The Application of Support Vector Machine and Behavior Knowledge Space in the Disulfide Connectivity Prediction Problem

  • Hong-Yu Chen
  • Kuo-Tsung Tseng
  • Chang-Biau YangEmail author
  • Chiou-Yi Hor
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 454)


In this paper, we apply support vector machine (SVM) and behavior knowledge space (BKS) to the disulfide connectivity prediction problem. The problem aims to establish the disulfide connectivity pattern of the target protein. It is an important problem since a disulfide bond, formed by two oxidized cysteines, plays an important role in the protein folding and structure stability. The disulfide connectivity prediction problem is difficult because the number of possible patterns grows rapidly with respect to the number of cysteines. We discover some rules to discriminate the patterns with high accuracy in various methods. Then, the pattern-wise and pair-wise BKS methods to fuse multiple classifiers constructed by the SVM methods are proposed. Finally, the CSP (cysteine separation profile) method is also applied to form our hybrid method. We perform some simulation experiments with the 4-fold cross-validation on SP39 dataset. The prediction accuracy of our method is increased to 69.1 %, which is better than the best previous result 65.9 %.


Disulfide bond Cysteine Connectivity pattern Support vector machine Behavior knowledge space 



This research work was partially supported by the National Science Council of Taiwan under contract NSC 100-2221-E-242-003.


  1. 1.
    Harrison, P.M., Sternberg, M.J.E.: Analysis and classification of disulphide connectivity in proteins: the entropic effect of cross-linkage. J. Mol. Biol. 244(4), 448–463 (1994)CrossRefGoogle Scholar
  2. 2.
    Lu, C.-H., Chen, Y.-C., Yu, C.-S., Hwang, J.-K.: Predicting disulfide connectivity patterns. Proteins Struct. Funct. Genet. 67, 262–270 (2007)CrossRefGoogle Scholar
  3. 3.
    Mirny, L.A., Shakhnovich, E.I.: How to derive a protein folding potential? a new approach to an old problem. J. Mol. Biol. 264(5), 1164–1179 (1996)CrossRefGoogle Scholar
  4. 4.
    Rubinstein, R., Fiser, A.: Predicting disulfide bond connectivity in proteins by correlated mutations analysis. Bioinformatics 24(4), 498–504 (2008)CrossRefGoogle Scholar
  5. 5.
    Baldi, P., Cheng, J., Vullo, A.: Large-scale prediction of disulphide bond connectivity. In: Saul, L., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17, pp. 97–104. MIT Press, Cambridge (2005)Google Scholar
  6. 6.
    Cheng, J., Saigo, H., Baldi, P.: Large-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural networks, and weighted graph matching. Proteins Struct. Funct. Genet. 62, 617–629 (2006)CrossRefGoogle Scholar
  7. 7.
    Fariselli, P., Riccobelli, P., Casadio, R.: Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins. Proteins Struct. Funct. Genet. 36, 340–346 (1999)CrossRefGoogle Scholar
  8. 8.
    Ferre, F., Clote, P.: Disulfide connectivity prediction using secondary structure information and diresidue frequencies. Bioinformatics 21(10), 2336–2346 (2005)CrossRefGoogle Scholar
  9. 9.
    Martelli, P.L., Fariselli, P., Malaguti, L., Casadio, R.: Prediction of the disulfide-bonding state of cysteines in proteins at 88 % accuracy. Protein Sci. 11, 2735–2739 (2002)CrossRefGoogle Scholar
  10. 10.
    Vullo, A., Frasconi, P.: Disulfide connectivity prediction using recursive neural networks and evolutionary information. Bioinformatics 20(5), 653–659 (2004)CrossRefGoogle Scholar
  11. 11.
    Chen, Y.-C., Lin, Y.-S., Lin, C.-J., Hwang, J.-K.: Prediction of the bonding states of cysteines using the support vector machines based on multiple feature vectors and cysteine state sequences. Proteins Struct. Funct. Genet. 55, 1036–1042 (2004)CrossRefGoogle Scholar
  12. 12.
    Chen, Y.-C., Hwang, J.-K.: Prediction of disulfide connectivity from protein sequences. Proteins Struct. Funct. Genet. 61, 507–512 (2005)CrossRefGoogle Scholar
  13. 13.
    Frasconi, P., Passerini, A., Vullo, A.: A two-stage svm architecture for predicting the disulfide bonding state of cysteines. In: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, pp. 25–34 (2002)Google Scholar
  14. 14.
    Jayavardhana Rama, G.L., Shilton, A.P., Parker, M.M., Palaniswami, M.: Prediction of cystine connectivity using svm. Bioinformation 1(2), 69–74 (2005)CrossRefGoogle Scholar
  15. 15.
    Liu, H.-L., Chen, S.-C.: Prediction of disulfide connectivity in proteins with support vector machine. J. Chin. Inst. Chem. Eng. 38(1), 63–70 (2007)CrossRefGoogle Scholar
  16. 16.
    Tsai, C.-H., Chen, B.-J., Chan, C.-H., Liu, H.-L., Kao, C.-Y.: Improving disulfide connectivity prediction with sequential distance between oxidized cysteines. Bioinformatics 21(24), 4416–4419 (2005)CrossRefGoogle Scholar
  17. 17.
    Vincent, M., Passerini, A., Labbe, M., Frasconi, P.: A simplified approach to disulfide connectivity prediction from protein sequences. BMC Bioinform. 9(1), 20 (2008)CrossRefGoogle Scholar
  18. 18.
    Zhao, E., Liu, H.-L., Tsai, C.-H., Tsai, H.-K., Chan, C.-H., Kao, C.-Y.: Cysteine separations profiles on protein sequences infer disulfide connectivity. Bioinformatics 21(8), 1415–1420 (2005)CrossRefGoogle Scholar
  19. 19.
    Wang, C.-J., Yang, C.-B., Hor, C.-Y., Tseng, K.-T.: Disulfide bond prediction with hybrid models. In: Proceedings of the 2012 International Conference on Computing and Security (ICCS 2012), Ulaanbaatar, Mongolia, July 2012Google Scholar
  20. 20.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1999)Google Scholar
  21. 21.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines (2001).
  22. 22.
    Raudys, S., Roli, F.: The behavior knowledge space fusion method: analysis of generalization error and strategies for performance improvement. In: Windeatt, T., Roli, F. (eds.) MCS 2003. LNCS, vol. 2709, pp. 55–64. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  23. 23.
    Chung, W.-C., Yang, C.-B., Hor, C.-Y.: An effective tuning method for cysteine state classification. In: Proceedings of National Computer Symposium, Workshop on Algorithms and Bioinformatics, Taipei, Taiwan, 27–28 November 2009Google Scholar
  24. 24.
    Chen, G., Deng, H., Gui, Y., Pan, Y., Wang, X.: Cysteine separations profiles on protein secondary structure infer disulfide connectivity. In: 2006 IEEE International Conference on Granular Computing, pp. 663–665, May 2006Google Scholar
  25. 25.
    Chuang, C.-C., Chen, C.-Y., Yang, J.-M., Lyu, P.-C., Hwang, J.-K.: Relationship between protein structures and disulfide-bonding patterns. Proteins Struct. Funct. Genet. 53, 1–5 (2003)CrossRefGoogle Scholar
  26. 26.
    Jones, D.T.: Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292(2), 195–202 (1999)CrossRefGoogle Scholar
  27. 27.
    Fariselli, P., Casadio, R.: Prediction of disulfide connectivity in proteins. Bioinformatics 17(10), 957–964 (2001)CrossRefGoogle Scholar
  28. 28.
    Chen, B.-J., Tsai, C.-H., Chan, C.-H., Kao, C.-Y.: Disulfide connectivity prediction with 70 % accuracy using two-level models. Proteins Struct. Funct. Genet. 64, 246–252 (2006)CrossRefGoogle Scholar
  29. 29.
    Chen, Y.-C.: Prediction of Disulfide Connectivity from Protein Sequences. Ph.D. dissertation, National Chiao Tung University, Hsinchu, Taiwan (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Hong-Yu Chen
    • 1
  • Kuo-Tsung Tseng
    • 2
  • Chang-Biau Yang
    • 1
    Email author
  • Chiou-Yi Hor
    • 1
  1. 1.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan
  2. 2.Department of Shipping and Transportation ManagementNational Kaohsiung Marine UniversityKaohsiungTaiwan

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