Discovery of genistein derivatives as potential SARS-CoV-2 main protease inhibitors by virtual screening, molecular dynamics simulations and ADMET analysis

Abstract

Background: Due to the constant mutation of virus and the lack of specific therapeutic drugs, the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still poses a huge threat to the health of people, especially those with underlying diseases. Therefore, drug discovery against the SARS-CoV-2 remains of great significance. Methods: With the main protease of virus as the inhibitor target, 9,614 genistein derivatives were virtually screened by LeDock and AutoDock Vina, and the top 20 compounds with highest normalized scores were obtained. Molecular dynamics simulations were carried out for studying interactions between these 20 compounds and the target protein. The drug-like properties, activity, and ADMET of these compounds were also evaluated by DruLiTo software or online server. Results: Twenty compounds, including compound 11, were screened by normalized molecular docking, which could bind to the target through multiple non-bonding interactions. Molecular dynamics simulation results showed that compounds 2, 4, 5, 11, 13, 14, 17, and 18 had the best binding force with the target protein of SARS-CoV-2, and the absolute values of binding free energies all exceeded 50 kJ/mol. The drug-likeness properties indicated that a variety of compounds including compound 11 were worthy of further study. The results of bioactivity score prediction found that compounds 11 and 12 had high inhibitory activities against protease, which indicated that these two compounds had the potential to be further developed as COVID-19 inhibitors. Finally, compound 11 showed excellent predictive ADMET properties including high absorption and low toxicity. Conclusion: These in silico work results show that the preferred compound 11 (ZINC000111282222), which exhibited strong binding to SARS-CoV-2 main protease, acceptable drug-like properties, protease inhibitory activity and ADMET properties, has great promise for further research as a potential therapeutic agent against COVID-19.

Keywords: ADMET; SARS-CoV-2; genistein; molecular docking; molecular dynamics simulation.

FIGURE 1

Structure of daidzein, genistein, and glycitein.

FIGURE 2

Schematic diagram of the discovery of genistein-based anti-SARS-CoV-2 drug molecules.

FIGURE 3

The superimposition between the N3 in the crystal structure (magenta) and the N3 from docked conformation (cyan) by AutoDock Vina (A) or by LeDock (B).

FIGURE 4

In LeDock and AutoDock Vina, the number of ligands with the same score.

FIGURE 5

Structure of the top 20 compounds screened by normalized assessment.

FIGURE 6

2D presentations of interactions of the top 20 genistein derivatives and N3 with SARS-CoV-2 main protease, respectively.

FIGURE 7

RMSD of backbone C-alpha atoms of COVID-19 M^pro relative to the starting complexes at different ligand systems or wild M^pro during 200 ns MD run.

FIGURE 8

The SASA plots of COVID-19 M^pro in a MD simulation system containing different ligands or wild M^pro.

FIGURE 9

The changes in the number of H-bonds during the MD trajectory of different M^pro-ligand system or wild M^pro.

FIGURE 10

RMSF per residue of backbone C-alpha atoms of COVID-19 M^pro relative to the starting complexes at different ligand systems or wild M^pro during 200 ns MD run.

FIGURE 11

Binding free energy calculation between the COVID-19 M^pro and the top 20 compounds screened by normalized assessment.