TSFSOM: Transmembrane Segments Prediction by Fuzzy Self-Organizing Map

  • Yong Deng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


A novel method based on fuzzy Self-Organizing Map to detect the transmembrane segments, called TSFSOM, is presented in the paper. The multivariate ”time” series of transmembrane proteins are classified by fuzzy Self-Organizing Map into five classes. Through the analysis of resulting trajectories on the map, frequent patterns of transmembrane segments are detected and even some kind of ”new” knowledge about membrane insertion mechanism is obtained. The discovered patterns and the knowledge are then used to predict transmembrane segments for query sequence. The prediction results not only show that the method is powerful, but also prove that the patterns and the knowledge about the interaction between the patterns are effective and acceptable.


Frequent Pattern Transmembrane Helix Transmembrane Segment Membrane Segment Membrane Protein Structure 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, Q., Chen, Z.Q., Wang, B.H., Zhu, Y.S., Li, Y.X.: Research on Several Prediction Methods of Membrane Protein Structure and Topology. High Tech. Lett. 7, 1–4 (2001)Google Scholar
  2. 2.
    Kyte, J., Doolittle, R.F.: A Simple Method For Displaying the Hydropathic Character of a Protein. J. Mol. Biol. 157, 105–132 (1982)CrossRefGoogle Scholar
  3. 3.
    Von Heijine, G.: The Distribution of Positively Charged Residues in Bacterial Inner Membrane Proteins Correlates with the Transmembrane Topology. EMBO. J. 5, 3021–3027 (1986)Google Scholar
  4. 4.
    von Heijine, G.: Membrane Protein Structure Prediction: Hydrophobic Analysis and the Positive-inside Rule. J. Mol. Biol. 225, 487–494 (1992)CrossRefGoogle Scholar
  5. 5.
    Persson, B., Argos, P.: Prediction of Transmembrane Segments in Proteins Utilizing Multiple Sequence Alignments. J. Mol. Bio. 273, 182–192 (1994)CrossRefGoogle Scholar
  6. 6.
    Persson, B., Argos, P.: Topology Prediction of Membrane Proteins. Protein Sci 273, 182–192 (1994)Google Scholar
  7. 7.
    Jones, D.T., Taylor, W.R., Thornton, J.M.: A Model Recognition Approach to the Prediction Of All-helical Membrane Protein Structure and Topology. Biochemistry 33, 3038–3049 (1994)CrossRefGoogle Scholar
  8. 8.
    Deng, Y., Liu, Q.: An Improved Optimal Fuzzy Information Fusion Method and Its Application in Group Decision. J Comp. Sys. Sci. Int. 44, 531–541 (2005)Google Scholar
  9. 9.
    Deng, Y., Liu, Q.: A TOPSIS-based Centroid-index Ranking Method of Fuzzy Numbers and Its Application in Decision-making. Cyber. Sys. 36, 581–595 (2005)CrossRefMATHGoogle Scholar
  10. 10.
    Liu, Q., Zhu, Y.S., Wang, B.H., Li, Y.X.: A HMM-based Method to Predict the Transmembrane Regions of Beta-barrel Membrane Proteins. Computational Bio. and Chemistry 27, 69–76 (2003)CrossRefGoogle Scholar
  11. 11.
    Deng, Y., Liu, Q., Li, Y.X.: Scoring Hidden Markov Models to Discriminate Beta-baffel Membrane Proteins Volume. Computational Bio. and Chemistry 28, 189–194 (2004)CrossRefMATHGoogle Scholar
  12. 12.
    Rost, B., Casadio, R., Fariselli, P., Sander, C.: Transmembrane Helices Predicted at 95 Percent Accuracy. Protein Sci. 4, 521–533 (1995)CrossRefGoogle Scholar
  13. 13.
    Rost, B., Casadio, R., Fariselli, P., Sander, C.: Prediction For Helical Transmembrane Proteins at 86 Percent Accuracy. Protein Sci. 5, 1704–1718 (1996)CrossRefGoogle Scholar
  14. 14.
    Deng, Y., Liu, Q., Li, Y.X.: Prediction Of Transmembrane Segments Based On Fuzzy Cluster Analysis Of Amino Acids. Acta Chimica Sinica 62, 1968–1976 (2004)Google Scholar
  15. 15.
    Jacoboni, I., Martelli, P.L., Fariselli, P., De, P.V., Casadio, R.: Prediction of the Transmembrane Regions of Beta-barrel Membrane Proteins with a Neural Network- Predictoremph. Protein Sci. 10, 779–787 (2001)CrossRefGoogle Scholar
  16. 16.
    Gromiha, M.M., Ahmad, S., Suwa, M.: Neural Network-based Prediction of Transmembrane Beta-strand Segments in Outer Mmembrane Proteins. J.Comp. Chem. 25, 762–767 (2004)CrossRefGoogle Scholar
  17. 17.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)Google Scholar
  18. 18.
    Hu, W.M., Xie, D., Tan, T.L., Steve, M.: Learning Activity Patterns Using Fuzzy Self-Organizing Neural Network. IEEE Trans. on Sys. Man Cyber. B 34, 1618–1626 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yong Deng
    • 1
  1. 1.Zhejiang Police Vocational AcademyHangzhouChina

Personalised recommendations