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Research Articles

Identification of polyphenols from Broussonetia papyrifera as SARS CoV-2 main protease inhibitors using in silico docking and molecular dynamics simulation approaches

, ORCID Icon, &
Pages 6747-6760 | Received 30 Jun 2020, Accepted 21 Jul 2020, Published online: 07 Aug 2020
 

Abstract

The current COVID-19 pandemic is caused by SARS CoV-2. To date, ∼463,000 people died worldwide due to this disease. Several attempts have been taken in search of effective drugs to control the spread of SARS CoV-2 infection. The main protease (Mpro) from SARS CoV-2 plays a vital role in viral replication and thus serves as an important drug target. This Mpro shares a high degree of sequence similarity (>96%) with the same protease from SARS CoV-1 and MERS. It was already reported that Broussonetia papyrifera polyphenols efficiently inhibit the catalytic activity of SARS CoV-1 and MERS Mpro. But whether these polyphenols exhibit any inhibitory effect on SARS CoV-2 Mpro is far from clear. To understand this fact, here we have adopted computational approaches. Polyphenols having proper drug-likeness properties and two repurposed drugs (lopinavir and darunavir; having binding affinity −7.3 to −7.4 kcal/mol) were docked against SARS CoV-2 Mpro to study their binding properties. Only six polyphenols (broussochalcone A, papyriflavonol A, 3'-(3-methylbut-2-enyl)-3',4',7-trihydroxyflavane, broussoflavan A, kazinol F and kazinol J) had interaction with both the catalytic residues (His41 and Cys145) of Mpro and exhibited good binding affinity (−7.6 to −8.2 kcal/mol). Molecular dynamic simulations (100 ns) revealed that all Mpro-polyphenol complexes are more stable, conformationally less fluctuated; slightly less compact and marginally expanded than Mpro-darunavir/lopinavir complex. Even the number of intermolecular H-bond and MM-GBSA analysis suggested that these six polyphenols are more potent Mpro inhibitors than the two repurposed drugs (lopinavir and darunavir) and may serve as promising anti-COVID-19 drugs.

Graphical Abstract

Communicated by Ramaswamy H. Sarma

Acknowledgements

RG acknowledges IIT Bhubaneswar for providing fellowship. The authors thank IIT Delhi HPC facility for computational resources.

Disclosure statement

The authors declare that they have no conflicts of interest with the contents of this article.

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