Abstract
Background: The recent outbreak of Coronavirus SARS-CoV-2 (Covid-19), which has rapidly spread around the world in about three months with tens of thousands of deaths recorded so far is a global concern. An urgent need for potential therapeutic intervention is of necessity. Mpro is an attractive druggable target for the development of anti-COVID-19 drug development.
Methods: Compounds previously characterized by Melissa officinalis were queried against the main protease of coronavirus SARS-CoV-2 using a computational approach.
Results: Melitric acid A and salvanolic acid A had higher affinity than lopinavir and ivermectin using both AutodockVina and XP docking algorithms. The computational approach was employed in the generation of the QSAR model using automated QSAR, and in the docking of ligands from Melissa officinalis with SARS-CoV-2 Mpro inhibitors. The best model obtained was KPLS_Radial_ 28 (R2 = 0.8548 and Q2=0.6474, which was used in predicting the bioactivity of the lead compounds. Molecular mechanics based MM-GBSA confirmed salvanolic acid A as the compound with the highest free energy and predicted bioactivity of 4.777; it interacted with His-41 of the catalytic dyad (Cys145-His41) of SARS-CoV-2 main protease (Mpro), as this may hinder the cutting of inactive viral protein into active ones capable of replication.
Conclusion: Salvanolic acid A can be further evaluated as a potential Mpro inhibitor.
Keywords: Covid-19, serine protease, medicinal plants, natural compounds, pandemic, melissa.
Current Drug Discovery Technologies
Title:Identification of Main Protease of Coronavirus SARS-CoV-2 (Mpro) Inhibitors from Melissa officinalis
Volume: 18 Issue: 5
Author(s): Olusola O. Elekofehinti*, Opeyemi Iwaloye, Courage D. Famusiwa, Olanrewaju Akinseye and Joao B. T. Rocha
Affiliation:
- Department of Biochemistry, Bioinformatics and Molecular Biology Unit, Federal University of Technology Akure, Ondo State, P.M.B 704, Akure,Nigeria
Keywords: Covid-19, serine protease, medicinal plants, natural compounds, pandemic, melissa.
Abstract:
Background: The recent outbreak of Coronavirus SARS-CoV-2 (Covid-19), which has rapidly spread around the world in about three months with tens of thousands of deaths recorded so far is a global concern. An urgent need for potential therapeutic intervention is of necessity. Mpro is an attractive druggable target for the development of anti-COVID-19 drug development.
Methods: Compounds previously characterized by Melissa officinalis were queried against the main protease of coronavirus SARS-CoV-2 using a computational approach.
Results: Melitric acid A and salvanolic acid A had higher affinity than lopinavir and ivermectin using both AutodockVina and XP docking algorithms. The computational approach was employed in the generation of the QSAR model using automated QSAR, and in the docking of ligands from Melissa officinalis with SARS-CoV-2 Mpro inhibitors. The best model obtained was KPLS_Radial_ 28 (R2 = 0.8548 and Q2=0.6474, which was used in predicting the bioactivity of the lead compounds. Molecular mechanics based MM-GBSA confirmed salvanolic acid A as the compound with the highest free energy and predicted bioactivity of 4.777; it interacted with His-41 of the catalytic dyad (Cys145-His41) of SARS-CoV-2 main protease (Mpro), as this may hinder the cutting of inactive viral protein into active ones capable of replication.
Conclusion: Salvanolic acid A can be further evaluated as a potential Mpro inhibitor.
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Cite this article as:
Elekofehinti O. Olusola *, Iwaloye Opeyemi , Famusiwa D. Courage , Akinseye Olanrewaju and Rocha B. T. Joao , Identification of Main Protease of Coronavirus SARS-CoV-2 (Mpro) Inhibitors from Melissa officinalis, Current Drug Discovery Technologies 2021; 18 (5) : e17092020186048 . https://dx.doi.org/10.2174/1570163817999200918103705
DOI https://dx.doi.org/10.2174/1570163817999200918103705 |
Print ISSN 1570-1638 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6220 |
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This thematic issue delves into the cutting-edge computational methodologies revolutionizing drug design. Emphasizing the integration of in silico techniques, this collection highlights advancements in some computational methods, as: molecular docking, molecular dynamics, QSAR (Quantitative Structure-Activity Relationship) and free energy calculations. These approaches enhance the efficiency of drug discovery, reduce costs, ...read more
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