MACHINE LEARNING, MOLECULAR DOCKING AND MOLECULAR DYNAMICS REVEAL POTENTIAL MAIN PROTEASE INHIBITORS OF SARS-COV-2

We studied how different substrates may be cleaved by the main protease of different SARS-CoV-2 variants of concerns. Furthermore, we conducted 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 squares (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 3CLpro over 100ns, and calculated the binding free energy using molecular mechanics combined with Poisson-Boltzmann surface area (MM-PBSA). Our machine learning models and molecular dynamics data suggest that seven repurposed drug candidates—acarbose-derived hexasaccharide, Angiotensinamide, Dihydrostreptomycin, Enviomycin, Fenoterol, Naratriptan, and Viomycin—are potential SARS-CoV-2 main protease inhibitors. Additionally, our machine learning models and molecular dynamics simulations revealed that His41, Asn142, Cys145, Glu166, and Gln189 are potential pharmacophoric centers for 3CLpro inhibitors. Glu166, in particular, emerges as a potential pharmacophore for drug design, with inhibitors targeting this residue potentially being crucial in preventing 3CLpro dimerization. Our findings will contribute to future investigations into novel chemical scaffolds and the discovery of new hits in high-throughput screening as potential anti-SARS-CoV-2 agents.

This work was supported by 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 State of São Paulo Research Foundation (FAPESP, grants 2019/00195-2, 2020/04680-0, 2016/09047-8, 2022/08730-7, 2021/10577-0, 2023/18211-0), Rede Virus MCTI (FINEP grant 0459/20), Brazil, for financial support.