Docking and quantitative structure–activity relationship studies for imidazo[1,2-a]pyrazines as inhibitors of checkpoint kinase-1

Abstract

We have performed docking of imidazo[1,2-a]pyrazines complexed with checkpoint kinase1 (Chk1) to better understand the structural requirements and preferred conformations of these inhibitors. The study was performed on a selected set of 33 compounds with variation in structure and activity. In addition, the predicted inhibitor concentrations (IC50) of the imidazo[1,2-a]pyrazines as Chk1 inhibitors were obtained by comparative molecular similarity analysis (CoMSIA). The best CoMSIA model included electrostatic and hydrophobic fields, had a good Q 2 value of 0.589, and adequately predicted the compounds contained in the test set. Furthermore, plots of the CoMSIA fields allowed conclusions to be drawn for the selection of suitable inhibitors.

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References

  1. Abagyan R, Totrov M (1994) Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J Mol Biol 235:983–1002. doi:10.1006/jmbi.1994.1052

    PubMed  Article  CAS  Google Scholar 

  2. Abagyan R, Totrov M, Kuznetsov D (1994) ICM—a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J Comput Chem 15:488–506. doi:10.1002/jcc.540150503

    Article  CAS  Google Scholar 

  3. Alzate-Morales JH, Caballero J, Vergara-Jaque A, González-Nilo FD (2009) Insights into the structural basis of N2 and O6 substituted guanine derivatives as cyclin-dependent kinase 2 (CDK2) inhibitors: prediction of the binding modes and potency of the inhibitors by docking and ONIOM calculations. J Chem Inf Model 49:886–899. doi:10.1021/ci8004034

    PubMed  Article  CAS  Google Scholar 

  4. Alzate-Morales JH, Vergara-Jaque A, Caballero J (2010) Computational study on the interaction of N1 substituted pyrazole derivatives with B-Raf kinase: an unusual water wire hydrogen-bond network and novel interactions at the entrance of the active site. J Chem Inf Model 50:1101–1112. doi:10.1021/ci100049h

    PubMed  Article  CAS  Google Scholar 

  5. An J, Totrov M, Abagyan R (2005) Pocketome via comprehensive identification and classification of ligand binding envelopes. Mol Cell Proteomics 4:752–761. doi:10.1074/mcp.M400159-MCP200

    PubMed  Article  CAS  Google Scholar 

  6. Bush BL, Nachbar RB (1993) Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. J Comput-Aided Mol Des 7:587–619. doi:10.1007/BF00124364

    PubMed  Article  CAS  Google Scholar 

  7. Caballero J, Fernández M, González-Nilo FD (2008) A CoMSIA study on the adenosine kinase inhibition of pyrrolo[2,3-d]pyrimidine nucleoside analogues. Bioorg Med Chem 16:5103–5108. doi:10.1016/j.bmc.2008.03.027

    PubMed  Article  CAS  Google Scholar 

  8. Caballero J, Vergara-Jaque A, Fernández M, Coll D (2009) Docking and quantitative structure–activity relationship studies for sulfonyl hydrazides as inhibitors of cytosolic human branched-chain amino acid aminotransferase. Mol Divers 13:493–500. doi:10.1007/s11030-009-9140-1

    PubMed  Article  CAS  Google Scholar 

  9. Garriga M, Caballero J (2011) Insights into the structure of urea-like compounds as inhibitors of the juvenile hormone epoxide hydrolase (JHEH) of the tobacco hornworm Manduca sexta: analysis of the binding modes and structure–activity relationships of the inhibitors by docking and CoMFA calculations. Chemosphere 82:1604–1613. doi:10.1016/j.chemosphere.2010.11.048

    PubMed  Article  CAS  Google Scholar 

  10. Halgren TA (1996) Merck molecular force field. V. Extension of MMFF94 using experimental data, additional computational data, and empirical rules. J Comput Chem 17:616–641. doi:10.1002/(SICI)1096-987X(199604)17:5/6<616:AID-JCC5>3.0.CO;2-X

    Article  CAS  Google Scholar 

  11. Hartwell L, Kastan M (1994) Cell cycle control and cancer. Science 266:1821–1828. doi:10.1126/science.7997877

    PubMed  Article  CAS  Google Scholar 

  12. Janetka JW, Ashwell S, Zabludoff S, Lyne P (2007) Inhibitors of checkpoint kinases: from discovery to the clinic. Curr Opin Drug Dicov Develop 10:473–486

    CAS  Google Scholar 

  13. Kubinyi H (1993) Applications of Hansch analysis. In: Mannhold R, Krokgsgaard-Larsen P, Timmerman H (eds) QSAR: Hansch analysis and related approaches. Wiley-VCH, Weinheim

    Google Scholar 

  14. Lagos CF, Caballero J, Gonzalez-Nilo FD, Pessoa-Mahana CD, Perez-Acle T (2008) Docking and quantitative structure–activity relationship studies for the bisphenylbenzimidazole family of non-nucleoside inhibitors of HIV-1 reverse transcriptase. Chem Biol Drug Des 72:360–369. doi:10.1111/j.1747-0285.2008.00716.x

    PubMed  Article  CAS  Google Scholar 

  15. Matthews TP, Klair S, Burns S, Boxall K, Cherry M, Fisher M, Westwood IM, Walton MI, McHardy T, Cheung K-MJ, Van Montfort R, Williams D, Aherne GW, Garrett MD, Reader J, Collins I (2009) Identification of inhibitors of checkpoint kinase 1 through template screening. J Med Chem 52:4810–4819. doi:10.1021/jm900314j

    PubMed  Article  CAS  Google Scholar 

  16. Matthews TP, McHardy T, Klair S, Boxall K, Fisher M, Cherry M, Allen CE, Addison GJ, Ellard J, Aherne GW, Westwood IM, van Montfort R, Garrett MD, Reader JC, Collins I (2010) Design and evaluation of 3, 6-di(hetero)aryl imidazo[1, 2-a]pyrazines as inhibitors of checkpoint and other kinases. Bioorg Med Chem Lett 20:4045–4049. doi:10.1016/j.bmcl.2010.05.096

    PubMed  Article  CAS  Google Scholar 

  17. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092. doi:10.1063/1.1699114

    Article  CAS  Google Scholar 

  18. Nemethy G, Gibson KD, Palmer KA, Yoon CN, Paterlini G, Zagari A, Rumsey S, Scheraga HA (1992) Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides. J Phys Chem 96:6472–6484. doi:10.1021/j100194a068

    Article  CAS  Google Scholar 

  19. Verma RP, Hansch C (2005) An approach toward the problem of outliers in QSAR. Bioorg Med Chem 13:4597–4621. doi:10.1016/j.bmc.2005.05.002

    PubMed  Article  CAS  Google Scholar 

  20. Zhou B-BS, Bartek J (2004) Targeting the checkpoint kinases: chemosensitization versus chemoprotection. Nat Rev Cancer 4:216–225. doi:10.1038/nrc1296

    PubMed  Article  CAS  Google Scholar 

  21. Zhou B-BS, Elledge SJ (2000) The DNA damage response: putting checkpoints in perspective. Nature 408:433–439. doi:10.1038/35044005

    PubMed  Article  CAS  Google Scholar 

  22. Zhou B-B, Anderson HJ, Roberge M (2003) Targeting DNA checkpoint kinases in cancer therapy. Cancer Biol Ther 2(Suppl 1):S16–S22

    PubMed  CAS  Google Scholar 

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Acknowledgements

Julio Caballero thanks “Becas Universidad de Talca” for financial support through doctoral fellowship. Part of this work has been supported by Fondecyt, Grant 11090431, Proyecto interno DI-13-10/R, Universidad Andres Bello.

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Correspondence to Julio Caballero.

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Caballero, J., Zilocchi, S., Tiznado, W. et al. Docking and quantitative structure–activity relationship studies for imidazo[1,2-a]pyrazines as inhibitors of checkpoint kinase-1. Med Chem Res 21, 1912–1920 (2012). https://doi.org/10.1007/s00044-011-9714-1

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Keywords

  • Checkpoint kinase-1 inhibitors
  • Molecular docking
  • Quantitative structure–activity relationships
  • CoMSIA