggested. Re-tasking or taking inspiration from the structures of tegobuvir and tipranavir can be a proper approach toward coping with the COVID-19 in the shortest possible time and at the lowest cost.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• protease 3Clpro
• RdRp
• docking study
• pharmacophore modelling

Disclosure statement

The authors declare no conflict of interest, financial or otherwise.

Authors' contributions

The concept and experimental research plan was designed by Hajiagha Bozorgi A and Mosayebnia M. The valuable collaboration of Prof. Farzad Kobarfard helped us to elaborate on our research ideas. The project administration and data analysis were done by Rezaeianpour R and Hajiagha Bozorgi A. The corresponding author is Dr. Mona Mosayebnia who has written, reviewed and edited this manuscript. All the authors approve the final manuscript for publication.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This study was funded by Shahid Beheshti University of Medical Sciences (SBMU, 99-24012), Tehran, Iran.