Targeting two potential sites of SARS-CoV-2 main protease through computational drug repurposing

Abstract

Before the rise of SARS-CoV-2, emergence of different coronaviruses such as SARS-CoV and MERS-CoV has been reported that indicates possibility of the future novel pathogen from the coronavirus family at a pandemic level. In this context, explicit studies on identifying inhibitors focused on the coronavirus life cycle, are immensely important. The main protease is critical for the life cycle of coronaviruses. Majority of the work done on the inhibitor studies on the catalytically active dimeric SARS-CoV-2 main protease (M^pro), primarily focussed on the catalytic site of a single protomer, with a few targeting the dimeric site. In this study, we have exploited the FDA-approved drugs, for a computational drug repurposing study against the M^pro. A virtual screening approach was employed with docking and molecular dynamics (MD) methods. Out of 1576, FDA-approved compounds, our study suggests three compounds: netupitant, paliperidone and vilazodone as possible inhibitors with a potential to inhibit both sites (monomeric and dimeric) of the M^pro. These compounds were found to be stable during the MD simulations and their post simulation binding energies were also correlated for both the targeted sites, suggesting equal binding capacity. This unique efficiency of the reported compounds might support further experimental studies on developing inhibitors against SARS-CoV-2 main protease.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• docking
• molecular dynamics simulation
• drugs

Disclosure statement

The authors of this manuscript declare no conflict of interest.

Additional information

Funding

The infrastructure facilities of IIT (BHU) Varanasi and DST funded IDAPT Hub Foundation, IIT BHU [DST/NMICPS/TIH11/IIT(BHU)2020/02] are sincerely acknowledged. The support and resources provided by PARAM Shivay facility under the National Supercomputing Mission, Government of India at the Indian Institute of Technology, Varanasi are gratefully acknowledged. Project grant support through SERB funding [CVD/2020/000031] to VKD is acknowledged.