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Computational studies of potential antiviral compounds from some selected Nigerian medicinal plants against SARS-CoV-2 proteins

Authors :
Raymond C. Ibeh
Gavin C. Ikechukwu
Chinonyerem J. Ukweni
Israel C. Omekara
Amanda U. Ezirim
Favour N. Ujowundu
Ebere O. Eziefuna
Callistus I. Iheme
Sunday O. Oyedemi
Hezekiel M. Kumalo
Umar Ndagi
Monsurat M. Lawal
Source :
Informatics in Medicine Unlocked, Vol 38, Iss , Pp 101230- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

The challenges posed by COVID-19's emergence have led to a search for its therapies. There is no cure for COVID-19 infection yet, but there is significant progress in vaccine formulation for prophylaxis and drug development (such as Paxlovid) for high-risk patients. As a contribution to the ongoing quest for solutions, this study shows potent phytocompounds identification as inhibitors of SARS-CoV-2 targets using in silico methods. We used virtual screening, molecular docking, and molecular dynamics (MD) simulations to investigate the interaction of some phytochemicals with 3CLpro, ACE2, and PLpro proteins crucial to the SARS-CoV-2 viral cycle. The predicted docking scores range from −5.5 to −9.4 kcal/mol, denoting appreciable binding of these compounds to the SARS-CoV-2 proteins and presenting a multitarget inhibition for COVID-19. Some phytocompounds interact favorably at non-active sites of the enzymes. For instance, MD simulation shows that an identified site on PLpro is stable and likely an allosteric region for inhibitor binding and modulation. These phytocompounds could be developed into effective therapy against COVID-19 and probed as potential multitarget-directed ligands and drug candidates against the SARS-CoV-2 virus. The study unveils drug repurposing, selectivity, allosteric site targeting, and multitarget-directed ligand in one piece. These concepts are three distinct approaches in the drug design and discovery pipeline.

Details

Language :
English
ISSN :
23529148
Volume :
38
Issue :
101230-
Database :
Directory of Open Access Journals
Journal :
Informatics in Medicine Unlocked
Publication Type :
Academic Journal
Accession number :
edsdoj.82ea947d32344080aa09be91261a092b
Document Type :
article
Full Text :
https://doi.org/10.1016/j.imu.2023.101230