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.
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References
Chen, C.P., Kernytsky, A., Rost, B.: Transmembrane helix predictions revisited. Protein Sci. 11(12), 2774–2791 (2002)
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)
Punta, M., Forrest, L.R., Bigelow, H., Kernytsky, A., Liu, J., Rost, B.: Membrane protein prediction methods. Methods 41(4), 460–474 (2007)
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)
Chou, P.Y., Fasman, G.D.: Prediction of protein conformation. Biochemistry 13(2), 222–245 (1974)
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)
Jayasinghe, S., et al.: A database of membrane protein topology. Protein Sci. 10(2), 455–458 (2001)
Membrane Proteins of Known Structure, http://blanco.biomol.uci.edu/Membrane_Proteins_xtal.html
EVA: Evaluation of automatic structure prediction servers, http://cubic.bioc.columbia.edu/eva/
Brünger, A.T., Free, R.: value: cross-validation in crystallography. Methods Enzymol. 277, 366–396 (1997)
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)
PDBTM: Protein Data Bank of Transmembrane Proteins, http://pdbtm.enzim.hu/
Rost, B., Sander, C.: Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 232(2), 584–599 (1993)
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)
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)
Jones, D.T.: Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999)
Cuff, J.A., Barton, G.J.: Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 40(3), 502–511 (2000)
Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge (1998)
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)
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)
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)
TMHMM server, v. 2.0, http://www.cbs.dtu.dk/services/TMHMM/
Rost, B., Casadio, R., Fariselli, P., Sander, C.: Transmembrane helices predicted at 95% accuracy. Protein Sci. 4(3), 521–533 (1995)
PredictProtein - Structure Prediction and Sequence Analysis, http://www.predictprotein.org/
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)
DAS-TMfilter server, http://mendel.imp.ac.at/sat/DAS/DAS.html
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)
Conpred II, http://bioinfo.si.hirosaki-u.ac.jp/~ConPred2/
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)
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)
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Konopka, B., Dyrka, W., Nebel, JC., Kotulska, M. (2009). Accuracy in Predicting Secondary Structure of Ionic Channels. In: Nguyen, N.T., Katarzyniak, R.P., Janiak, A. (eds) New Challenges in Computational Collective Intelligence. Studies in Computational Intelligence, vol 244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03958-4_27
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DOI: https://doi.org/10.1007/978-3-642-03958-4_27
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