DOI QR코드

DOI QR Code

Classification of HDAC8 Inhibitors and Non-Inhibitors Using Support Vector Machines

  • Cao, Guang Ping (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 (GNU)) ;
  • 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 (GNU)) ;
  • 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 (GNU)) ;
  • 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 (GNU))
  • 투고 : 2012.03.26
  • 심사 : 2012.03.30
  • 발행 : 2012.03.30

초록

Introduction: Histone deacetylases (HDAC) are a class of enzymes that remove acetyl groups from ${\varepsilon}$-N-acetyl lysine amino acids of histone proteins. Their action is opposite to that of histone acetyltransferase that adds acetyl groups to these lysines. Only few HDAC inhibitors are approved and used as anti-cancer therapeutics. Thus, discovery of new and potential HDAC inhibitors are necessary in the effective treatment of cancer. Materials and Methods: This study proposed a method using support vector machine (SVM) to classify HDAC8 inhibitors and non-inhibitors in early-phase virtual compound filtering and screening. The 100 experimentally known HDAC8 inhibitors including 52 inhibitors and 48 non-inhibitors were used in this study. A set of molecular descriptors was calculated for all compounds in the dataset using ADRIANA. Code of Molecular Networks. Different kernel functions available from SVM Tools of free support vector machine software and training and test sets of varying size were used in model generation and validation. Results and Conclusion: The best model obtained using kernel functions has shown 75% of accuracy on test set prediction. The other models have also displayed good prediction over the test set compounds. The results of this study can be used as simple and effective filters in the drug discovery process.

키워드

참고문헌

  1. Clark, D.E., and Pickett, S.D. (2000). Computational methods for the prediction of 'drug-likeness'. Drug Discov Today 5, 49-58. https://doi.org/10.1016/S1359-6446(00)80001-X
  2. Schneider, G., and Böhm, H.J. (2002). Virtual screening and fast automated docking methods. Drug Discov Today 7, 64-70. https://doi.org/10.1016/S1359-6446(01)02091-8
  3. Byvatov, E., Fechner, U., Sadowski, J., and Schneider G. (2003). Comparison of Support Vector and Artificial Neural Network System for Drug/ Nondrug Classification. J Chem Inf Comput Sci 43, 1882-1889 https://doi.org/10.1021/ci0341161
  4. Cortes, C., and Vapnik, V. (1995). Support-vector networks. Mach Learn 20, 273-297.
  5. Han, L.Y., Zheng, C. J., Xie, B., Jia, J., Ma, X.H., Zhu, F., Lin, H.H., Chen, X., and Chen, Y.Z. (2007). Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. Drug Discov Today 12, 7-8.
  6. Yap, C.W. and Chen, Y.Z. (2004). Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines. J Chem Inf Model 45, 982-992.
  7. Yang, Z.R., and Chou, K.C. (2004). Bio-support Vector Machines for Computational Proteomics. Bioinformatics 20, 735-741. https://doi.org/10.1093/bioinformatics/btg477
  8. Mahe, P., Ueda, N., Akutsu, T., Perret, J.L., and Vert, J.P. (2005). Graph Kernels for Molecular Structure-Activity Relationship Analysis with Support Vector Machines. J Chem Inf Model 45, 939-951. https://doi.org/10.1021/ci050039t
  9. Burbidge, R., Trotter, M., Buxton, B., and Holden, S. (2001). Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem 26, 5-14. https://doi.org/10.1016/S0097-8485(01)00094-8
  10. Warmuth, M.K., Liao, J., Ratsch, G., Mathieson, M., Putta, S., and Lemmen, C. (2003). Active learning with Support Vector Machines in the drug discovery process. J Chem Inf Comput Sci 43, 667-673. https://doi.org/10.1021/ci025620t
  11. Wilton, D., Willett, P., Lawson, K., and Mullier, G. (2003). Comparison of ranking methods for virtual screening in lead-discovery programs. J Chem Inf Comput Sci 43, 469-474. https://doi.org/10.1021/ci025586i
  12. Choudhary, C., Kumar, C., Gnad, F., Nielsen, M.L., Rehman, M., Walther, T.C., Olsen, J.V., and Mann, M. (2009). Lysine acetylation targets protein complexes and co-regulates major cellular functions. Science 325(5942), 834-40. https://doi.org/10.1126/science.1175371
  13. 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 Reports 8, 879-884. https://doi.org/10.1038/sj.embor.7401047
  14. Dokmanovic, M., Clarke, C., and Marks, P.A. (2007). Histone deacetylase inhibitors: overview and perspectives. Mol Cancer Res 5(10), 981-9. https://doi.org/10.1158/1541-7786.MCR-07-0324
  15. Valenzuela-Fernández, A., Cabrero J.R., Serrador J.M., and Sánchez- Madrid, F. (2008). HDAC6: a key regulator of cytoskeleton, cell migration and cell-cell interactions error. Trends Cell Biol 18(6), 291-297. https://doi.org/10.1016/j.tcb.2008.04.003
  16. 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
  17. Brodeur, G.M. (2003). Neuroblastoma: Biological insights into a clinical enigma. Nat Rev Cancer 3, 203-216. https://doi.org/10.1038/nrc1014
  18. 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., Fischer, M., and Witt, O. (2009). Histone deacetylase 8 in neuroblastoma tumorigenesis. Clin Cancer Res 15, 91-99. https://doi.org/10.1158/1078-0432.CCR-08-0684
  19. 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
  20. 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
  21. 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
  22. Abu-Awwad F. M. (2009). A computational Study of Histamine H1-Receptor Agonist Activity Using QSPR and Molecular Surface Electrostatic Potential. Imt J ChemTech Res 1(3), 742-750.
  23. Todeschini, R., and Consonni, V. (2000). Handbook of Molecular Descriptors, Weinheim: Wiley-VCH.
  24. Chang, C. C., and Lin, C. J. (2011). LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology doi.acm.org/10.1145/ 1961189.1961-199. Software available at http://www.csie.ntu.edu.tw/-cjlin/lib svm

피인용 문헌

  1. Ligand release mechanisms and channels in histone deacetylases vol.34, pp.26, 2013, https://doi.org/10.1002/jcc.23390