Quantitative structure-activity relationships, molecular docking and molecular dynamics simulations reveal drug repurposing candidates as potent SARS-CoV-2 main protease inhibitors

Abstract

The current outbreak of COVID-19 is leading an unprecedented scientific effort focusing on targeting SARS-CoV-2 proteins critical for its viral replication. Herein, we performed high-throughput virtual screening of more than eleven thousand FDA-approved drugs using backpropagation-based artificial neural networks (q²_LOO = 0.60, r² = 0.80 and r²_pred = 0.91), partial-least-square (PLS) regression (q²_LOO = 0.83, r² = 0.62 and r²_pred = 0.70) and sequential minimal optimization (SMO) regression (q²_LOO = 0.70, r² = 0.80 and r²_pred = 0.89). We simulated the stability of Acarbose-derived hexasaccharide, Naratriptan, Peramivir, Dihydrostreptomycin, Enviomycin, Rolitetracycline, Viomycin, Angiotensin II, Angiotensin 1-7, Angiotensinamide, Fenoterol, Zanamivir, Laninamivir and Laninamivir octanoate with 3CL_pro by 100 ns and calculated binding free energy using molecular mechanics combined with Poisson-Boltzmann surface area (MM-PBSA). Our QSAR models and molecular dynamics data suggest that seven repurposed-drug candidates such as Acarbose-derived Hexasaccharide, Angiotensinamide, Dihydrostreptomycin, Enviomycin, Fenoterol, Naratriptan and Viomycin are potential SARS-CoV-2 main protease inhibitors. In addition, our QSAR models and molecular dynamics simulations revealed that His41, Asn142, Cys145, Glu166 and Gln189 are potential pharmacophoric centers for 3CL_pro inhibitors. Glu166 is a potential pharmacophore for drug design and inhibitors that interact with this residue may be critical to avoid dimerization of 3CL_pro. Our results will contribute to future investigations of novel chemical scaffolds and the discovery of novel hits in high-throughput screening as potential anti-SARS-CoV-2 properties.

Communicated by Ramaswamy H. Sarma

Keywords:

• QSAR models
• high-throughput virtual screening
• pK_i prediction
• SARS-CoV-2
• molecular dynamics simulations

Acknowledgements

The authors acknowledge the National Council for Scientific and Technological Development (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES, grant 88887.374931/2019-00, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Finance Code 01), and the São Paulo Research Foundation (FAPESP, grants 2019/00195-2, 2020/04680-0 and 2016/09047-8), Brazil, for financial support.

Disclosure statement

No potential conflict of interest was reported by the authors.