Identification of new inhibitors of the kinase acti­vity of CDK2 and CDK9 by molecular modeling and high-efficiency screening

G.V. Andrianov*, I.G. Serebriiskii**

Kazan Federal University, Kazan, 420008 Russia

Fox Chase Cancer Center, Philadelphia, 19111 USA

E-mail: *grigorii.andrianov@gmail.com, **ilya.serebriiskii@fccc.edu

Received September 30, 2021


ORIGINAL ARTICLE

Full text PDF

DOI: 10.26907/2542-064X.2021.4.543-556

For citation: Andrianov G.V., Serebriiskii I.G. Identification of new inhibitors of the kinase activity of CDK2 and CDK9 by molecular modeling and high-efficiency screening. Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki, 2021, vol. 163, no. 4, pp. 543–556. doi: 10.26907/2542-064X.2021.4.543-556. (In Russian)

Abstract

Kinases are important components of many signaling pathways in a cell, and therefore they are involved in the regulation of such diverse processes as trans­cription, cell cycle progression, apoptosis, cell differentiation, metabolism, and intercellular communication. Their increased activity often results in the development of various oncological, neurological, and cardiovascular diseases, as well as of immune system disorders. Treatment is most frequently based on an antagonistic approach, when a small molecule compound inhibits excessive kinase activity. However, the ATP-binding site of kinases is highly conserved, which often causes these ATP-competitive inhibitors to cross-react with a variety of other kinases, resulting in low selectivity. Thus, the discovery and development of new safe and effective inhibitors is a challenging task because it requires a comprehensive study of their effects on the human kinome. The experimental validation of possible candidates is an extremely expensive procedure, and narrowing down selection of candidate targets in silico became an attractive approach in early drug discovery. Various approaches to virtual screening were developed, with the goal to increase the cost-effectiveness of drug development manyfold by reducing the need for validation experiments. In this work, we applied a combination of different computational approaches to select 22 candidate inhibitors of the kinase activity of CDK2 and CDK9 and then tested them experimentally. Establishing a pipeline of virtual screening and subsequent validation made it possible to reveal the advantages and limitations of the methods used. About 1/3 of candidates were predicted correctly, including one compound with IC50 < 2 μM for both kinases.

Keywords: virtual screening, structural alignment, energy minimization of ligand-target complex, protein kinase, CDK2, CDK9

Acknowledgements. We are grateful to J. Karanicolas, Professor at Fox Chase Cancer Center, for making this research possible and to N.I. Akberova, Associate Professor of the Department of Biochemistry and Biotechnology at Kazan Federal University, PhD in Biology, for her critical reflections on the manus­cript and valuable advice.

The study was supported by the Kazan Federal University Strategic Academic Leadership Program.

Figure Captions

Fig. 1. a) structure of the catalytic domain of CDK9 (PDB: 3BLQ) and a focused view of the binding mode of ATP and the inhibitor in the ATP-binding pocket; b) non-selective action of the kinase inhibitor dasatinib (initially developed as an ABL kinase inhibitor) on the human kinome. Dasatinib-responsive kinases are shown as dark red dots.

Fig. 2. Virtual screening strategy for the identification of new kinase inhibitors: a) 2D library of compounds in text format; b) generation of 3D conformers for each compound in the library; c) 3D alignment of the conformers relative to the position of known kinase inhibitors; d) minimization of the energy of interaction of the aligned conformers in complex with the catalytic domain of kinases.

Fig. 3. Quantitative characteristics of aminothiazole-containing compounds in the Enamine libraries: a) structural similarity between the compounds studied and known kinase inhibitors; b) distribution of templates used for alignment, the dotted line shows 50% of compounds in the library; c) energy of interaction of the inhibitors with CDK2 and CDK9.

Fig. 4. In vitro validation of the selected compounds. Compounds marked with an asterisk * showed no inhibitory effects. 

References

  1. Hanks S.K. Genomic analysis of the eukaryotic protein kinase superfamily: A perspective. Genome Biol., 2003, vol. 4, no. 5, art. 111, pp. 1–7. doi: 10.1186/gb-2003-4-5-111.
  2. Manning G., Whyte D.B., Martinez R., Hunter T., Sudarsanam S. The protein kinase complement of the human genome. Science, 2002, vol. 298, no. 5600, pp. 1912–1934. doi: 10.1126/science.1075762.
  3. Davis R.J. Trans­criptional regulation by MAP kinases. Mol. Reprod. Dev., 1995, vol. 42, no. 4, pp. 459–467. doi: 10.1002/mrd.1080420414.
  4. Rauch J., Volinsky N., Romano D., Kolch W. The secret life of kinases: Functions beyond catalysis. Cell Commun. Signaling, 2011, vol. 9, no. 1, art. 23, pp. 1–28. doi: 10.1186/1478-811X-9-23.
  5. Lu Z., Hunter T. Metabolic kinases moonlighting as protein kinases. Trends Biochem. Sci., 2018, vol. 43, no. 4, pp. 301–310. doi: 10.1016/j.tibs.2018.01.006.
  6. Bhullar K.S., Lagarón N.O., McGowan E.M., Parmar I., Jha A., Hubbard B.P., Rupasinghe H. Kinase-targeted cancer therapies: Progress, challenges and future directions. Mol. Cancer, 2018, vol. 17, no. 1, art. 48, pp. 1–20. doi: 10.1186/s12943-018-0804-2.
  7. Lahiry P., Torkamani A., Schork N.J., Hegele R.A. Kinase mutations in  human  disease:  Interpreting genotype–phenotype relationships. Nat. Rev. Genet., 2010, vol. 11, no. 1, pp. 60–74. doi: 10.1038/nrg2707.
  8. Zaman A., Wu W., Bivona T.G. Targeting oncogenic BRAF: Past, present, and future. Cancers, 2019, vol. 11, no. 8, art. 1197, pp. 1–19. doi: 10.3390/cancers11081197.
  9. Chavda J., Bhatt H. Systemic review on B-RafV600E mutation as potential therapeutic target for the treatment of cancer. Eur. J. Med. Chem., 2020, vol. 206, art. 112675, pp. 1–30. doi: 10.1016/j.ejmech.2020.112675.
  10. Zhao K., Zhou X., Ding M. Molecular insight into mutation-induced conformational change in metastasic bowel cancer BRAF kinase domain and its implications for selective inhibitor design. J. Mol. Graphics Modell., 2018, vol. 79, pp. 59–64. doi: 10.1016/j.jmgm.2017.11.005.
  11. Wilson L.J., Linley A., Hammond D.E., Hood F.E., Coulson J.M., MacEwan D.J., Ross S.J., Slupsky J.R., Smith P.D., Eyers P.A., Prior I.A. New Perspectives, Opportunities, and challenges in exploring the human protein kinome. Cancer Res., 2018, vol. 78, no. 1, pp. 15–29. doi: 10.1158/0008-5472.CAN-17-2291.
  12. Roskoski R. Jr. Properties of FDA-approved small molecule protein kinase inhibitors: A 2021 update. Pharmacol. Res., 2021, vol. 165, art. 105463, pp. 1–21. doi: 10.1016/j.phrs.2021.105463.
  13. Gani O.A., Thakkar B., Narayanan D., Alam K.A., Kyomuhendo P., Rothweiler U., Tello-Franco V., Engh R.A. Assessing protein kinase target similarity: Comparing sequence, structure, and cheminformatics approaches. Biochim. Biophys. Acta, 2015, vol. 1854, no. 10, pt. B, pp. 1605–1616. doi: 10.1016/j.bbapap.2015.05.004.
  14. Modi V., Dunbrack R.L. Defining a new nomenclature for the structures of active and inactive kinases. Proc. Natl. Acad. Sci. U. S. A., 2019, vol. 116, no. 14. pp. 6818–6827. doi: 10.1073/pnas.1814279116.
  15. Gagic Z, Ruzic D., Djokovic N., Djikic T., Nikolic. K. In silico methods for design of kinase inhibitors as anticancer drugs. Front. Chem., 2019, vol. 7, art. 873, pp. 1–25. doi: 10.3389/fchem.2019.00873.
  16. Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., Bourne P.E. The Protein Data Bank. Nucleic Acids Res., 2000, vol. 28, no. 1, pp. 235–242. doi: 10.1093/nar/28.1.235.
  17. Hawkins P.C.D., Nicholls A. Conformer generation with OMEGA: learning from the data set and the analysis of failures. J. Chem. Inf. Model., 2012, vol. 52, no. 11, pp. 2919–2936. doi: 10.1021/ci300314k.
  18. Hawkins P.C.D., Skillman A.G., Nicholls A. Comparison of shape-matching and docking as virtual screening tools. J. Med. Chem., 2007, vol. 50, no. 1, pp. 74–82. doi: 10.1021/jm0603365.
  19. Alford R.F., Leaver-Fay A., Jeliazkov J.R., O'Meara M.J., DiMaio F.P., Park H., Shapova­lov M.V., Renfrew P.D., Mulligan V.K., Kappel K., Labonte J.W., Pacella M.S., Bonneau R., Bradley Ph., Dunbrack R.L., Das Rh., Baker D., Kuhlman B., Kortemme T., Gray J.J. The Rosetta all-atom energy function for macromolecular modeling and design. J. Chem. Theory Comput., 2017, vol. 13, no. 6, pp. 3031–3048. doi: 10.1021/acs.jctc.7b00125.
  20. Andersen C.B., Wan Y., Chang J.W., Riggs B., Lee C., Liu Y, Sessa F., Villa F., Kwiatkowski N., Suzuki M., Nallan L., Heald R., Musacchio A., Gray N.S. Discovery of selective aminothiazole aurora kinase inhibitors. ACS Chem. Biol., 2008, vol. 3, no. 3, pp. 180–192. doi: 10.1021/cb700200w.
  21. Zhang H., Pandey S., Travers M., Sun H., Morton G., Madzo J., Chung W., Khowsathit J., Perez-Leal O., Barrero C.A., Merali C., Okamoto Y., Sato T., Pan J., Garriga J., Bhanu N.V., Simithy J., Patel B., Huang J., Raynal N.J.-M., Garcia B.A., Jacobson M.A., Kadoch C., Merali S., Zhang Y., Childers W., Abou-Gharbia M., Karanicolas J., Baylin S.B., Zahnow C.A., Jelinek J., Graña X.,  Issa J.-P.J. Targeting CDK9 reactivates epigenetically silenced genes in cancer. Cell, 2018, vol. 175, no. 5, pp. 1244–1258. doi: 10.1016/j.cell.2018.09.051.
  22. Das J., Chen P., Norris D., Padmanabha R., Lin J., Moquin R.V., Shen Zh., Cook L.S., Doweyko A.M., Pitt S., Pang S., Shen D.R., Fang Q., de Fex H.F., McIntyre K.W., Shuster D.J., Gillooly K.M., Behnia K., Schieven G.L., Wityak J., Barrish J.C. 2-aminothiazole as a novel kinase inhibitor template. Structure-activity relationship studies toward the discovery of N-(2-chloro-6-methylphenyl)-2-[[6-[4-(2-hydroxyethyl)-1- piperazinyl)]-2-methyl-4-pyrimidinyl]amino)]-1,3-thiazole-5-carboxamide (dasatinib, BMS-354825) as a potent pan-Src kinase inhibitor. J. Med. Chem., 2006, vol. 49, no. 23, pp. 6819–6832. doi: 10.1021/jm060727j.
  23. Doman T.N., McGovern S.L., Witherbee B.J., Kasten T.P., Kurumbail R.,  Stallings W.C.,  Connolly D.T., Shoichet B.K. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J. Med. Chem., 2002, vol. 45, no. 11, pp. 2213–2221. doi: 10.1021/jm010548w.
  24. Andrianov G.V., Ong W.J.G., Serebriiskii I., Karanicolas J. Efficient hit-to-lead searching of kinase inhibitor chemical space via computational fragment merging. J. Chem. Inf. Model., 2021, vol. 61, no. 12, pp. 5967–5987. doi: 10.1021/acs.jcim.1c00630.


The content is available under the license Creative Commons Attribution 4.0 License.