Screening of potent phytochemical inhibitors against SARS-CoV-2 protease and its two Asian mutants.

Abstract

BACKGROUND:

COVID-19, declared a pandemic in March 2020 by the World Health Organization is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The virus has already killed more than 2.3 million people worldwide.

OBJECT:

The principal intent of this work was to investigate lead compounds by screening natural product library (NPASS) for possible treatment of COVID-19.

METHODS:

Pharmacophore features were used to screen a large database to get a small dataset for structure-based virtual screening of natural product compounds. In the structure-based screening, molecular docking was performed to find a potent inhibitor molecule against the main protease (Mpro) of SARS-CoV-2 (PDB ID: 6Y7M). The predicted lead compound was further subjected to Molecular Dynamics (MD) simulation to check the stability of the leads compound with the evolution of time.

RESULTS:

In pharmacophore-based virtual screening, 2,361 compounds were retained out of 30,927. In the structure-based screening, the lead compounds were filtered based on their docking scores. Among the 2,360 compounds, 12 lead compounds were selected based on their docking score. Kazinol T with NPASS ID: NPC474104 showed the highest docking score of -14.355 and passed criteria of Lipinski's drug-like parameters. Monitoring ADMET properties, Kazinol T showed its safety for consumption. Docking of Kazinol T with two Asian mutants (R60C and I152V) showed variations in binding and energy parameters. Normal mode analysis for ligand-bound and unbound form of protease along with its mutants, revealed displacement and correlation parameters for C-alpha atoms. MD simulation results showed that all ligand-protein complexes remained intact and stable in a dynamic environment with negative Gibbs free energy.

CONCLUSIONS:

The natural product Kazinol T was a predicted lead compound against the main protease of SARS-CoV-2 and will be the possible treatment for COVID-19.

• Authors: Ijaz Muhammad, Noor Rahman, Gul-E-Nayab, Sadaf Niaz, Zarrin Basharat, Luca Rastrelli, Sivaraman Jayanthi, Thomas Efferth, Haroon Khan
• Journal: Computers in biology and medicine (Comput Biol Med) Vol. 133 Pg. 104362 (06 2021) ISSN: 1879-0534 [Electronic] United States
• PMID: 33894500 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Elsevier Ltd. All rights reserved.

Chemical References

• Antiviral Agents
• Phytochemicals
• Protease Inhibitors
• Peptide Hydrolases

Topics

• Antiviral Agents (pharmacology)
• COVID-19
• Humans
• Molecular Docking Simulation
• Peptide Hydrolases
• Phytochemicals
• Protease Inhibitors (pharmacology)
• SARS-CoV-2