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Research Articles

Antivirals virtual screening to SARS-CoV-2 non-structural proteins

, , &
Pages 8989-9003 | Received 13 Aug 2020, Accepted 19 Apr 2021, Published online: 05 May 2021
 

Abstract

In March 2020, the World Health Organization (WHO) declared coronavirus disease-19 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a pandemic. Since then, the search for a vaccine or drug for COVID-19 treatment has started worldwide. In this regard, a fast approach is the repurposing of drugs, primarily antiviral drugs. Herein, we performed a virtual screening using 22 antiviral drugs retrieved from the DrugBank repository, azithromycin (antibiotic), ivermectin (antinematode), and seven non-structural proteins (Nsps) of SARS-CoV-2, which are considered important targets for drugs, via molecular docking and molecular dynamics simulations. Drug–receptor binding energy was employed as the main descriptor. Based on the results, paritaprevir was predicted as a promising multi-target drug that favorably bound to all tested Nsps, mainly adipose differentiation-related protein (ADRP) (-36.2 kcal mol−1) and coronavirus main proteinase (Mpro) (-32.2 kcal mol−1). Moreover, the results suggest that simeprevir is a strong inhibitor of Mpro (-37.2 kcal mol−1), which is an interesting finding because Mpro plays an important role in viral replication. In addition to drug–receptor affinity, hot spot residues were characterized to facilitate the design of new drug derivatives with improved biological responses.

Graphical Abstract

Acknowledgements

We would like to express our gratitude to the financial support from the Brazilian Agencies CNPq, FAPEMIG and CAPES that provided continuous support to our laboratories. This is a project conducted by members of the Rede Mineira de Química (RQ-MG).

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contribution

Conceived and designed the experiments: VSN, DFSP, LASC, HFDS. Performed the experiments: VSN, HFDS. Analyzed the data: VSN, DFSP, HFDS. Wrote the paper: VSN, DFSP, LASC, HFDS.

Additional information

Funding

Fundação de Amparo à Pesquisa do Estado de Minas Gerais.

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