Advertisement

Molecular Diversity

, Volume 22, Issue 2, pp 359–381 | Cite as

Application of two-dimensional binary fingerprinting methods for the design of selective Tankyrase I inhibitors

  • B. S. Muddukrishna
  • Vasudev Pai
  • Richard Lobo
  • Aravinda Pai
Original Article
  • 405 Downloads

Abstract

In the present study, five important binary fingerprinting techniques were used to model novel flavones for the selective inhibition of Tankyrase I. From the fingerprints used: the fingerprint atom pairs resulted in a statistically significant 2D QSAR model using a kernel-based partial least square regression method. This model indicates that the presence of electron-donating groups positively contributes to activity, whereas the presence of electron withdrawing groups negatively contributes to activity. This model could be used to develop more potent as well as selective analogues for the inhibition of Tankyrase I.

Graphical Abstract

Schematic representation of 2D QSAR work flow

Keywords

Tankyrase Binary fingerprints Descriptors 2D QSAR 

Notes

Acknowledgements

We sincerely acknowledge the support of Mr. Mikal Rekdal, Department of Chemical Engineering, Norwegian University of Science and technology, Norway. Authors acknowledge Manipal University for providing necessary facilities. Authors acknowledge Schrödinger Inc. USA for the software and technical support.

Supplementary material

11030_2017_9793_MOESM1_ESM.rar (885 kb)
Supplementary material 1 (rar 884 KB)

References

  1. 1.
    Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM (2010) Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 127:2893–2917.  https://doi.org/10.1002/ijc.25516 CrossRefPubMedGoogle Scholar
  2. 2.
    Morin PJ, Sparks AB, Korinek V, Barker N, Clevers H, Vogelstein B, Kinzler KW (1997) Activation of \(\beta \)-catenin-TCF signaling in colon cancer by mutations in \(\beta \)-catenin or APC. Science 275:1787–1790.  https://doi.org/10.1126/science.275.5307.1787 CrossRefPubMedGoogle Scholar
  3. 3.
    De Sousa EMF, Vermeulen L, Richel D, Medema JP (2011) Targeting WNT signaling in colon cancer stem cells. Clin Cancer Res 17:647–653.  https://doi.org/10.1158/1078-0432.CCR-10-1204 CrossRefPubMedGoogle Scholar
  4. 4.
    Ikeda S, Kishida M, Matsuura Y, Usui H, Kikuchi A (2000) GSK-3 [beta]-dependent phosphorylation of adenomatous polyposis coli gene product can be modulated by [beta]-catenin and protein phosphatase 2A complexed with Axin. Oncogene 19:537.  https://doi.org/10.1038/sj.onc.1203359 CrossRefPubMedGoogle Scholar
  5. 5.
    Huang SMA, Mishina YM, Liu S, Cheung A, Stegmeier F, Michaud GA, Hild M (2009) Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling. Nature 461:614–620.  https://doi.org/10.1038/nature08356 CrossRefPubMedGoogle Scholar
  6. 6.
    Donigian JR, de Lange T (2007) The role of the poly (ADP-ribose) polymerase tankyrase1 in telomere length control by the TRF1 component of the shelterin complex. J Biol Chem 282:22662–22667.  https://doi.org/10.1074/jbc.M702620200 CrossRefPubMedGoogle Scholar
  7. 7.
    Narwal M, Koivunen J, Haikarainen T, Obaji E, Legala OE, Venkannagari H, Lehtiö L (2013) Discovery of tankyrase inhibiting flavones with increased potency and isoenzyme selectivity. J Med Chem 56:7880–7889.  https://doi.org/10.1021/jm201510p CrossRefPubMedGoogle Scholar
  8. 8.
    Schrödinger Release 2017-2: Maestro, Schrödinger, LLC, New York, NY (2017)Google Scholar
  9. 9.
    Okouchi S, Saegusa H (1989) Prediction of soil sorption coefficients of hydrophobic organic pollutants by adsorbability index. Bull Chem Soc Jpn 62:922–924.  https://doi.org/10.1246/bcsj.62.922 CrossRefGoogle Scholar
  10. 10.
    Dancoff SM, Quastler H (1953) The information content and error rate of living things. In: Essays on the use of information theory in biology. University of Illinois Press, Urbana, p 263Google Scholar
  11. 11.
    Bertz SH (1981) The first general index of molecular complexity. J Am Chem Soc 103:3599–3601.  https://doi.org/10.1021/ja00402a071 CrossRefGoogle Scholar
  12. 12.
    Ośmiałowski K, Halkiewicz J, Kaliszan R (1986) Quantum chemical parameters in correlation analysis of gas–liquid chromatographic retention indices of amines. J Chromatogr A 361:63–69CrossRefGoogle Scholar
  13. 13.
    Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 496:1027–1040.  https://doi.org/10.1021/cr950202r CrossRefGoogle Scholar
  14. 14.
    Stanton DT, Jurs PC (1990) Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure–property relationship studies. Anal Chem 62:2323–2329.  https://doi.org/10.1021/ac00220a013 CrossRefGoogle Scholar
  15. 15.
    Raychaudhury C, Ray SK, Ghosh JJ, Roy AB, Basak SC (1984) Discrimination of isomeric structures using information theoretic topological indices. J Comput Chem 5:581–588.  https://doi.org/10.1002/jcc.540050612 CrossRefGoogle Scholar
  16. 16.
    Chenzhong C, Zhiliang L (1998) Molecular polarizability: a relationship to water solubility of alkanes and alcohols. J Chem Inf Comput Sci 38:1–7.  https://doi.org/10.1021/ci9601729 CrossRefGoogle Scholar
  17. 17.
    Mulliken RS (1955) Electronic population analysis on LCAO–MO molecular wave functions. J Chem Phys 23:1833–1840.  https://doi.org/10.1063/1.1740588 CrossRefGoogle Scholar
  18. 18.
    Knox JH, Kaliszan R (1985) Theory of solvent disturbance peaks and experimental determination of thermodynamic dead-volume in column liquid chromatography. J Chromatogr A 349:211–234CrossRefGoogle Scholar
  19. 19.
    Luco JM, Yamin LJ, Ferretti HF (1995) Molecular topology and quantum chemical descriptors in the study of reversed-phase liquid chromatography:hydrogen-bonding behavior of chalcones and flavonones. J Pharm Sci 84:903–908.  https://doi.org/10.1002/jps.2600840722 CrossRefPubMedGoogle Scholar
  20. 20.
    Hammette LP (1970) Physical organic chemistry: reaction rates, equilibria and mechanism. Mc Graw Hill, New YorkGoogle Scholar
  21. 21.
    Joshi RK, Meister T, Scapozza L, Ha TK (1994) A new quantum chemical approach in QSAR-analysis: parametrisation of conformational energies into molecular descriptors JMn (steric) and JSn (electronic). Arzneimittelforschung 44:779–790PubMedGoogle Scholar
  22. 22.
    Beckhaus HD (1978) \(\cal{S}_f\) parameters: a measure of the front strain of alkyl groups. Angew Chem Int Ed Engl 17:593–594.  https://doi.org/10.1002/cber.19781110107 CrossRefGoogle Scholar
  23. 23.
    Ivanciuc O, Balaban AT (1996) Design of topological indices: a new topological parameter for the steric effect of alkyl substituents. Croat Chem Acta 69:75–83.  https://doi.org/10.1021/ci034266b CrossRefGoogle Scholar
  24. 24.
    Dash SC, Behera GB (1980) A new steric parameter to explain ortho-substituent effect. Indian J Chem Sect A 19:541–543Google Scholar
  25. 25.
    Miyaki Y, Einaga Y, Fujita H (1978) Excluded-volume effects in dilute polymer solutions: very high molecular weight polystyrene in benzene and cyclohexane. Macromolecules 11:1180–1186.  https://doi.org/10.1021/ma60066a022 CrossRefGoogle Scholar
  26. 26.
    Carbó R, Leyda L, Arnau M (1980) How similar is a molecule to another? An electron density measure of similarity between two molecular structures. Int J Quantum Chem 17:1185–1189CrossRefGoogle Scholar
  27. 27.
    Parr RG, Pearson RG (1983) Absolute hardness: companion parameter to absolute electronegativity. J Am Chem Soc 105:7512–7516.  https://doi.org/10.1021/ja00364a005 CrossRefGoogle Scholar
  28. 28.
    Kier LB (1989) An index of molecular flexibility from kappa shape attributes. Mol Inf 8:221–224.  https://doi.org/10.1021/acs.jcim.6b00565 CrossRefGoogle Scholar
  29. 29.
    Sastry M, Lowrie JF, Dixon SL, Sherman W (2010) Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J Chem Inf Model 50:771–784.  https://doi.org/10.1021/ci100062n CrossRefPubMedGoogle Scholar
  30. 30.
    Duan J, Dixon SL, Lowrie JF, Sherman W (2010) Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods. J Mol Graph 29:157–170.  https://doi.org/10.1016/j.jmgm.2010.05.008 CrossRefGoogle Scholar
  31. 31.
    Schrödinger Release 2016-3: Canvas, Schrödinger, LLC, New York, NY (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  • B. S. Muddukrishna
    • 1
  • Vasudev Pai
    • 2
  • Richard Lobo
    • 2
  • Aravinda Pai
    • 3
  1. 1.Department of Pharmaceutical Quality Assurance, Manipal College of Pharmaceutical Sciences (MCOPS)Manipal UniversityManipalIndia
  2. 2.Department of Pharmacognosy, Manipal College of Pharmaceutical Sciences (MCOPS)Manipal UniversityManipalIndia
  3. 3.Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences (MCOPS)Manipal UniversityManipalIndia

Personalised recommendations