Interdisciplinary Bio Central

Korean Society for Bioinformatics (한국생명정보학회)
권호 표지
DOI QR코드

DOI QR Code

Genetic Function Approximation and Bayesian Models for the Discovery of Future HDAC8 Inhibitors

  • Thangapandian, Sundarapandian (Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University) ;
  • John, Shalini (Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University) ;
  • Lee, Keun-Woo (Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University)
  • Received : 2011.11.25
  • Accepted : 2011.11.28
  • Published : 2011.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Background: Histone deacetylase (HDAC) 8 is one of its family members catalyzes the removal of acetyl groups from N-terminal lysine residues of histone proteins thereby restricts transcription factors from being expressed. Inhibition of HDAC8 has become an emerging and effective anti-cancer therapy for various cancers. Application computational methodologies may result in identifying the key components that can be used in developing future potent HDAC8 inhibitors. Results: Facilitating the discovery of novel and potential chemical scaffolds as starting points in the future HDAC8 inhibitor design, quantitative structure-activity relationship models were generated with 30 training set compounds using genetic function approximation (GFA) and Bayesian algorithms. Six GFA models were selected based on the significant statistical parameters calculated during model development. A Bayesian model using fingerprints was developed with a receiver operating characteristic curve cross-validation value of 0.902. An external test set of 54 diverse compounds was used in validating the models. Conclusions: Finally two out of six models based on their predictive ability over the test set compounds were selected as final GFA models. The Bayesian model has displayed a high classifying ability with the same test set compounds and the positively and negatively contributing molecular fingerprints were also unveiled by the model. The effectively contributing physicochemical properties and molecular fingerprints from a set of known HDAC8 inhibitors were identified and can be used in designing future HDAC8 inhibitors.

Keywords

References

  1. Daniel, P.D., Samuel, G.G., Carol, A.F., and David, W.C. (2010). Structures of metal-substituted human histone deacetylase 8 provide mech anistic inferences on biological function. Biochemistry 348, 5048-5056.
  2. Stephanie, L.G., Samuel, G.G., and Carol, A.F. (2006). Catalytic activity and inhibition of human histone deacetylase 8 is dependent on the identity of the active site metal ion. Biochemistry 45, 6170-6178. https://doi.org/10.1021/bi060212u
  3. Thangapandian S., John, S., Sakkiah, S., and Lee, K.W. (2010). Ligand and structure based pharmacophore modeling to facilitate novel histone deacetylase 8 inhibitor design. Eur J Med Chem 45, 4409-4417. https://doi.org/10.1016/j.ejmech.2010.06.024
  4. Dokmanovic, M., and Marks, P.A. (2005). Prospects: Histone deacetylase inhibitors. J Cell Biochem 96, 293-304. https://doi.org/10.1002/jcb.20532
  5. Hahnen, E., Hauke, J., Tränkle, C., Eyupoglu, I.Y., Wirth, B., and Blümcke, I. (2008). Histone deacetylase inhibitors: Possible implications for neurodegenerative disorders. Expert Opin Investig Drugs 17, 169-184. https://doi.org/10.1517/13543784.17.2.169
  6. Morrison, B.E., Majdzadeh, N., and D'Mello, S.R. (2007). Histone deacetylases: Focus on the nervous system. Cell Mol Life Sci 64, 2258-2269. https://doi.org/10.1007/s00018-007-7035-9
  7. Brichta, L., Hofmann, Y., Hahnen, E., Siebzehnrubl, F.A., Raschke, H., Blumcke, I., Eyupoglu, I.Y., and Wirth, B. (2003). Valproic acid increases the SMN2 protein level: A well-known drug as a potential therapy for spinal muscular atrophy. Hum Mol Genet 12, 2481-2489. https://doi.org/10.1093/hmg/ddg256
  8. Hockly, E., Richon, V.M., Woodman, B., Smith, D.L., Zhou, X., Rosa, E., Sathasivam, K., Ghazi-Noori, S., Mahal, A., Lowden, P.A., et al. (2003). Suberoylanilide hydroxamic acid, a histone deacetylase inhibitor, ameliorates motor deficits in a mouse model of Huntington's disease. Proc Natl Acad Sci USA 100, 2041-2046. https://doi.org/10.1073/pnas.0437870100
  9. Adcock, I.M. (2007). HDAC inhibitors as anti-inflammatory agents. Br J Pharmacol 150, 829-831. https://doi.org/10.1038/sj.bjp.0707166
  10. Mottet, D., and Castronova, V. (2008). Histone deacetylases: Target enzymes for cancer therapy. Clin Exp Metastasis 25, 183-189. https://doi.org/10.1007/s10585-007-9131-5
  11. Pan, L., Lu, J., and Huang, B. (2007). HDAC inhibitors: A potential new category of anti-tumor agents. Cell Mol Immunol 4, 337-343.
  12. Paris, M., Porcelloni, M., Binaschi, M., and Fattori, D. (2008). Histone deacetylase inhibitors: From bench to clinic. J Med Chem 51, 1505-1529. https://doi.org/10.1021/jm7011408
  13. Benson, L.J., Phillips, J.A., Gu, Y., Parthun, M.R., Hoffman, C.S., and Annunziato, A.T. (2007). Properties of the type B histone acetyltransferase Hat1: H4 tail interaction, site preference, and involvement in DNA repair. J Biol Chem 282, 836-842. https://doi.org/10.1074/jbc.M607464200
  14. Thangapandian, S., John, S., Sakkiah, S., and Lee, K.W. (2010). Dockingenabled pharmacophore model for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery. J Mol Graph Model 29, 382-395. https://doi.org/10.1016/j.jmgm.2010.07.007
  15. Mai, A., Massa, S., Ragno, R., Esposito, M., Sbardella, G., Nocca, G., Scatena, R., Jesacher, F., Loidl, P., and Brosch, G. (2002). Binding mode analysis of 3-(4-benzoyl-1-methyl-1H-2-pyrrolyl)-N-hydroxy-2-propenamide: A new synthetic histone deacetylase inhibitor inducing histone hyperacetylation, growth inhibition, and terminal cell differentiation. J Med Chem 45, 1778-1784. https://doi.org/10.1021/jm011088+
  16. Vadivelan, S., Sinha, B.N., Rambabu, G., Boppana, K., and Jagarlapudi, S.A. (2008). Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads. J Mol Graph Model 26, 935-946. https://doi.org/10.1016/j.jmgm.2007.07.002
  17. Chen, Y., Jiang, Y., Zhou, J., Yu, Q., and You, Q. (2008). Identification of ligand features essential for HDACs inhibitors by pharmacophore modeling. J Mol Graph Model 26, 1160-1168. https://doi.org/10.1016/j.jmgm.2007.10.007
  18. Chen, Y., Li, H., Tang, W., Zhu, C., Jiang, Y., Zou, J., Yu, Q., and You, Q. (2009). 3D-QSAR studies of HDACs inhibitors using pharmacophorebased alignment. Eur J Med Chem 44, 1-9. https://doi.org/10.1016/j.ejmech.2008.03.002
  19. Kramer, O.H., Gottlicher, M., and Heinzel, T. (2001). Histone deacetylase as a therapeutic target. Trends Endocrinol Metab 12, 294-300. https://doi.org/10.1016/S1043-2760(01)00438-6
  20. Lee, S.C., Bottaro, A., and Insel, R.A. (2003). Activation of terminal B cell differentiation by inhibition of histone deacetylation. Mol Immunol 39, 923-932. https://doi.org/10.1016/S0161-5890(03)00029-4
  21. Kapustin, G.V., Fejer, G., Gronlund, J.L., McCafferty, D.G., Seto, E., and Etzkorn, F.A. (2003). Phosphorus-based SAHA analogues as histone deacetylase inhibitors. Org Lett 5, 3053-3056. https://doi.org/10.1021/ol035056n
  22. Gregoretti, I., Lee, Y.M., and Goodson, H.V. (2004). Molecular evolution of the histone deacetylase family: Functional implications of phylogenetic analysis. J Mol Biol 338, 17-31. https://doi.org/10.1016/j.jmb.2004.02.006
  23. Fischle, W., Kiermer, V., Dequiedt, F., and Verdin, E. (2001). The emerging role of class II histone deacetylases. Biochem Cell Biol 79, 337-348. https://doi.org/10.1139/o01-116
  24. Kozikowski, A.P., Chen, Y., Gaysin, A., Chen, B., D'Annibale, M.A., Suto, C.M., and Langley, B.C. (2007). Functional differences in epigenetic modulators-superiority of mercaptoacetamide-based histone deacetylase inhibitors relative to hydroxamates in cortical neuron neuroprotection studies. J Med Chem 50, 3054-3061. https://doi.org/10.1021/jm070178x
  25. Marks, P.A., and Breslow, R. (2007). Dimethyl sulfoxide to vorinostat: Development of this histone deacetylase inhibitor as an anticancer drug. Nat Biotechnol 25, 84-90. https://doi.org/10.1038/nbt1272
  26. Xu, W.S., Parmigiani, R.B., and Marks, P.A. (2007). Histone deacetylase inhibitors: Molecular mechanisms of action. Oncogene 26, 5541-5552. https://doi.org/10.1038/sj.onc.1210620
  27. Dokmanovic, M., Clarke, C., and Marks, P.A. (2007). Histone deacetylase inhibitors: Overview and perspectives. Mol Cancer Res 5, 981-989. https://doi.org/10.1158/1541-7786.MCR-07-0324
  28. Butler, K.V., and Kozikowski, A.P. (2008). Chemical origins of isoform selectivity in histone deacetylase inhibitors. Curr Pharm Des 14, 505- 528. https://doi.org/10.2174/138161208783885353
  29. Estiu, G., Greenberg, E., Harrison, C.B., Kwiatkowski, N.P., Mazitschek, R., Bradner, J.E., and Wiest, O. (2008). Structural origin of selectivity in class II-selective histone deacetylase inhibitors. J Med Chem 51, 2898- 2906. https://doi.org/10.1021/jm7015254
  30. Khan, N., Jeffers, M., Kumar, S., Hackett, C., Boldog, F., Khramtsov, N., Qian, X., Mills, E., Berghs, S.C., Carey, N., et al. (2008). Determination of the class and isoform selectivity of small-molecule histone deacetylase inhibitors. Biochem J 409, 581-589. https://doi.org/10.1042/BJ20070779
  31. Moradei, O., Vaisburg, A., and Martell, R.E. (2008). Histone deacetylase inhibitors in cancer therapy: New compounds and clinical update of benzamide-type agents. Curr Top Med Chem 8, 841-858. https://doi.org/10.2174/156802608784911581
  32. Shankar, S., and Srivastava, R.K. (2008). Histone deacetylase inhibitors: Mechanisms and clinical significance in cancer: HDAC inhibitor-induced apoptosis. Adv Exp Med Biol 615, 261-298. https://doi.org/10.1007/978-1-4020-6554-5_13
  33. Jones, P., Bottomley, M.J., Carfí, A., Cecchetti, O., Ferrigno, F., Lo Surdo, P., Ontoria, J.M., Rowley, M., Scarpelli, R., Schultz-Fademrecht, C., et al. (2008). 2-Trifluoroacetylthiophenes, a novel series of potent and selective class II histone deacetylase inhibitors. Bioorg Med Chem Lett 18, 3456-3461. https://doi.org/10.1016/j.bmcl.2008.02.026
  34. Schemies, J., Sippl, W., and Jung, M. (2009). Histone deacetylase inhibitors that target tubulin. Cancer Lett 280, 222-232. https://doi.org/10.1016/j.canlet.2009.01.040
  35. Marks, P.A., and Xu, W.S. (2009). Histone deacetylase inhibitors: Potential in cancer therapy. J Cell Biochem 107, 600-608. https://doi.org/10.1002/jcb.22185
  36. Marks, P.A. (2007). Discovery and development of SAHA as an anticancer agent. Oncogene 26, 1351-1356. https://doi.org/10.1038/sj.onc.1210204
  37. Bolden, J.E., Peart, M.J., and Johnstone, R.W. (2006). Anticancer activities of histone deacetylase inhibitors. Nat Rev Drug Discov 5, 769-784. https://doi.org/10.1038/nrd2133
  38. Vannini, A., Volpari, C., Gallinari, P., Jones, P., Mattu, M., Carfí, A., De Francesco, R., Steinkühler, C., and Di Marco, S. (2007). Substrate binding to histone deacetylases as shown by the crystal structure of the HDAC8-substrate complex. EMBO Rep 8, 879-884. https://doi.org/10.1038/sj.embor.7401047
  39. Brodeur, G.M. (2003). Neuroblastoma: Biological insights into a clinical enigma. Nat Rev Cancer 3, 203-216. https://doi.org/10.1038/nrc1014
  40. Oehme, I., Deubzer, H.E., Wegener, D., Pickert, D., Linke, J.P., Hero, B., Kopp-Schneider, A., Westermann, F., Ulrich, S.M., von Deimling, A., et al. (2009). Histone deacetylase 8 in neuroblastoma tumorigenesis. Clin Cancer Res 15, 91-99. https://doi.org/10.1158/1078-0432.CCR-08-0684
  41. Durst, K.L., Lutterbach, B., Kummalue, T., Friedman, A.D., and Hiebert, S.W. (2003). The inv(16) fusion protein associates with corepressors via a smooth muscle myosin heavy-chain domain. Mol Cell Biol 23, 607- 619. https://doi.org/10.1128/MCB.23.2.607-619.2003
  42. Gu, W., Nusinzon, I., Smith, R.D.Jr., Horvath, C.M., and Silverman, R.B. (2006). Carbonyl-sulfurcontaining analogs of suberoylanilide hydroxamic acid: Potent inhibition of histone deacetylases. Bioorg Med Chem 14, 3320-3329. https://doi.org/10.1016/j.bmc.2005.12.047
  43. Wu, T.Y., Hassig, C., Wu, Y., Ding, S., and Schultz, P.G. (2004). Design, synthesis, and activity of HDAC inhibitors with a N-formyl hydroxylamine head group. Bioorg Med Chem Lett 14, 449-453. https://doi.org/10.1016/j.bmcl.2003.10.055
  44. Jeffrey, M.B., Zuomei, L., Daniel, D., and Claire, B. (2004). Methods for specifically inhibiting histone-7 and 8. Patents US 2004/0072770 A1.
  45. Dizhong, C., Weiping, D., Kand, S., Hong, Y.S., Eric, T.S., Niefang, Y., and Yong, Z. (2007). Benzimidazole derivatives: Preparation and pharmaceutical applications. Patents US 2007/0043043 A1.
  46. Walter, S., Haishan, W., and Zheng, Y. (2007). Biaryl linked hydroxamates: Preparation and pharmaceutical applications. Patents US 2007/0167499 A1.
  47. Ze-Yi, L., Haishan, W., and Yan, Z. (2008). Aclyurea connected and sulfonamide connected hydroxamates. Patents US 2008/0070954 A1.
  48. Joseph, J.B., and Sriram, B. (2008). Uses of selective inhibitors of HDAC8 for treatment of T-cell proliferative disorders. Patents US 2008/0112889 A1.
  49. Brooks, B.R., Bruccoleri, R.E., Olafson, B.D., States, D.J., Swaminathan, S., and Karplus, M. (1983). CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4, 187-217. https://doi.org/10.1002/jcc.540040211
  50. Tetko, I. V. (2005). Computing chemistry on the web. Drug Discov Today 10, 1497-1500. https://doi.org/10.1016/S1359-6446(05)03584-1
  51. Caballero, J., Fernández, L., Garriga, M., Abreu, J.I., Collina, S., and Fernández, M. (2007). Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines. J Mol Graph Model 26, 166-178. https://doi.org/10.1016/j.jmgm.2006.11.002
  52. So, S.S., and Karplus, M. (1996). Genetic neural networks for quantitative structure-activity relationships: improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors. J Med Chem 39, 1521-1530. https://doi.org/10.1021/jm9507035
  53. Holland, J.H. (1992). Adaptation in Natural and Artificial Systems, 2nd Edition, (Cambridge: MIT Press). pp. 15-18.
  54. Yujie, D., Qiang, W., Xiuli, Z., Shiru, Jia., Heng, Z., Dacheng, F., and Peng, Yu. (2010). Molecular docking and QSAR study on steroidal compounds as aromatase inhibitors. Eur J Med Chem 45, 5612-5620. https://doi.org/10.1016/j.ejmech.2010.09.011