In silico screening of potential antiviral inhibitors against SARS-CoV-2 main protease

ABSTRACT

Respiratory illness due to SARS-CoV-2 emerged in 2019 and has a significant morbidity and mortality rate. The main protease (Mpro) is mainly responsible for viral replications, which acts as a good drug target to inhibit SARS-CoV-2-related diseases. Chemical compounds obtained from various herbal plants are showing potent antiviral activity against numerous viral diseases. Initial screening was performed with the phytochemicals against Mpro using molecular docking. This result shows that there is a strong interaction exhibited between active sites (His-41 and Cys-145) of Mpro with chemical compounds. In addition, ADME prediction and Lipinski’s rule of five (RO5) calculations demonstrated that the selected compounds have potential drug-like properties. Further, molecular dynamics (MD) simulations were performed to understand the stability and structural changes of protein–ligand complexes for the top five compounds. MM/PBSA studies strongly suggested that compounds, β-spinasterol, and asarinin form stable complexes with Mpro. The most significant hot spot residues such as Thr-25, Met-49, Cys-145, Met-165, and Gln-189 have strongly interacted with the selected chemical compounds. Our calculations suggest that asarinin is the best inhibitor to the Mpro, which supports these candidates and could be potent antiviral agent against SARS-CoV-2.

GRAPHICAL ABSTRACT

KEYWORDS:

• Main protease
• phytochemicals
• ADME
• molecular dynamics
• MM/PBSA

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data are available from the corresponding author upon reasonable request.

Additional information

Funding

K.P., K.R.M. and S.K. thank the SRM Institute of Science and Technology (SRM-IST) Research Fellowship for their research work. M.P. thanks the Department of Science and Technology-Science and Engineering Research Board (DST-SERB), New Delhi, India for the financial support. The authors also thank SRM-IST for providing the supercomputing facility and financial support.