Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease

The novel coronavirus (SARS-CoV-2) has infected over 850,000 people and caused more than 42000 deaths worldwide as of April 1 st , 2020. As the disease is spreading rapidly all over the world, it is urgent to find effective drugs to treat the virus. The main protease (Mpro) of SARS-CoV-2 is one of the potential drug targets. In this work, we used rigorous computational methods, including molecular docking, fast pulling of ligand (FPL), and free energy perturbation (FEP), to investigate potential inhibitors of SARS-CoV-2 Mpro. We first tested our approach with three reported inhibitors of SARS-CoV-2 Mpro; and our computational results are in good agreement with the respective experimental data. Subsequently, we applied our approach on a databases of ~4600 natural compounds found in Vietnamese plants, as well as 8 available HIV-1 protease (PR) inhibitors and an aza-peptide epoxide. Molecular docking resulted in a short list of 35 natural compounds, which was subsequently refined using the FPL scheme. FPL simulations resulted in five potential inhibitors, including 3 natural compounds and two available HIV-1 PR inhibitors. Finally, FEP, the most accurate and precise method, was used to determine the absolute binding free energy of these five compounds. FEP results indicate that two natural compounds, cannabisin A and isoacteoside , and an HIV-1 PR inhibitor, darunavir , exhibit large binding free energy to SARS-CoV-2 Mpro, which is larger than that of 13b , the most reliable SARS-CoV-2 Mpro inhibitor recently reported. The binding free energy largely arises from van der Waals (vdW) interaction. We also found that Glu166 form H-bonds to all the inhibitors. Replacing Glu166 by an alanine residue leads to ~ 2.0 kcal/mol decreases in the affinity of darunavir to SARS-CoV-2 Mpro. Our results could contribute to the development of potentials drugs inhibiting SARS-CoV-2.