Support Vector Regression for Predicting Binding Affinity in Spinocerebellar Ataxia

  • P. R. AshaEmail author
  • M. S. Vijaya
Part of the Studies in Computational Intelligence book series (SCI, volume 771)


Spinocerebellar ataxia (SCA) is an inherited disorder. It arises mainly due to gene mutations, which affect gray matter in the brain causing neurodegeneration. There are certain types of SCA that are caused by repeat mutation in the gene, which produces differences in the formation of protein sequence and structures. Binding affinity is very essential to know how tightly the ligand binds with the protein. In this work, a binding affinity prediction model is built using machine learning. To build the model, predictor variables and their values such as binding energy, IC50, torsional energy and surface area for both ligand and protein are extracted from the complex using AutoDock, AutoDock Vina and PyMOL. A total of 17 structures and 18 drugs were used for learning the support vector regression (SVR) model. Experimental results proved that the SVR-based affinity prediction model performs better than other regression models.


Binding affinity Docking Ligand Machine learning Prediction Protein Protein structure 


  1. 1.
    Thomas, C. Weiss. 2010. Ataxia spinocerebellar: SCA facts and information.Google Scholar
  2. 2.
    Thomas, D. Bird. 2016. Hereditary ataxia overview.Google Scholar
  3. 3.
    Whaley, N.R., S. Fujioka, and Z.K. Wszolek. 2011. Autosomal dominant cerebellar ataxia type I: A review of the phenotypic and genotypic characteristics. Scholar
  4. 4.
    Fischer, E. 1894. Einfluss der configuration auf die working derenzyme. Berichte der Deutschen Chemischen Gesellschaf 27: 2985–2993.CrossRefGoogle Scholar
  5. 5.
    Koshland Jr., D.E. 1963. Correlation of structure and function in enzyme action. Science 142: 1533–1541.CrossRefGoogle Scholar
  6. 6.
    Kuntz, I.D., J.M. Blaney, S.J. Oatley, R. Langridge, and T.E. Ferrin. 1982. A geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology 161 (2): 269–288.CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Li, X., M. Zhu, X. Li, H.Q. Wang, and S. Wang. 2012. Protein-protein binding affinity prediction based on an SVR ensemble. In Intelligent Computing Technology, ICIC 2012, ed. D.S. Huang, C. Jiang, V. Bevilacqua, J.C. Figueroa, vol. 7389. Lecture Notes in Computer Science, Springer: Berlin, Heidelberg.Google Scholar
  9. 9.
    Volkan, Uslan, and Huseyin Seker. 2016. Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression. Applied Soft Computing 43: 210–221.CrossRefGoogle Scholar
  10. 10.
    Bhasin, M., and G.P.S. Raghava. 2004. Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Science: A Publication of the Protein Society 13 (3): 596–607. Scholar
  11. 11.
    Volkan, Uslan, and Huseyin Seker. 2016. Binding affinity prediction of S. Ccerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression. In 2016 IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC), 3445–3448, ISSN 1558-4615.Google Scholar
  12. 12.
    Berman, Helen M., John Westbrook, Zukang Feng, Gary Gilliland, T.N. Bhat, Helge Weissig, Ilya, N. Shindyalov, and Philip E. Bourne. 2000. Protein data bank, Nucleic Acids Research, 28 (1): 235–242.Google Scholar
  13. 13.
    Rebhan, M., V. Chalifa-Caspi, J. Prilusky, and D. Lancet. 1997. GeneCards: Integrating information about genes, proteins and diseases. Trends in Genetics 13: 163.CrossRefGoogle Scholar
  14. 14.
    Soman, K.P., R. Loganathan, and V. Ajay. 2009. Machine learning with SVM and other kernel methods.Google Scholar
  15. 15.
    LIBSVM is an open source tool.

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer SciencePSGR Krishnammal College for WomenCoimbatoreIndia

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