Background: SARS-CoV-2, an emerging strain of coronavirus, has affected millions of people from all the continents of world and received worldwide attention. This emerging health crisis calls for the urgent development of specific therapeutics against COVID-19 to potentially reduce the burden of this emerging pandemic. Purpose: This study aims to evaluate the anti-viral efficacy of natural bioactive entities against COVID-19 via molecular docking and molecular dynamics simulation. Methods: A library of 27 caffeic-acid derivatives was screened against 5 proteins of SARS-CoV-2 by using Molegro Virtual Docker 7 to obtain the binding energies and interactions between compounds and SARS-CoV-2 proteins. ADME properties and toxicity profiles were investigated via www.swissadme.ch web tools and Toxtree respectively. Molecular dynamics simulation was performed to determine the stability of the lead-protein interactions. Results: Our obtained results has uncovered khainaoside C, 6-O-Caffeoylarbutin, khainaoside B, khainaoside C and vitexfolin A as potent modulators of COVID-19 possessing more binding energies than nelfinavir against COVID-19 Mpro, Nsp15, SARS-CoV-2 spike S2 subunit, spike open state and closed state structure respectively. While Calceolarioside B was identified as pan inhibitor, showing strong molecular interactions with all proteins except SARS-CoV-2 spike glycoprotein closed state. The results are supported by 20 ns molecular dynamics simulations of the best complexes. Conclusion: This study will hopefully pave a way for development of phytonutrients-based antiviral therapeutic for treatment or prevention of COVID-19 and further studies are recommended to evaluate the antiviral effects of these phytochemicals against SARS-CoV-2 in in vitro and in vivo models.
|Original language||English (US)|
|State||Published - Apr 13 2021|
Bibliographical noteKAUST Repository Item: Exported on 2022-06-14
Acknowledgements: Shaheen supercomputer of King Abdullah University of Science and Technology (KAUST) is used to perform the MDS study (under the project number k1482). We thank Dr. Bahaa Mostafa for using his computational power unit during the analysis of the MDS data.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.