Advertisement

Understanding Signal Sequences with Machine Learning

  • Jean-Luc Falcone
  • Renée Kreuter
  • Dominique Belin
  • Bastien Chopard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4447)

Abstract

Protein translocation, the transport of newly synthesized proteins out of the cell, is a fundamental mechanism of life. We are interested in understanding how cells recognize the proteins that are to be exported and how the necessary information is encoded in the so called “Signal Sequences”. In this paper, we address these problems by building a physico-chemical model of signal sequence recognition, using experimental data. This model was built using decision trees. In a first phase the classifier were built from a set of features derived from the current knowledge about signal sequences. It was then expanded by feature generation with genetic algorithms. The resulting predictors are efficient, achieving an accuracy of more than 99% with our wild-type proteins set. Furthermore the generated features can give us a biological insight about the export mechanism. Our tool is freely available through a web interface.

Keywords

Decision Tree Signal Sequence Reduction Function Hydrophobicity Scale Circle Node 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Stryer, L.: Biochemistry, 4th edn. W.H. Freeman and Company, New York (1995)Google Scholar
  2. 2.
    von Heijne, G.: The signal peptide. J. Membrane Biology 115, 195–201 (1990)CrossRefGoogle Scholar
  3. 3.
    Menne, K.M., Hermjakob, H., Apweiler, R.: A comparison of signal sequence prediction methods using a test set of signal peptides. Bioinformatics 16(8), 741–742 (2000)CrossRefGoogle Scholar
  4. 4.
    Nielsen, H., Engelbrecht, J., Brunak, S., von Heijne, G.: Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Engineering 10(1), 1–6 (1997)CrossRefGoogle Scholar
  5. 5.
    Bendtsen, J.D., Nielsen, H., Widdick, D., Palmer, T., Brunak, S.: Prediction of twin-arginine signal peptides. BMC bioinformatics 6(167) (2005)Google Scholar
  6. 6.
    Falcone, J.L.: SigTree website (2007), http://cui.unige.ch/spc/tools/sigtree/
  7. 7.
    Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157(1), 105–132 (1982)CrossRefGoogle Scholar
  8. 8.
    Janin, J., Chothia, C.: Role of hydrophobicity in the binding of coenzymes. appendix. translational and rotational contribution to the free energy of dissociation. Biochemistry 17(15), 2943–2948 (1978)CrossRefGoogle Scholar
  9. 9.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  10. 10.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)Google Scholar
  11. 11.
    von Heijne, G.: A new method for predicting signal sequence clevage site. Nucleic Acid Res. 14, 4683–4690 (1986)CrossRefGoogle Scholar
  12. 12.
    Kawashima, S., Ogata, H., Kanehisa, M.: Aaindex: amino acid index database. Nucleic Acids Res. 27, 368–369 (1999)CrossRefGoogle Scholar
  13. 13.
    Falcone, J.L., Albuquerque, P.: Agrégation des propriétés physico-chimiques des acides aminés. In: IEEE Proc. of CCECE’04, 4, pp. 1881–1884 (2004)Google Scholar
  14. 14.
    Falcone, J.L.: Decoding the Signal Sequence. PhD thesis, University of Geneva, Switzerland, to be published (2007)Google Scholar
  15. 15.
    Fontignie, J., Falcone, J.L.: n-genes website (2005), http://cui.unige.ch/spc/tools/n-genes/
  16. 16.
    Izard, J., Kendall, D.: Signal peptides: exquisitely designed transport promoters. Mol. Microbiol. 13(5), 765–773 (1994)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jean-Luc Falcone
    • 1
  • Renée Kreuter
    • 1
  • Dominique Belin
    • 2
  • Bastien Chopard
    • 1
  1. 1.Département d’informatique, Université de Genève, 1211 Genève 4Switzerland
  2. 2.Département de Pathologie et d’Immunologie, Université de GenèveSwitzerland

Personalised recommendations