Computer-Aided Analysis of Multiple SARS-CoV-2 Therapeutic Targets: Identification of Potent Molecules from African Medicinal Plants
The COVID-19 pandemic, which started in Wuhan, China, has spread rapidly over the world with no known antiviral therapy or vaccine. Interestingly, traditional Chinese medicine helped in flattening the pandemic curve in China. In this study, molecules from African medicinal plants were analysed as po...
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| Language: | English |
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Wiley
2020-01-01
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| Series: | Scientifica |
| Online Access: | http://dx.doi.org/10.1155/2020/1878410 |
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| author | Franklyn Nonso Iheagwam Solomon Oladapo Rotimi |
| author_facet | Franklyn Nonso Iheagwam Solomon Oladapo Rotimi |
| author_sort | Franklyn Nonso Iheagwam |
| collection | DOAJ |
| description | The COVID-19 pandemic, which started in Wuhan, China, has spread rapidly over the world with no known antiviral therapy or vaccine. Interestingly, traditional Chinese medicine helped in flattening the pandemic curve in China. In this study, molecules from African medicinal plants were analysed as potential candidates against multiple SARS-CoV-2 therapeutic targets. Sixty-five molecules from the ZINC database subset (AfroDb Natural Products) were virtually screened with some reported repurposed therapeutics against six SARS-CoV-2 and two human targets. Molecular docking, druglikeness, absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the best hits were further simulated. Of the 65 compounds, only three, namely, 3-galloylcatechin, proanthocyanidin B1, and luteolin 7-galactoside found in almond (Terminalia catappa), grape (Vitis vinifera), and common verbena (Verbena officinalis), were able to bind to all eight targets better than the reported repurposed drugs. The findings suggest these molecules may play a role as therapeutic leads in tackling this pandemic due to their multitarget activity. |
| format | Article |
| id | doaj-art-d4c3e30958cd414e97d13733748dbe8a |
| institution | Kabale University |
| issn | 2090-908X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
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| series | Scientifica |
| spelling | doaj-art-d4c3e30958cd414e97d13733748dbe8a2025-02-03T01:05:05ZengWileyScientifica2090-908X2020-01-01202010.1155/2020/18784101878410Computer-Aided Analysis of Multiple SARS-CoV-2 Therapeutic Targets: Identification of Potent Molecules from African Medicinal PlantsFranklyn Nonso Iheagwam0Solomon Oladapo Rotimi1Department of Biochemistry, College of Science and Technology, Covenant University, Canaanland, P.M.B. 1023, Ota, Ogun, NigeriaDepartment of Biochemistry, College of Science and Technology, Covenant University, Canaanland, P.M.B. 1023, Ota, Ogun, NigeriaThe COVID-19 pandemic, which started in Wuhan, China, has spread rapidly over the world with no known antiviral therapy or vaccine. Interestingly, traditional Chinese medicine helped in flattening the pandemic curve in China. In this study, molecules from African medicinal plants were analysed as potential candidates against multiple SARS-CoV-2 therapeutic targets. Sixty-five molecules from the ZINC database subset (AfroDb Natural Products) were virtually screened with some reported repurposed therapeutics against six SARS-CoV-2 and two human targets. Molecular docking, druglikeness, absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the best hits were further simulated. Of the 65 compounds, only three, namely, 3-galloylcatechin, proanthocyanidin B1, and luteolin 7-galactoside found in almond (Terminalia catappa), grape (Vitis vinifera), and common verbena (Verbena officinalis), were able to bind to all eight targets better than the reported repurposed drugs. The findings suggest these molecules may play a role as therapeutic leads in tackling this pandemic due to their multitarget activity.http://dx.doi.org/10.1155/2020/1878410 |
| spellingShingle | Franklyn Nonso Iheagwam Solomon Oladapo Rotimi Computer-Aided Analysis of Multiple SARS-CoV-2 Therapeutic Targets: Identification of Potent Molecules from African Medicinal Plants Scientifica |
| title | Computer-Aided Analysis of Multiple SARS-CoV-2 Therapeutic Targets: Identification of Potent Molecules from African Medicinal Plants |
| title_full | Computer-Aided Analysis of Multiple SARS-CoV-2 Therapeutic Targets: Identification of Potent Molecules from African Medicinal Plants |
| title_fullStr | Computer-Aided Analysis of Multiple SARS-CoV-2 Therapeutic Targets: Identification of Potent Molecules from African Medicinal Plants |
| title_full_unstemmed | Computer-Aided Analysis of Multiple SARS-CoV-2 Therapeutic Targets: Identification of Potent Molecules from African Medicinal Plants |
| title_short | Computer-Aided Analysis of Multiple SARS-CoV-2 Therapeutic Targets: Identification of Potent Molecules from African Medicinal Plants |
| title_sort | computer aided analysis of multiple sars cov 2 therapeutic targets identification of potent molecules from african medicinal plants |
| url | http://dx.doi.org/10.1155/2020/1878410 |
| work_keys_str_mv | AT franklynnonsoiheagwam computeraidedanalysisofmultiplesarscov2therapeutictargetsidentificationofpotentmoleculesfromafricanmedicinalplants AT solomonoladaporotimi computeraidedanalysisofmultiplesarscov2therapeutictargetsidentificationofpotentmoleculesfromafricanmedicinalplants |