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IAPR International Conference on Pattern Recognition in Bioinformatics

PRIB 2012: Pattern Recognition in Bioinformatics pp 26–37Cite as

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A Genetic Algorithm for Scale-Based Translocon Simulation

A Genetic Algorithm for Scale-Based Translocon Simulation

  • Sami Laroum23,
  • Béatrice Duval23,
  • Dominique Tessier24 &
  • …
  • Jin-Kao Hao23 
  • Conference paper
  • 1572 Accesses

Part of the Lecture Notes in Computer Science book series (LNBI,volume 7632)

Abstract

Discriminating between secreted and membrane proteins is a challenging task. A recent and important discovery to understand the machinery responsible of the insertion of membrane proteins was the results of Hessa experiments [9]. The authors developed a model system for measuring the ability of insertion of engineered hydrophobic amino acid segments in the membrane. The main results of these experiments are summarized in a new ”biological hydrophobicity scale”. In this scale, each amino acid is represented by a curve that indicates its contribution to the process of protein insertion according to its position inside the membrane. We follow the same hypothesis as Hessa but we propose to determine “in silico” the hydrophobicity scale. This goal is formalized as an optimization problem, where we try to define a set of curves that gives the best discrimination between signal peptide and protein segments which cross the membrane. This paper describes the genetic algorithm that we developed to solve this problem and the experiments that we conducted to assess its performance.

Keywords

  • Membrane Proteins
  • Classification
  • Optimization
  • Genetic Algorithm

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References

  1. Bendtsen, J.D., Nielsen, H., von Heijne, G., Brunak, S.: Improved prediction of signal peptides: SignalP 3.0. Journal of Molecular Biology 340(4), 783–795 (2004)

    CrossRef  Google Scholar 

  2. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The Protein Data Bank. Nucleic Acids Research 28(1), 235–242 (2000)

    CrossRef  Google Scholar 

  3. Bernsel, A., Viklund, H., Falk, J., Lindahl, E., von Heijne, G., Elofsson, A.: Prediction of membrane-protein topology from first principles. Proceedings of the National Academy of Sciences of the Unites States of America 105(20), 7177–7181 (2008)

    CrossRef  Google Scholar 

  4. Cuthbertson, J.M., Doyle, D.A., Sansom, M.S.P.: Transmembrane helix prediction: a comparative evaluation and analysis. Protein Engineering Design and Selection 18(6), 295–308 (2005)

    CrossRef  Google Scholar 

  5. Eisenberg, D., Weiss, R.M., Terwilliger, T.C.: The helical hydrophobic moment: a measure of the amphiphilicity of a helix. Nature 299(5881), 371–374 (1982)

    CrossRef  Google Scholar 

  6. Engelman, D.M., Steitz, T.A., Goldman, A.: Identifying nonpolar transbilayer helices in amino acid sequences of membrane proteins. Annual Review of Biophysics and Biophysical Chemistry 15, 321–353 (1986)

    CrossRef  Google Scholar 

  7. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, 1st edn. Addison-Wesley (January 1989)

    Google Scholar 

  8. Hessa, T., Kim, H., Bihlmaier, K., Lundin, C., Boekel, J., Andersson, H., Nilsson, I., White, S.H., von Heijne, G.: Recognition of transmembrane helices by the endoplasmic reticulum translocon. Nature 433(7024), 377–381 (2005)

    CrossRef  Google Scholar 

  9. Hessa, T., Meindl-Beinker, N.M., Bernsel, A., Kim, H., Sato, Y., Lerch-Bader, M., Nilsson, I., White, S.H., von Heijne, G.: Molecular code for transmembrane-helix recognition by the Sec61 translocon. Nature 450(7172), 1026–U2 (2007)

    Google Scholar 

  10. Jones, D.T.: Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics 23(5), 538–544 (2007)

    CrossRef  Google Scholar 

  11. Jones, D.T., Taylor, W.R., Thorton, J.M.: A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry 33(10), 3038–3049 (1994)

    CrossRef  Google Scholar 

  12. Junker, V.L., Apweiler, R., Bairoch, A.: Representation of functional information in the SWISS-PROT data bank. Bioinformatics 15(12), 1066–1067 (1999)

    CrossRef  Google Scholar 

  13. Kall, L.: Prediction of transmembrane topology and signal peptide given a protein’s amino acid sequence. Method. In: Molecular Biology, vol. 673, pp. 53–62 (2010)

    Google Scholar 

  14. Kall, L., Krogh, A., Sonnhammer, E.L.L.: A combined transmembrane topology and signal peptide prediction method. Journal of Molecular Biology 338(5), 1027–1036 (2004)

    CrossRef  Google Scholar 

  15. Krogh, A., Larsson, B., von Heijne, G., Sonnhammer, E.L.L.: Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. Journal of Molecular Biology 305(3), 567–580 (2001)

    CrossRef  Google Scholar 

  16. Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. Journal of Molecular Biology 157(1), 105–132 (1982)

    CrossRef  Google Scholar 

  17. Laroum, S., Duval, B., Tessier, D., Hao, J.-K.: Multi-Neighborhood Search for Discrimination of Signal Peptides and Transmembrane Segments. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2011. LNCS, vol. 6623, pp. 111–122. Springer, Heidelberg (2011)

    Google Scholar 

  18. Pasquier, C., Promponas, V.J., Palaios, G.A., Hamodrakas, J.S., Hamodrakas, S.J.: A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm. Protein Engineering 12(5), 381–385 (1999)

    CrossRef  Google Scholar 

  19. Reynolds, S.M., Kaell, L., Riffle, M.E., Bilmes, J.A., Noble, W.S.: Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks. Plos Computational Biology 4(11) (2008)

    Google Scholar 

  20. Rost, B., Fariselli, P., Casadio, R.: Topology prediction for helical transmembrane proteins at 86% accuracy. Protein Science 5(8), 1704–1718 (1996)

    CrossRef  Google Scholar 

  21. Tusnady, G.E., Simon, I.: The HMMTOP transmembrane topology prediction server. Bioinformatics 17(9), 849–850 (2001)

    CrossRef  Google Scholar 

  22. Viklund, H., Bernsel, A., Skwark, M., Elofsson, A.: SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology. Bioinformatics 24(24), 2928–2929 (2008)

    CrossRef  Google Scholar 

  23. White, S.H., von Heijne, G.: How translocons select transmembrane helices. Annual Review of Biophysics 37, 23–42 (2008)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. LERIA, 2 Boulevard Lavoisier, 49045, Angers, France

    Sami Laroum, Béatrice Duval & Jin-Kao Hao

  2. UR 1268 Biopolymères Interactions Assemblages, INRA, 44300, Nantes, France

    Dominique Tessier

Authors
  1. Sami Laroum
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  2. Béatrice Duval
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  3. Dominique Tessier
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  4. Jin-Kao Hao
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Editor information

Editors and Affiliations

  1. Institute of Medical Science, University of Tokyo, 4-6-1, Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan

    Tetsuo Shibuya

  2. Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, 113-8654, Bunkyo-ku, Tokyo, Japan

    Hisashi Kashima

  3. Department of Comouter Science, Tokyo Institute of Technology, 2-12-1 Ookayamama, 152-8550, Meguro-ku, Tokyo, Japan

    Jun Sese

  4. Bioinformatics Project, National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, 567-0085, Suita, Osaka, Japan

    Shandar Ahmad

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© 2012 Springer-Verlag Berlin Heidelberg

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Laroum, S., Duval, B., Tessier, D., Hao, JK. (2012). A Genetic Algorithm for Scale-Based Translocon Simulation. In: Shibuya, T., Kashima, H., Sese, J., Ahmad, S. (eds) Pattern Recognition in Bioinformatics. PRIB 2012. Lecture Notes in Computer Science(), vol 7632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34123-6_3

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  • DOI: https://doi.org/10.1007/978-3-642-34123-6_3

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  • Print ISBN: 978-3-642-34122-9

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