title: Wavelet change-point prediction of transmembrane proteins.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The transmembrane database, TMALN and source code are available upon request from the authors.
authors.affiliation: Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK. plio@hgmp.mrc.ac.uk
authors.forename: P
authors.initials: P
authors.lastname: Lio
authors.forename: M
authors.initials: M
authors.lastname: Vannucci
mesh_terms: D001426:Bacterial Proteins / Q000032:analysis; D006801:Humans; D008565:Membrane Proteins / Q000032:analysis*; D019713:Receptors, CCR5 / Q000032:analysis
chemical_list: D001426:Bacterial Proteins; D008565:Membrane Proteins; D019713:Receptors, CCR5