Accuracy in Predicting Secondary Structure of Ionic Channels

  • Bogumil Konopka
  • Witold Dyrka
  • Jean-Christophe Nebel
  • Malgorzata Kotulska
Part of the Studies in Computational Intelligence book series (SCI, volume 244)

Abstract

Ionic channels are among the most difficult proteins for experimental structure determining, very few of them has been resolved. Bioinformatical tools has not been tested for this specific protein group. In the paper, prediction quality of ionic channel secondary structure is evaluated. The tests were carried out with general protein predictors and predictors only for transmembrane segments. The predictor performance was measured by the accuracy per residue Q and per segment SOV. The evaluation comparing ionic channels and other transmembrane proteins shows that ionic channels are only slightly more difficult objects for modeling than transmembrane proteins; the modeling quality is comparable with a general set of all proteins. Prediction quality showed dependence on the ratio of secondary structures in the ionic channel. Surprisingly, general purpose PSIPRED predictor outperformed other general but also dedicated transmembrane predictors under evaluation.

Keywords

ionic channel secondary structure prediction 

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References

  1. 1.
    Chen, C.P., Kernytsky, A., Rost, B.: Transmembrane helix predictions revisited. Protein Sci. 11(12), 2774–2791 (2002)CrossRefGoogle Scholar
  2. 2.
    Cuthbertson, J.M., Doyle, D.A., Sansom, M.S.: Transmembrane helix prediction: a comparative evaluation and analysis. Protein Eng. Des. Sel. 18(6), 295–308 (2005)CrossRefGoogle Scholar
  3. 3.
    Punta, M., Forrest, L.R., Bigelow, H., Kernytsky, A., Liu, J., Rost, B.: Membrane protein prediction methods. Methods 41(4), 460–474 (2007)CrossRefGoogle Scholar
  4. 4.
    Koh, I.Y., Eyrich, V.A., Marti-Renom, M.A., Przybylski, D., Madhusudhan, M.S., Eswar, N., Graña, O., Pazos, F., Valencia, A., Sali, A., Rost, B.E.: Evaluation of protein structure prediction servers. Nucleic Acids Res. 31(13), 3311–3315 (2003)CrossRefGoogle Scholar
  5. 5.
    Chou, P.Y., Fasman, G.D.: Prediction of protein conformation. Biochemistry 13(2), 222–245 (1974)CrossRefGoogle Scholar
  6. 6.
    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, vol. 28, pp. 235–242 (2002)Google Scholar
  7. 7.
    Jayasinghe, S., et al.: A database of membrane protein topology. Protein Sci. 10(2), 455–458 (2001)CrossRefGoogle Scholar
  8. 8.
    Membrane Proteins of Known Structure, http://blanco.biomol.uci.edu/Membrane_Proteins_xtal.html
  9. 9.
    EVA: Evaluation of automatic structure prediction servers, http://cubic.bioc.columbia.edu/eva/
  10. 10.
    Brünger, A.T., Free, R.: value: cross-validation in crystallography. Methods Enzymol. 277, 366–396 (1997)CrossRefGoogle Scholar
  11. 11.
    Tusnady, G.E., Dosztanyi, Z., Simon, I.: PDB_TM: selection and membrane localization of transmembrane proteins in the protein data bank. Nucleic Acids Res. 33(Database issue), 275–278 (2005)CrossRefGoogle Scholar
  12. 12.
    PDBTM: Protein Data Bank of Transmembrane Proteins, http://pdbtm.enzim.hu/
  13. 13.
    Rost, B., Sander, C.: Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 232(2), 584–599 (1993)CrossRefGoogle Scholar
  14. 14.
    Zemla, A., Venclovas, C., Fidelis, K., Rost, B.: A modified definition of Sov, a segment-based measure for protein secondary structure prediction assessment. Proteins 34(2), 220–223 (1999)CrossRefGoogle Scholar
  15. 15.
    Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997)CrossRefGoogle Scholar
  16. 16.
    Jones, D.T.: Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999)CrossRefGoogle Scholar
  17. 17.
    Cuff, J.A., Barton, G.J.: Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 40(3), 502–511 (2000)CrossRefGoogle Scholar
  18. 18.
    Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge (1998)MATHGoogle Scholar
  19. 19.
    Montgomerie, S., Sundararaj, S., Gallin, W.J., Wishart, D.S.: Improving the accuracy of protein secondary structure prediction using structural alignment. BMC Bioinformatics 7, 301 (2006)CrossRefGoogle Scholar
  20. 20.
    Tusnady, G.E., Simon, I.: Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J. Mol. Biol. 283(2), 489–506 (1998)CrossRefGoogle Scholar
  21. 21.
  22. 22.
    Krogh, A., Larsson, B., von Heijne, G., Sonnhammer, E.L.: Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305(3), 567–580 (2001)CrossRefGoogle Scholar
  23. 23.
  24. 24.
    Rost, B., Casadio, R., Fariselli, P., Sander, C.: Transmembrane helices predicted at 95% accuracy. Protein Sci. 4(3), 521–533 (1995)CrossRefGoogle Scholar
  25. 25.
    PredictProtein - Structure Prediction and Sequence Analysis, http://www.predictprotein.org/
  26. 26.
    Cserzo, M., Wallin, E., Simon, I., von Heijne, G., Elofsson, A.: Prediction of transmembrane alpha-helices in procariotic membrane proteins: the Dense Alignment Surface method. Prot. Eng. 10, 673–676 (1997)CrossRefGoogle Scholar
  27. 27.
  28. 28.
    Arai, M., Mitsuke, H., Ikeda, M., Xia, J.X., Kikuchi, T., Satake, M., Shimizu, T.: ConPred II: a consensus prediction method for obtaining transmembrane topology models with high reliability. Nucleic Acids Res 32(Web Server issue), W390–W393 (2004)CrossRefGoogle Scholar
  29. 29.
  30. 30.
    Tusnady, G.E., Dosztanyi, Z., Simon, I.: TMDET: web server for detecting transmembrane regions of proteins by using their 3D coordinates. Bioinformatics 21(7), 1276–1277 (2005)CrossRefGoogle Scholar
  31. 31.
    Clayton, G.M., Altieri, S., Heginbotham, L., Unger, V.M., Morais-Cabral, J.H.: Structure of the transmembrane regions of a bacterial cyclic nucleotide-regulated channel. Proc. Natl. Acad. Sci. USA 105, 1511–1515 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Bogumil Konopka
    • 1
  • Witold Dyrka
    • 1
  • Jean-Christophe Nebel
    • 2
  • Malgorzata Kotulska
    • 1
  1. 1.Institute of Biomedical Engineering and InstrumentationWroclaw University of TechnologyWroclawPoland
  2. 2.Faculty of Computing, Information Systems & Mathematics, Kingston Upon ThamesKingston UniversitySurreyUnited Kingdom

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