Using Fuzzy Support Vector Machine Network to Predict Low Homology Protein Structural Classes

  • Tongliang Zhang
  • Rong Wei
  • Yongsheng Ding
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)

Abstract

Prediction of protein structural classes for low homology proteins is a challenging research task in bioinformatics. A dual-layer fuzzy support vector machine (FSVM) network approach is proposed to predict protein structural classes. A protein sample can be represented by nine representation feature vectors: pair couple amino acid (210-D) and eight pseudo amino acid composition vectoers (PseAAC). Eight physicochemical properties of amino acids extracted from AAIndex databank are used to calculate low frequencies of power spectrum density of sequence-order correlation in protein sequence. In the first layer of FSVM network, nine FSVM classifiers are established, which are trained by different protein feature vectors, respectively. The outputs of the first layer are reclassified by FSVM classifier in 2nd layer of the network. The performance of proposed method is validated by low homology (average 25%) dataset covering 1673 proteins. The promising results indicate that the new method may become a useful tool for predicting not only the structural classification of proteins but also their other attributes.

Keywords

Power Spectrum Density Jackknife Test Fuzzy Support Vector Machine Biophysical Research Communication Pseudo Amino Acid Composition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: J. Mol. Biol. 247, 536–540 (1995)Google Scholar
  2. 2.
    Lo Conte, L., Brenner, S.E., Hubbard, T.J.P., Chothia, C., Murzin, A.: Nucl. Acid Res. 30(1), 264–267 (2002)Google Scholar
  3. 3.
    Andreeva, A., Howorth, D., Brenner, S.E., Hubbard, T.J.P., Chothia, C., Murzin, A.: Nucl. Acid Res. 32, D226–D229 (2004)Google Scholar
  4. 4.
    Klein, P., Delisi, C.: Prediction of Protein Structural Class from the Amino Acid Sequence. Biopolymers 25, 1659–1672 (1986)CrossRefGoogle Scholar
  5. 5.
    Cai, Y.D., Liu, X.J., Xu, X., Zhou, G.P.: Support Vector Machines for Predicting Protein Structural Class. BMC Bioinformatics 2, 3–7 (2001)CrossRefGoogle Scholar
  6. 6.
    Cao, Y., Liu, S., Zhang, L., Qin, J., Wang, J., Tang, K.: Prediction of Protein |Structural Class with Rough Sets. BMC Bioinformatics 7, 20 (2006)CrossRefGoogle Scholar
  7. 7.
    Chen, C., Zhou, X., Tian, Y., Zou, X., Cai, P.: Predicting Protein Structural Class with Pseudo Amino Acid Composition and Support Vector Machine Fusion Network. Anal. Biochem. 357, 116–121 (2006)CrossRefGoogle Scholar
  8. 8.
    Chou, K.C., Cai, Y.D.: Predicting Protein Structural Class by Functional Domain Composition. Biochemical and Biophysical Research Communications (Corrigendum: ibid., 2005, Vol.329, 1362) 321, 1007–1009 (2004)CrossRefGoogle Scholar
  9. 9.
    Du, Q.S., Jiang, Z.Q., He, W.Z., Li, D.P., Chou, K.C.: Amino Acid Principal Component Analysis (AAPCA) and Its Applications in Protein Structural Class Prediction. Journal of Biomolecular Structure and Dynamics 23, 635–640 (2006)Google Scholar
  10. 10.
    Feng, K.Y., Cai, Y.D., Chou, K.C.: Boosting Classifier for Predicting Protein Domain Structural Class. Biochemical and Biophysical Research Communications 334, 213–217 (2005)CrossRefGoogle Scholar
  11. 11.
    Luo, R.Y., Feng, Z.P., Liu, J.K.: Prediction of Protein Structural Class by Amino Acid and Polypeptide Composition. Eur. J. Biochem. 269, 4219–4225 (2002)CrossRefGoogle Scholar
  12. 12.
    Niu, B., Cai, Y.D., Lu, W.C., Zheng, G.Y., Chou, K.C.: Predicting Protein Structural Class with AdaBoost learner. Protein & Peptide Letters 13, 489–492 (2006)CrossRefGoogle Scholar
  13. 13.
    Shen, H.B., Yang, J., Liu, X.J., Chou, K.C.: Using Supervised Fuzzy Clustering to Predict Protein Structural Classes. Biochemical and Biophysical Research Communications 334, 577–581 (2005)CrossRefGoogle Scholar
  14. 14.
    Sun, X.D., Huang, R.B.: Prediction of Protein Structural Classes Using Support Vector Machines. Amino Acids 30, 469–475 (2006)CrossRefGoogle Scholar
  15. 15.
    Xiao, X., Shao, S.H., Huang, Z.D., Chou, K.C.: Using Pseudo Amino Acid Composition to Predict Protein Structural Classes: Approached with Complexity Measure Factor. Journal of Computational Chemistry 27, 478–482 (2006)CrossRefGoogle Scholar
  16. 16.
    Kedarisetti, K.D., Kurgan, L., Dick, S.: Classifier Ensemble s for Protein Structural Class Prediction with Varying Homology. Biochemical and Biophysical Research Communications 348, 981–988 (2006)CrossRefGoogle Scholar
  17. 17.
    Wang, Z.X., Yuan, Z.: How Good is the Prediction of Protein Structural Class by the Component-coupled Method? Proteins 38, 165–175 (2000)CrossRefGoogle Scholar
  18. 18.
    Chou, K.C., Shen, H.B.: Hum-PLoc: A Novel Ensemble Classifier for Predicting Human Protein Subcellular Localization. Biochem. Biophys. Res. Commun. 347, 150–157 (2006)CrossRefGoogle Scholar
  19. 19.
    Nanni, L., Lumini, A.: MppS: An Ensemble of Support Vector Machine Based on Multiple Physicochemical Properties of Amino Acids. Eurocomputing 69, 1688–1690 (2006)CrossRefGoogle Scholar
  20. 20.
    Nanni, L., Lumini, A.: Ensemblator: An Ensemble of Classifiers for Reliable Classification of Biological Data. Pattern Recognition Letters 28, 622–630 (2007)CrossRefGoogle Scholar
  21. 21.
    Peng, Y.H.: A Novel Ensemble Machine Learning for Robust Microarray Data Classification. Computers in Biology and Medicine 36, 553–573 (2006)CrossRefGoogle Scholar
  22. 22.
    Kurgan, L., Homaeian, L.: Prediction of Structural Classes for Protein Sequences and Domain: Impact of Prediction algorithms, Sequence Representation and Homology, and Test Procedures on Accuracy. Pattern Recognition 39, 2323–2343 (2006)MATHCrossRefGoogle Scholar
  23. 23.
    Chou, K.C.: Prediction of Protein Structural Classes and Subcellular Locations. Curr. Protein Peptide Sci. 1, 171–208 (2000)CrossRefGoogle Scholar
  24. 24.
    Kawashima, S., Ogata, H., Kanehisa, M.: AAindex: Amino Acid Index Database. Nucleic Acids Res. 27, 368–369 (1999)CrossRefGoogle Scholar
  25. 25.
    Chou, K.C.: Prediction of Protein Cellular Attributes Using Pseudo Amino Acid Composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol.44, 60) 43, 246–255 (2001)CrossRefGoogle Scholar
  26. 26.
    Cai, Y.D., Liu, X.J., Xu, X.B., Chou, K.C.: Artificial Neural Network Method for Predicting Protein Secondary Structure Content. Computers and Chemistry 26, 347–350 (2002)CrossRefGoogle Scholar
  27. 27.
    Chou, K.C.: Using Pair-coupled Amino Acid composition to Predict Protein Secondary Structure Content. J. Protein Chem. 18, 473–480 (1999)CrossRefGoogle Scholar
  28. 28.
    Liu, H., Wang, M., Chou, K.C.: Low-frequency Fourier Spectrum for Predicting Membrane Protein Types. Biochem. Biophys. Res. Commun. 336, 737–739 (2005)CrossRefGoogle Scholar
  29. 29.
    Liu, H., Yang, J., Wang, M., Xue, L., Chou, K.C.: Using Fourier Spectrum Analysis and Pseudo Amino Acid Composition for Prediction of Membrane Protein Types. The Protein Journal 24, 385–389 (2005)CrossRefGoogle Scholar
  30. 30.
    Zhang, T.L., Ding, Y.S.: Using Pseudo Amino Acid Composition and Binary-tree Support Vector Machines to Predict Protein Structural Classes. Amino Acids (2007) 10.1007/s00726-007-0496-1Google Scholar
  31. 31.
    Chou, K.C.: Review: Low-frequency Collective Motion in Biomacromolecules and Its Biological functions. Biophysical Chemistry 30, 3–48 (1988)CrossRefGoogle Scholar
  32. 32.
    Chou, K.C.: Low-frequency Resonance and Cooperativity of Hemoglobin. Trends in Biochemical Sciences 14, 212–213 (1989)CrossRefGoogle Scholar
  33. 33.
    Shen, H.B., Chou, K.C.: Ensemble Classifier for Protein fold pattern recognition. Bioinformatics 22, 1717–1722 (2006a)CrossRefGoogle Scholar
  34. 34.
    Shen, H.B., Chou, K.C.: Using Ensemble Classifier to Identify Membrane Protein Types. Amino Acids (2006), 10.1007/s00726-006-0439-2Google Scholar
  35. 35.
    Shen, H.B., Yang, J., Chou, K.C.: Fuzzy KNN for Predicting Membrane Protein Types from Pseudo Amino Acid Composition. Journal of Theoretical Biology 240, 9–13 (2006)CrossRefMathSciNetGoogle Scholar
  36. 36.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)MATHGoogle Scholar
  37. 37.
    Abe, S.: Fuzzy LP-SVM for multiClass problems. In: ESANN 2004 proceedings- European symposium on artificial neural networks Bruges (Belgium), 28-30 April 2004 d-side public, pp. 429–434 (2004), ISBN 2-930307-04-8Google Scholar
  38. 38.
    Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)MATHGoogle Scholar
  39. 39.
    Chou, K.C., Zhang, C.T.: Review: Prediction of Protein Structural Classes. Critical Reviews in Biochemistry and Molecular Biology 30, 275–349 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Tongliang Zhang
    • 1
  • Rong Wei
    • 3
  • Yongsheng Ding
    • 1
    • 2
  1. 1.College of Information Sciences and Technology 
  2. 2.Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620P.R. China
  3. 3.College of Sciences, Hebei Polytechnic University, Hebei Tangshan 063009P.R. China

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