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Virtual Screening and Molecular Design of Potential SARS-COV-2 Inhibitors

ABSTRACT: According to recent studies, the main Mpro protease of the SARS-CoV-2 virus, which is the most important target in the development of promising drugs for the treatment of COVID-19, is evolutionarily conservative and has not undergone significant changes compared with the main Mpro protease of the SARS-CoV virus. Many researchers note the similarity between the binding sites of the main Mpro protease of SARS-CoV and SARS-CoV-2 viruses; thus, with the spreading epidemic, further studies on inhibitors of the main Mpro protease of the SARS-CoV virus to fight COVID-19 seems logical. In the course of the study, satisfactory QSAR models are built using simplex, fractal, and HYBOT descriptors; the Partial Least Squares (PLS), Random Forest (RF), Support Vectors, Gradient Boosting (GBM) methods; and the OCHEM Internet platform (https://ochem.eu), in which different types of molecular descriptors and machine learning methods are implemented. The structural interpretation, which allowed us to identify molecular fragments that increase and decrease the activity of SARS-CoV inhibitors, is performed for the obtained models. The results of the structural interpretation are used for the rational molecular design of potential SARS-CoV-2 inhibitors. The resulting QSAR models are used for the virtual screening of 2087 FDA-approved drugs.

SUBMITTER: Tinkov O

PROVIDER: S-EPMC8207500 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

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Virtual Screening and Molecular Design of Potential SARS-COV-2 Inhibitors

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