Wavelet change-point prediction of transmembrane proteins

MOTIVATION A non-parametric method, based on a wavelet data-dependent threshold technique for change-point analysis, is applied to predict location and topology of helices in transmembrane proteins. A new propensity scale generated from a transmembrane helix database is proposed. RESULTS We show that wavelet change-point performs well for smoothing hydropathy and transmembrane profiles generated using different scales. We investigate which wavelet bases and threshold functions are overall most appropriate to detect transmembrane segments. Prediction accuracy is based on the analysis of two data sets used as standard benchmarks for transmembrane prediction algorithms. The analysis of a test set of 83 proteins results in accuracy per segment equal to 98.2%; the analysis of a 48 proteins blind-test set, i.e. containing proteins not used to generate the propensity scales, results in accuracy per segment equal to 97.4%. We believe that this method can also be applied to the detection of boundaries of other patterns such as G + Cisochores and dot-plots. AVAILABILITY The transmembrane database, TMALN and source code are available upon request from the authors.

[1]  P Argos,et al.  A conformational preference parameter to predict helices in integral membrane proteins. , 1986, Biochimica et biophysica acta.

[2]  David C. Jones,et al.  Using evolutionary trees in protein secondary structure prediction and other comparative sequence analyses. , 1996, Journal of molecular biology.

[3]  B. Rost,et al.  Topology prediction for helical transmembrane proteins at 86% accuracy–Topology prediction at 86% accuracy , 1996, Protein science : a publication of the Protein Society.

[4]  P. Ponnuswamy.  Hydrophobic characteristics of folded proteins. , 1993, Progress in biophysics and molecular biology.

[5]  P. Lio’,et al.  Using protein structural information in evolutionary inference: transmembrane proteins. , 1999, Molecular biology and evolution.

[6]  Satoru Kuhara,et al.  The hydrophobic cores of proteins predicted by wavelet analysis , 1999, Bioinform..

[7]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[8]  G von Heijne,et al.  The distribution of charged amino acids in mitochondrial inner-membrane proteins suggests different modes of membrane integration for nuclearly and mitochondrially encoded proteins. , 1992, European journal of biochemistry.

[9]  A Elofsson,et al.  Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method. , 1997, Protein engineering.

[10]  Kay Hofmann,et al.  Tmbase-A database of membrane spanning protein segments , 1993 .

[11]  W R Taylor,et al.  A model recognition approach to the prediction of all-helical membrane protein structure and topology. , 1994, Biochemistry.

[12]  Marc Raimondo,et al.  Minimax estimation of sharp change points , 1998 .

[13]  Erik L. L. Sonnhammer,et al.  A Hidden Markov Model for Predicting Transmembrane Helices in Protein Sequences , 1998, ISMB.

[14]  I. Johnstone,et al.  Minimax estimation via wavelet shrinkage , 1998 .

[15]  E. Parzen,et al.  Data dependent wavelet thresholding in nonparametric regression with change-point applications , 1996 .

[16]  G von Heijne,et al.  Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. , 1992, Journal of molecular biology.

[17]  R. Doolittle,et al.  A simple method for displaying the hydropathic character of a protein. , 1982, Journal of molecular biology.

[19]  B. Rost,et al.  Transmembrane helices predicted at 95% accuracy , 1995, Protein science : a publication of the Protein Society.

[20]  R. Hodges,et al.  New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. , 1986, Biochemistry.

[21]  T. Steitz,et al.  Identifying nonpolar transbilayer helices in amino acid sequences of membrane proteins. , 1986, Annual review of biophysics and biophysical chemistry.

[22]  Yazhen Wang.  Jump and sharp cusp detection by wavelets , 1995 .

[23]  Davor Juretic,et al.  The Preference Functions Method for Predicting Protein Helical Turns with Membrane Propensity , 1998, J. Chem. Inf. Comput. Sci..

[24]  Emanuel Parzen,et al.  Change-point approach to data analytic wavelet thresholding , 1996, Stat. Comput..

[25]  I. Johnstone,et al.  Wavelet Shrinkage: Asymptopia? , 1995 .

[26]  G. Tusnády,et al.  Principles governing amino acid composition of integral membrane proteins: application to topology prediction. , 1998, Journal of molecular biology.

[27]  Shigeki Mitaku,et al.  SOSUI: classification and secondary structure prediction system for membrane proteins , 1998, Bioinform..

[28]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Bernard W. Silverman,et al.  The discrete wavelet transform in S , 1994 .