In Silico Screening of Some Active Phytochemicals to Identify Promising Inhibitors Against SARS-CoV-2 Targets
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
BACKGROUND: There are very few small-molecule drug candidates developed against SARS-CoV-2 that have been revealed since the epidemic began in November 2019. The typical medicinal chemistry discovery approach requires more than a decade of the year of painstaking research and development and a significant financial guarantee, which is not feasible in the challenge of the current epidemic.
OBJECTIVE: This current study proposes to find and identify the most effective and promising phytomolecules against SARS-CoV-2 in six essential proteins (3CL protease, Main protease, Papain- Like protease, N-protein RNA binding domain, RNA-dependent RNA polymerase, and Spike receptor binding domain target through in silico screening of 63 phytomolecules from six different Ayurveda medicinal plants.
METHODS: The phytomolecules and SARS-CoV-2 proteins were taken from public domain databases such as PubChem and RCSB Protein Data Bank. For in silico screening, the molecular interactions, binding energy, and ADMET properties were investigated.
RESULTS: The structure-based molecular docking reveals some molecules' greater affinity towards the target than the co-crystal ligand. Our results show that tannic acid, cyanidin-3-rutinoside, zeaxanthin, and carbolactone are phytomolecules capable of inhibiting SARS-CoV-2 target proteins in the least energy conformations. Tannic acid had the least binding energy of -8.8 kcal/mol, which is better than the binding energy of its corresponding co-crystal ligand (-7.5 kcal/mol) against 3 CL protease. Also, it has shown the least binding energy of -9.9 kcal/mol with a more significant number of conventional hydrogen bond interactions against the RdRp target. Cyanidin-3-rutinoside showed binding energy values of -8.8 and -7.6 kcal/mol against Main protease and Papain-like protease, respectively. Zeaxanthin was the top candidate in the N protein RBD with a binding score of - 8.4 kcal/mol, which is slightly better when compared to a co-crystal ligand (-8.2 kcal/mol). In the spike, carbolactone was the suitable candidate with the binding energy of -7.2 kcal/mol and formed a conventional hydrogen bond and two hydrophobic interactions. The best binding affinity-scored phytomolecules were selected for the MD simulations studies.
CONCLUSION: The present in silico screening study suggested that active phytomolecules from medicinal plants could inhibit SARS-CoV-2 targets. The elite docked compounds with drug-like properties have a harmless ADMET profile, which may help to develop promising COVID-19 inhibitors.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2023 |
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Erschienen: |
2023 |
Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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Enthalten in: |
Current drug discovery technologies - (2023) vom: 09. Okt. |
Sprache: |
Englisch |
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Beteiligte Personen: |
Alagarsamy, V [VerfasserIn] |
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Links: |
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Themen: |
ADMET. |
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Anmerkungen: |
Date Revised 20.10.2023 published: Print-Electronic Citation Status Publisher |
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doi: |
10.2174/0115701638243222230920051050 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM363534121 |
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520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
520 | |a BACKGROUND: There are very few small-molecule drug candidates developed against SARS-CoV-2 that have been revealed since the epidemic began in November 2019. The typical medicinal chemistry discovery approach requires more than a decade of the year of painstaking research and development and a significant financial guarantee, which is not feasible in the challenge of the current epidemic | ||
520 | |a OBJECTIVE: This current study proposes to find and identify the most effective and promising phytomolecules against SARS-CoV-2 in six essential proteins (3CL protease, Main protease, Papain- Like protease, N-protein RNA binding domain, RNA-dependent RNA polymerase, and Spike receptor binding domain target through in silico screening of 63 phytomolecules from six different Ayurveda medicinal plants | ||
520 | |a METHODS: The phytomolecules and SARS-CoV-2 proteins were taken from public domain databases such as PubChem and RCSB Protein Data Bank. For in silico screening, the molecular interactions, binding energy, and ADMET properties were investigated | ||
520 | |a RESULTS: The structure-based molecular docking reveals some molecules' greater affinity towards the target than the co-crystal ligand. Our results show that tannic acid, cyanidin-3-rutinoside, zeaxanthin, and carbolactone are phytomolecules capable of inhibiting SARS-CoV-2 target proteins in the least energy conformations. Tannic acid had the least binding energy of -8.8 kcal/mol, which is better than the binding energy of its corresponding co-crystal ligand (-7.5 kcal/mol) against 3 CL protease. Also, it has shown the least binding energy of -9.9 kcal/mol with a more significant number of conventional hydrogen bond interactions against the RdRp target. Cyanidin-3-rutinoside showed binding energy values of -8.8 and -7.6 kcal/mol against Main protease and Papain-like protease, respectively. Zeaxanthin was the top candidate in the N protein RBD with a binding score of - 8.4 kcal/mol, which is slightly better when compared to a co-crystal ligand (-8.2 kcal/mol). In the spike, carbolactone was the suitable candidate with the binding energy of -7.2 kcal/mol and formed a conventional hydrogen bond and two hydrophobic interactions. The best binding affinity-scored phytomolecules were selected for the MD simulations studies | ||
520 | |a CONCLUSION: The present in silico screening study suggested that active phytomolecules from medicinal plants could inhibit SARS-CoV-2 targets. The elite docked compounds with drug-like properties have a harmless ADMET profile, which may help to develop promising COVID-19 inhibitors | ||
650 | 4 | |a Journal Article | |
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650 | 4 | |a MD simulation | |
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650 | 4 | |a medicinal plants | |
650 | 4 | |a molecular docking | |
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700 | 1 | |a Murugesan, S |e verfasserin |4 aut | |
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