In silico approaches and in vitro assays identify a coumarin derivative as antiviral potential against SARS-CoV-2

Abstract

COVID-19, a disease caused by SARS-CoV-2, was declared a pandemic in 2020 and created a global crisis in health systems, with more than 545 million confirmed cases and 6.33 million deaths. In this sense, this work aims to identify possible inhibitors of the SARS-CoV-2 RdRp enzyme using in silico approaches. RdRp is a crucial enzyme in the replication and assembly cycle of new viral particles and a critical pharmacological target in the treatment of COVID-19. We performed a virtual screening based on molecular docking from our in-house chemical library, which contains a diversity of 313 structures from different chemical classes. Nine compounds were selected since they showed important interactions with the active site from RdRp. Next, the ADME-Tox in silico predictions served as a filter and selected the three most promising compounds: a coumarin LMed-052, a hydantoin LMed-087, and a guanidine LMed-250. Molecular dynamics simulations revealed details such as changes in the positions of ligands and catalytic residues during the simulations compared to the complex from molecular docking studies. Binding free energy analysis was performed using the MMGBSA method, demonstrating that LMed-052 and LMed-087 have better affinities for the RdRp by energetic contributions to the stability of the complexes when compared to LMed-250. Furthermore, LMed-052 showed significant in vitro inhibition against MHV-3, decreasing 99% of viral titers. Finally, these findings are useful to guide structural modifications aiming to improve the potential of these compounds to act as inhibitors of SARS-CoV-2.

Communicated by Ramaswamy H. Sarma

Keywords:

• RNA-dependent RNA polymerase
• RdRp
• COVID-19
• molecular dynamics
• docking
• virtual screening

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that supports the findings of this work are available in the supporting information.

Additional information

Funding

This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) under Grant [number 001, 88887.507109/2020-00, 88881.507319/2020-01], and Centro Nacional de Processamento de Alto Desempenho em São Paulo (CENAPAD-SP).