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.
Chapter PDF
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, 1st edn. Addison-Wesley (January 1989)
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)
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)
Jones, D.T.: Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics 23(5), 538–544 (2007)
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)
Junker, V.L., Apweiler, R., Bairoch, A.: Representation of functional information in the SWISS-PROT data bank. Bioinformatics 15(12), 1066–1067 (1999)
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)
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)
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)
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)
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)
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)
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)
Rost, B., Fariselli, P., Casadio, R.: Topology prediction for helical transmembrane proteins at 86% accuracy. Protein Science 5(8), 1704–1718 (1996)
Tusnady, G.E., Simon, I.: The HMMTOP transmembrane topology prediction server. Bioinformatics 17(9), 849–850 (2001)
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)
White, S.H., von Heijne, G.: How translocons select transmembrane helices. Annual Review of Biophysics 37, 23–42 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-34123-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34122-9
Online ISBN: 978-3-642-34123-6
eBook Packages: Computer ScienceComputer Science (R0)