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Computers in biology and medicine
12, 2022;151 (Pt A): 106318.

Computational investigation of natural compounds as potential main protease (M) inhibitors for SARS-CoV-2 virus.

Chirag N Patel, Siddhi P Jani, Sivakumar Prasanth Kumar, Krunal M Modi, Yogesh Kumar
Author Information
  1. Chirag N Patel: Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institute of Health, Frederick, MD, 21702, USA; Department of Botany, Bioinformatics, and Climate Change Impacts Management, School of Sciences, Gujarat University, Ahmedabad, 380009, Gujarat, India. Electronic address: chiragpatel269@gmail.com.
  2. Siddhi P Jani: Institute of Science, Nirma University, Ahmedabad, 382481, Gujarat, India.
  3. Sivakumar Prasanth Kumar: Department of Botany, Bioinformatics, and Climate Change Impacts Management, School of Sciences, Gujarat University, Ahmedabad, 380009, Gujarat, India.
  4. Krunal M Modi: Department of Molecular Electrochemistry and Catalysis, J. Heyrovsky Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, Dolejskova 2155/3, 182 23 Prague 8, Czech Republic; Department of Humanities and Science, School of Engineering, Indrashil University, Mehsana, 382740, Gujarat, India. Electronic address: kmodi5033@gmail.com.
  5. Yogesh Kumar: Department of General Visceral and Thoracic Surgery, University Medical Center Hamburg- Eppendorf, Martinistrasse 52, Hamburg, 20246, Germany.
PMID: 36423529 DOI: 10.1016/j.compbiomed.2022.106318

Abstract

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is significantly impacting human lives, overburdening the healthcare system and weakening global economies. Plant-derived natural compounds are being largely tested for their efficacy against COVID-19 targets to combat SARS-CoV-2 infection. The SARS-CoV-2 Main protease (M) is considered an appealing target because of its role in replication in host cells. We curated a set of 7809 natural compounds by combining the collections of five databases viz Dr Duke's Phytochemical and Ethnobotanical database, IMPPAT, PhytoHub, AromaDb and Zinc. We applied a rigorous computational approach to identify lead molecules from our curated compound set using docking, dynamic simulations, the free energy of binding and DFT calculations. Theaflavin and ginkgetin have emerged as better molecules with a similar inhibition profile in both SARS-CoV-2 and Omicron variants.

Keywords

Binding free energy DFT calculation Dynamics simulation Main proteases (M(pro)) Omicron

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MeSH Term

Humans
SARS-CoV-2
Peptide Hydrolases
Pandemics
COVID-19 Drug Treatment

Chemicals

Peptide Hydrolases
Journal Article
Article has an altmetric score of 1

Word Cloud

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