Repurposing simeprevir, calpain inhibitor IV and a cathepsin F inhibitor against SARS-CoV-2 and insights into their interactions with M^pro

Abstract

The world has come to a sudden halt due to the incessant spread of a viral pneumonia dubbed COVID-19 caused by the beta-coronavirus, SARS-CoV-2. The main protease of SARS-CoV-2 plays a key role in the replication and propagation of the virus in the host cells. Inhibiting the protease blocks the replication of the virus; therefore it is considered as an attractive therapeutic target. Here we describe the screening of the DrugBank database, a public repository for small molecule therapeutics, to identify approved or experimental phase drugs that can be repurposed against the main protease of SARS-CoV-2. The initial screening was performed on more than 13,000 drug entries in the target database using an energy optimised pharmacophore hypothesis AARRR. A sub-set of the molecules selected based on the fitness score was further screened using molecular docking by sequentially filtering the molecules through the high throughput virtual screening, extra precision and standard precision docking modalities. The best hits were subjected to binding free energy estimation using the MM-GBSA method. Approved drugs viz, Cobicistat, Larotrectinib and Simeprevir were identified as potential candidates for repurposing. Drugs in the discovery phase identified as inhibitors include the known cysteine protease inhibitors, Calpain inhibitor IV and an experimental cathepsin F inhibitor. In order to analyse the stability of the binding interactions, the known cysteine protease inhibitors viz, Simeprevir, calpain inhibitor IV and the cathepsin F inhibitor in complex M^pro were subjected to molecular dynamics simulations at 100 ns. Based on the results Simeprevir was found to be a strong inhibitor of SARS-CoV-2 M^pro.

Communicated by Ramaswamy H. Sarma

Keywords:

• Covid-19
• SARS-CoV-2
• main protease
• E-pharmacophore
• molecular docking
• simeprevir
• protease inhibitors

Acknowledgements

The authors gratefully acknowledge the help received from Dr. Dileep Vijayan, Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Japan in performing MD simulations and related analysis, and for other valuable scientific insights.

Disclosure statement

The authors declare no conflicts of interest.

Additional information

Funding

Abhithaj J and Arun KG acknowledge the Indian Council for Medical Research (ICMR), New Delhi, India for the financial support. Sharanya CS acknowledge CSIR-SRF for the financial support. The authors would like to thank the Bioinformatics Infrastructure facility (BIF) at the Department of Biotechnology & Microbiology, Kannur University, supported by Department of Biotechnology (DBT), Government of India for computational facilities.