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In search of SARS CoV-2 replication inhibitors: Virtual screening, molecular dynamics simulations and ADMET analysis.

ABSTRACT: Severe acute respiratory syndrome has relapsed recently as novel coronavirus causing a life threat to the entire world in the absence of an effective therapy. To hamper the replication of the deadly SARS CoV-2 inside the host cells, systematic in silico virtual screening of total 267,324 ligands from Asinex EliteSynergy and BioDesign libraries has been performed using AutoDock Vina against RdRp. The molecular modeling studies revealed the identification of twenty-one macrocyclic hits (2-22) with better binding energy than remdesivir (1), marketed SARS CoV-2 inhibitor. Further, the analysis using rules for drug-likeness and their ADMET profile revealed the candidature of these hits due to superior oral bioavailability and druggability. Further, the MD simulation studies of top two hits (2 and 3) performed using GROMACS 2020.1 for 10 ns revealed their stability into the docked complexes. These results provide an important breakthrough in the design of macrocyclic hits as SARS CoV-2 RNA replicase inhibitor.

SUBMITTER: Nagar PR

PROVIDER: S-EPMC8313085 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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In search of SARS CoV-2 replication inhibitors: Virtual screening, molecular dynamics simulations and ADMET analysis.

Nagar Prinsa R PR Gajjar Normi D ND Dhameliya Tejas M TM

Journal of molecular structure 20210726

Severe acute respiratory syndrome has relapsed recently as novel coronavirus causing a life threat to the entire world in the absence of an effective therapy. To hamper the replication of the deadly SARS CoV-2 inside the host cells, systematic <i>in silico</i> virtual screening of total 267,324 ligands from Asinex EliteSynergy and BioDesign libraries has been performed using AutoDock Vina against RdRp. The molecular modeling studies revealed the identification of twenty-one macrocyclic hits (<b> ...[more]

PMID: 34334813

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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.

Dataset Information

In search of SARS CoV-2 replication inhibitors: Virtual screening, molecular dynamics simulations and ADMET analysis.

Publications

In search of SARS CoV-2 replication inhibitors: Virtual screening, molecular dynamics simulations and ADMET analysis.

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