Drug repurposing studies targeting SARS-CoV-2: an ensemble docking approach on drug target 3C-like protease (3CL^pro)

Abstract

The COVID-19 pandemic has been responsible for several deaths worldwide. The causative agent behind this disease is the Severe Acute Respiratory Syndrome – novel Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 belongs to the category of RNA viruses. The main protease, responsible for the cleavage of the viral polyprotein is considered as one of the hot targets for treating COVID-19. Earlier reports suggest the use of HIV anti-viral drugs for targeting the main protease of SARS-CoV, which caused SARS in the year 2002–2003. Hence, drug repurposing approach may prove to be useful in targeting the main protease of SARS-CoV-2. The high-resolution crystal structure of the main protease of SARS-CoV-2 (PDB ID: 6LU7) was used as the target. The Food and Drug Administration approved and SWEETLEAD database of drug molecules were screened. The apo form of the main protease was simulated for a cumulative of 150 ns and 10 μs open-source simulation data was used, to obtain conformations for ensemble docking. The representative structures for docking were selected using RMSD-based clustering and Markov State Modeling analysis. This ensemble docking approach for the main protease helped in exploring the conformational variation in the drug-binding site of the main protease leading to the efficient binding of more relevant drug molecules. The drugs obtained as top hits from the ensemble docking possessed anti-bacterial and anti-viral properties. This in silico ensemble docking approach would support the identification of potential candidates for repurposing against COVID-19.

Communicated by Ramaswamy H. Sarma

Keywords:

• COVID-19
• SARS-CoV-2
• main protease
• repurposing
• cryptic pockets
• 3CL^pro

Acknowledgements

The authors would like to acknowledge the PARAM supercomputing facility and the Bioinformatics Resources and Applications Facility (BRAF) at Centre for Development of Advanced Computing (C-DAC), Pune for providing the computing infrastructure. The authors would like to acknowledge SR Rajesh Kumar, Saurabh Patil and Akash Khade for their timely administrative support and services.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors would like to acknowledge the National Supercomputing Mission (NSM), Ministry of Electronics and Information Technology (MeitY), Government of India for funding this work.