In-silico Screening of Natural Compound Library against COVID-19 Main Protease

Aims: To perform in-silico screening of the natural phytoconstituents against COVID-19 main protease Background: COVID-19 is an atypical virus that is universal around the globe. WHO (World Health Organization) has also affirmed the emergency public health concerns regarding COVID-19. As COVID-19 spreads globally within a shorter duration, there is a great demand for effective therapy with minimal side effects. Objective: To perform SP and XP docking of the naturally obtained ligand Quercetin (flavonoid) based on HTVS. : To identify the effectiveness of natural phytoconstituents for COVID-19 main protease virus. Methods: In the present study, natural ligands were used for performing the virtual screening, which was obtained from Prestwick Phytochemical Library by applying hierarchal screening. Several docking protocols, such as SP and XP, are used to screen the best natural hit compounds. The three best-hit compounds and hydroxychloroquine were further studied for SAR analysis. Moreover, the ADME study by QikProp and the identification of biological sources were carried out by Duke's Phytochemical and Ethnobotanical Databases. Results: The results suggest that Quercetin showed good binding affinity towards 6LU7 (-56.689 kcal/mol), which could be an effective natural phytoconstituent for COVID-19. Conclusion: Based on HTVS, SP, and XP docking, the naturally obtained ligand Quercetin (a flavonoid) can be used in place of Hydroxychloroquine against COVID-19's main protease virus. This scientific exploration will help to identify the effective natural compound for the COVID-19 main protease virus.

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Publication Details

Journal
Anti-Infective Agents
Published
2024-08-20
DOI
https://doi.org/10.2174/0122113525310221240713022605
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.36
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article

In-silico Screening of Natural Compound Library against COVID-19 Main Protease

Faruk Alam, Josef Yakin, Rakesh Kumar Paul, Rinchi Bora et al.
1 citations
Anti-Infective Agents
Computational Drug Discovery Methods
0.36
article

In-silico Screening of Natural Compound Library against COVID-19 Main Protease

Faruk Alam, Josef Yakin, Rakesh Kumar Paul, Rinchi Bora, Souvik Chattopadhyay, Mrinmoy Nag, Souvik Biswas, Deboprotim Dasgupta
article en
1 citations

Abstract

Aims: To perform in-silico screening of the natural phytoconstituents against COVID-19 main protease Background: COVID-19 is an atypical virus that is universal around the globe. WHO (World Health Organization) has also affirmed the emergency public health concerns regarding COVID-19. As COVID-19 spreads globally within a shorter duration, there is a great demand for effective therapy with minimal side effects. Objective: To perform SP and XP docking of the naturally obtained ligand Quercetin (flavonoid) based on HTVS. : To identify the effectiveness of natural phytoconstituents for COVID-19 main protease virus. Methods: In the present study, natural ligands were used for performing the virtual screening, which was obtained from Prestwick Phytochemical Library by applying hierarchal screening. Several docking protocols, such as SP and XP, are used to screen the best natural hit compounds. The three best-hit compounds and hydroxychloroquine were further studied for SAR analysis. Moreover, the ADME study by QikProp and the identification of biological sources were carried out by Duke's Phytochemical and Ethnobotanical Databases. Results: The results suggest that Quercetin showed good binding affinity towards 6LU7 (-56.689 kcal/mol), which could be an effective natural phytoconstituent for COVID-19. Conclusion: Based on HTVS, SP, and XP docking, the naturally obtained ligand Quercetin (a flavonoid) can be used in place of Hydroxychloroquine against COVID-19's main protease virus. This scientific exploration will help to identify the effective natural compound for the COVID-19 main protease virus.

Anti-Infective AgentsVol. 23(2)
Bharat Electronics (India) (IN), British Horseracing Authority (GB), Assam Down Town University (IN), Central University of Rajasthan (IN)
Good health and well-being
Openalex Percentile: Top 37%
Computational Drug Discovery Methods
0.36
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