De novo design of potential SARS-CoV-2 main protease inhibitors using artificial intelligence and molecular modeling technologies
https://doi.org/10.29235/1561-8323-2023-67-3-197-206
Abstract
De novo design of 95 775 potential ligands of SARS-CoV-2 main protease (Mpro), playing an important role in the process of virus replication, was carried out using a deep learning generative neural network that was developed previously based on artificial intelligence technologies. Molecular docking and molecular dynamics methods were used to evaluate the binding affinity of these molecules to the catalytic site of the enzyme. As a result, 7 leading compounds exhibiting Gibbs free energy low values comparable with the values obtained using an identical computational protocol for two potent non-covalent SARS-CoV-2 Mpro inhibitors used in calculations as a positive control were selected. The results obtained indicate the promise of applying identified compounds for development of new antiviral drugs able to inhibit the catalytic activity of SARSCoV-2 Mpro.
About the Authors
A. M. Andrianov
Institute of Bioorganic Chemistry of the National Academy of Sciences of Belarus
Belarus
Belarus
Andrianov Alexander M. – D. Sc. (Chemisrty), Professor, Chief Researcher
5/2, Kuprevich Str., 220084, Minsk, Republic of Belarus
K. V. Furs
United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Furs Konstantin V. – Software Engineer
6, Surganov Str., 220012, Minsk, Republic of Belarus
M. A. Shuldau
United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Shuldau Mikita A. – Software Engineer
6, Surganov Str., 220012, Minsk, Republic of Belarus
A. V. Tuzikov
United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Tuzikov Alexander V. – Corresponding Member, D. Sc. (Physics and Mathematics), Professor, Head of the Laboratory
6, Surganov Str., 220012, Minsk, Republic of Belarus
References
1. Lipinski C. F., Maltarollo V. G., Oliveira P. R., da Silva A. B. F., Honorio K. M. Advances and perspectives in applying deep learning for drug design and discovery. Frontiers in Robotics and AI, 2019, vol. 6, no. 108. https://doi.org/10.3389/frobt.2019.00108
2. Zhavoronkov A., Ivanenkov Y. A., Aliper A., Veselov M. S., Aladinskiy V. A., Aladinskaya A. V., Terentiev V. A. [et al.]. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 2019, vol. 37, no. 9, pp. 1038–1040. https://doi.org/10.1038/s41587-019-0224-x
3. Andrianov A. M., Nikolaev G. I., Shuldov N. A., Bosko I. P., Anischenko A. I., Tuzikov A. V. Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors. Journal of Biomolecular Structure and Dynamics, 2022, vol. 40, no. 16, pp. 7555–7573. https://doi.org/10.1080/07391102.2021.1905559
4. Sahoo R. N., Pattanaik S., Pattnaik G., Mallick S., Mohapatra R. Review on the use of Molecular Docking as the First Line Tool in Drug Discovery and Development. Indian Journal of Pharmaceutical Sciences, 2022, vol. 84, no. 5, pp. 1334–1337. https://doi.org/10.36468/pharmaceutical-sciences.1031
5. Hollingsworth S. A., Dror R. O. Molecular dynamics simulation for all. Neuron, 2018, vol. 99, no. 6, pp. 1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011
6. Shuldau N. A., Yushkevich A. M., Furs K. V., Tuzikov A. V., Andrianov A. M. Development of a deep learning generative neural network for computer-aided design of potential SARS-CoV-2 inhibitors. Mathematical Biology and Bioinformatics, 2022, vol. 17, no. 2, pp. 188–207. https://doi.org/10.17537/2022.17.188
7. Ullrich S., Nitsche C. The SARS-CoV-2 main protease as drug target. Bioorganic & Medicinal Chemistry Letters, 2020, vol. 30, no. 17, art. 127377. https://doi.org/10.1016/j.bmcl.2020.127377
8. Katre S. G., Asnani A. J., Pratyush K., Sakharkar N. G., Bhope A. G., Sawarkar K. T., Nimbekar V. S. Review on development of potential inhibitors of SARS-CoV-2 main protease (MPro). Future Journal of Pharmaceutical Sciences, 2022, vol. 8, no. 1, art. 36. https://doi.org/10.1186/s43094-022-00423-7
9. Palacio-Rodriguez K., Lans I., Cavasotto C. N., Cossio P. Exponential consensus ranking improves the outcome in docking and receptor ensemble docking. Scientific Reports, 2019, vol. 9, no. 1, art. 5142. https://doi.org/10.1038/s41598-019-41594-3
10. Genheden S., Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 2015, vol. 10, no. 5, pp. 449–461. https://doi.org/10.1517/17460441.2015.1032936
11. Zhang C. H., Stone E. A.., Deshmukh M., Ippolito J. A., Ghahremanpour M. M., Tirado-Rives J., Spasov K. A. [et al.]. Potent noncovalent inhibitors of the main protease of SARS-CoV-2 from molecular sculpting of the drug perampanel guided by free energy perturbation calculations. ACS Central Sciences, 2021, vol. 7, no. 3, pp. 467–475. https://doi.org/10.1021/acscentsci.1c00039
12. Lipinski C. A., Lombardo F., Dominy B. W., Feeney P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 1997, vol. 23, no. 1–3, pp. 3–25. https://doi.org/10.1016/s0169-409x(96)00423-1
13. Qamar M. T., Alqahtani S. M., Alamri M. A., Chen L.-L. Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants. Journal of Pharmaceutical Analysis, 2020, vol. 10, no. 4, pp. 313–319. https://doi.org/10.1016/j.jpha.2020.03.009
14. Sharma G., First E. A. Thermodynamic analysis reveals a temperature-dependent change in the catalytic mechanism of Bacillus stearothermophilus tyrosyl-tRNA synthetase. Journal of Biological Chemistry, 2009, vol. 284, no. 7, pp. 4179–4190. https://doi.org/10.1074/jbc.m808500200
15. Shen C., Hu Y., Wang Z., Zhang X., Zhong H., Wang G., Yao X., Xu L., Cao D., Hou T. Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions. Briefings in Bioinformatics, 2021, vol. 22, no. 1, pp. 497–514. https://doi.org/10.1093/bib/bbz173
Review
Views: 334
Email this article 