Dataset Information

Virtual screening based on the structure of more than 10⁵ compounds against four key proteins of SARS-CoV-2: M_Pro, S_RBD, RdRp, and PL_pro.

ABSTRACT:

Background

SARS-CoV-2 initially originated in Wuhan (China) around December 2019, and spread all over the world. Currently, WHO (Word Health Organization) has licensed several vaccines for this viral infection. However, not everyone can be vaccinated. People with underlying health conditions that weaken their immune systems or those with severe allergies to some vaccine components, may not be able to be vaccinated. Moreover, no vaccination is 100% safe, and the emergence of new SARS-CoV-2 mutations may reduce the efficacy of immunizations. Therefore, it is urgent to develop effective drugs to protect people against this virus.

Material and method

We performed structure-based virtual screening (SBVS) of a library that was built from ChemDiv and PubChem databases against four SARS-CoV-2 target proteins: S-protein (spike), main protease (M_Pro), RNA-dependent RNA polymerase, and PL_pro. A virtual screening study was performed using PyRx and AutoDock tools.

Results

Our results suggest that twenty-five top-ranked drugs with the highest energy binding as the potential inhibitors against four SARS-CoV-2 targets, relative to the reference molecules. Based on the energy binding, we suggest that these compounds could be used to produce effective anti-viral drugs against SARS-CoV-2.

Conclusion

The discovery of novel compounds for COVID-19 using computer-aided drug discovery tools requires knowledge of the structure of coronavirus and various target proteins of the virus. These compounds should be further assessed in experimental assays and clinical trials to validate their actual activity against the disease. These findings may contribute to the drug design studies against COVID-19.

SUBMITTER: Nabati F

PROVIDER: S-EPMC9652154 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Virtual screening based on the structure of more than 10<sup>5</sup> compounds against four key proteins of SARS-CoV-2: M<sub>Pro</sub>, S<sub>RBD</sub>, RdRp, and PL<sub>pro</sub>.

Nabati Farzan F Kamyabiamineh Ayshin A Kosari Ramin R Ghasemi Faezeh F Seyedebrahimi Seyedehmasoumeh S Mohammadi Sobhan S Moradi Mohammad M

Informatics in medicine unlocked 20221112

<h4>Background</h4>SARS-CoV-2 initially originated in Wuhan (China) around December 2019, and spread all over the world. Currently, WHO (Word Health Organization) has licensed several vaccines for this viral infection. However, not everyone can be vaccinated. People with underlying health conditions that weaken their immune systems or those with severe allergies to some vaccine components, may not be able to be vaccinated. Moreover, no vaccination is 100% safe, and the emergence of new SARS-CoV- ...[more]

PMID: 36406927

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Project description:Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12-20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7-45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle.

Dataset Information

Virtual screening based on the structure of more than 10⁵ compounds against four key proteins of SARS-CoV-2: M_Pro, S_RBD, RdRp, and PL_pro.

Background

Material and method

Results

Conclusion

Publications

Virtual screening based on the structure of more than 10<sup>5</sup> compounds against four key proteins of SARS-CoV-2: M<sub>Pro</sub>, S<sub>RBD</sub>, RdRp, and PL<sub>pro</sub>.

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OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Virtual screening based on the structure of more than 105 compounds against four key proteins of SARS-CoV-2: MPro, SRBD, RdRp, and PLpro.

Background

Material and method

Results

Conclusion

Publications

Virtual screening based on the structure of more than 10<sup>5</sup> compounds against four key proteins of SARS-CoV-2: M<sub>Pro</sub>, S<sub>RBD</sub>, RdRp, and PL<sub>pro</sub>.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Virtual screening based on the structure of more than 10⁵ compounds against four key proteins of SARS-CoV-2: M_Pro, S_RBD, RdRp, and PL_pro.