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Phenylethanoid glycosides as a possible COVID-19 protease inhibitor: a virtual screening approach.

Authors :
Bernardi M
Ghaani MR
Bayazeid O
Source :
Journal of molecular modeling [J Mol Model] 2021 Nov 03; Vol. 27 (11), pp. 341. Date of Electronic Publication: 2021 Nov 03.
Publication Year :
2021

Abstract

From the beginning of pandemic, more than 240 million people have been infected with a death rate higher than 2%. Indeed, the current exit strategy involving the spreading of vaccines must be combined with progress in effective treatment development. This scenario is sadly supported by the vaccine's immune activation time and the inequalities in the global immunization schedule. Bringing the crises under control means providing the world population with accessible and impactful new therapeutics. We screened a natural product library that contains a unique collection of 2370 natural products into the binding site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (M <superscript>pro</superscript> ). According to the docking score and to the interaction at the active site, three phenylethanoid glycosides (forsythiaside A, isoacteoside, and verbascoside) were selected. In order to provide better insight into the atomistic interaction and test the impact of the three selected compounds at the binding site, we resorted to a half microsecond-long molecular dynamics simulation. As a result, we are showing that forsythiaside A is the most stable molecule and it is likely to possess the highest inhibitory effect against SARS-CoV-2 M <superscript>pro</superscript> . Phenylethanoid glycosides also have been reported to have both protease and kinase activity. This kinase inhibitory activity is very beneficial in fighting viruses inside the body as kinases are required for viral entry, metabolism, and/or reproduction. The dual activity (kinase/protease) of phenylethanoid glycosides makes them very promising anit-COVID-19 agents.<br /> (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Details

Language :
English
ISSN :
0948-5023
Volume :
27
Issue :
11
Database :
MEDLINE
Journal :
Journal of molecular modeling
Publication Type :
Academic Journal
Accession number :
34731296
Full Text :
https://doi.org/10.1007/s00894-021-04963-2