Fuzzy Classification of Secretory Signals in Proteins Encoded by the Plasmodium falciparum Genome

  • Erica Logan
  • Richard Hall
  • Nectarios Klonis
  • Susanna Herd
  • Leann Tilley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3213)

Abstract

Over five thousand types of protein are produced by the malaria parasite Plasmodium falciparum. Each protein contains the address of a specific destination to which it will be trafficked by various cellular translocation mechanisms. This address may be encoded in a secretory signal, physically represented as an amino acid subsequence forming a motif or pattern. The different signal sequences are classified according to where they occur with respect to the entire amino acid sequence. Biologists are interested in computational techniques that can automatically classify the large amount of data in the Plasmodium falciparum genome, since they have inferred from ongoing experimentation that a correlation exists between particular signals and significant cellular locations. We describe the development of a web-accessible fuzzy classifier of secretory signals in proteins. The application of this classifier to the entire P. falciparum genome immediately produced some biologically-interesting predictions that are briefly discussed.

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References

  1. 1.
    Wirth, D.F.: The parasite genome: Biological revelations. Nature 419, 495–496 (2002)CrossRefGoogle Scholar
  2. 2.
    Greenwood, B., Mutabingwa, T.: Malaria in 2002. Nature 415, 670–672 (2002)CrossRefGoogle Scholar
  3. 3.
    Gardner, M.J., Hall, N., Fung, E., White, O., Berriman, M., Hyman, R.W., Carlton, J.M., Pain, A., Nelson, K.E., Bowman, S., Paulsen, I.T., James, K., Eisen, J.A., Rutherford, K., Salzberg, S.L., Craig, A., Kyes, S., Chan, M.S., Nene, V., Shallom, S.J., Suh, B., Peterson, J., Angiuoli, S., Pertea, M., Allen, J., Selengut, J., Haft, D., Mather, M.W., Vaidya, A.B., Martin, D.M., Fairlamb, A.H., Fraunholz, M.J., Roos, D.S., Ralph, S.A., McFadden, G.I., Cummings, L.M., Subramanian, G.M., Mungall, C., Venter, J.C., Carucci, D.J., Hoffman, S.L., Newbold, C., Davis, R.W., Fraser, C.M., Barrell, B.: Genome sequence of the human malaria parasite Plasmodium falciparum. Nature 419, 498–511 (2002)CrossRefGoogle Scholar
  4. 4.
    Baruch, D.I., Rogerson, S.J., Cooke, B.M.: Asexual blood stages of malaria antigens: cytoadherence. Chem. Immunol. 80, 144–162 (2002)CrossRefGoogle Scholar
  5. 5.
    Kyte, J., Doolittle, R.F.: A Simple Method for Displaying the Hydropathic Character of a Protein. Journal of Molecular Biology 57, 105–132 (1982)CrossRefGoogle Scholar
  6. 6.
    Martoglio, B., Dobberstein, B.: Signal sequences: more than just greasy peptides. Trends in Cell Biology 8, 410–415 (1998)CrossRefGoogle Scholar
  7. 7.
    von Heijne, G.: A new method for predicting signal sequence cleavage sites. Nucleic Acids Research 14, 4683–4690 (1986)CrossRefGoogle Scholar
  8. 8.
    von Heijne, G.: Signal sequences. The limits of variation. J. Mol. Biol. 184, 99–105 (1985)CrossRefGoogle Scholar
  9. 9.
    Albano, F.R., Foley, M., Tilley, L.: Export of parasite proteins to the erythrocyte cytoplasm: secretory machinery and traffic signals. In: Transport and trafficking in the malaria-infected erythrocyte. Novartis Foundation Symposium, vol. 226, pp. 157–175. Wiley, Chichester (1999)Google Scholar
  10. 10.
    Lingelbach, K.R.: Plasmodium falciparum: A molecular view of protein transport from the parasite into the host erythrocyte. Experimental parasitology 76, 318–327 (1993)CrossRefGoogle Scholar
  11. 11.
    Wickham, M.E., Rug, M., Ralph, S.A., Klonis, N., McFadden, G.I., Tilley, L., Cowman, A.F.: Trafficking and assembly of the cytoadherence complex in Plasmodium falciparum-infected human erythrocytes. Embo Journal 20, 5636–5649 (2001)CrossRefGoogle Scholar
  12. 12.
    Nakai, K., Horton, P.: PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem. Sci. 24, 34–36 (1999)CrossRefGoogle Scholar
  13. 13.
    Nielsen, H., Engelbrecht, J., Brunak, S.: Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Engineering 10, 1–6 (1997)CrossRefMATHGoogle Scholar
  14. 14.
    Nielsen, H., Krogh, A.: Prediction of signal peptides and signal anchors by a hidden Markov model. In: Glasgow, J., et al. (eds.) Proc. Sixth Int. Conf. on Intelligent Systems for Molecular Biology, pp. 122–130 (1998)Google Scholar
  15. 15.
    Anders, R.F., Saul, A.: Malaria vaccines. Parasitol Today 16, 444–447 (2000)CrossRefGoogle Scholar
  16. 16.
    Zhang, C.T., Chou, K.C., Maggiora, G.M.: Predicting protein structural classes from amino acid composition: application of fuzzy clustering. Protein Engineering 8, 425–435 (1995)CrossRefGoogle Scholar
  17. 17.
    Schlosshauer, M., Ohlsson, M.: A novel approach to local reliability of sequence alignments. Bioinformatics 18, 847–854 (2002)CrossRefGoogle Scholar
  18. 18.
    Chang, B.C., Halgamuge, S.K.: Protein motif extraction with neuro-fuzzy optimization. Bioinformatics 18, 1084–1090 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Erica Logan
    • 1
    • 2
  • Richard Hall
    • 2
  • Nectarios Klonis
    • 1
  • Susanna Herd
    • 1
  • Leann Tilley
    • 1
  1. 1.Department of BiochemistryLa Trobe UniversityMelbourneAustralia
  2. 2.Department of Computer Science and EngineeringLa Trobe UniversityMelbourneAustralia

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