Evaluation of methods for the prediction of membrane spanning regions

MOTIVATION A variety of tools are available to predict the topology of transmembrane proteins. To date no independent evaluation of the performance of these tools has been published. A better understanding of the strengths and weaknesses of the different tools would guide both the biologist and the bioinformatician to make better predictions of membrane protein topology. RESULTS Here we present an evaluation of the performance of the currently best known and most widely used methods for the prediction of transmembrane regions in proteins. Our results show that TMHMM is currently the best performing transmembrane prediction program.

[1]  Rolf Apweiler,et al.  The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000 , 2000, Nucleic Acids Res..

[2]  A J Cuticchia,et al.  TM Finder: A prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales , 2001, Protein science : a publication of the Protein Society.

[3]  Davor Jureti,et al.  Sequence Analysis of Membrane Proteins with the Web Server SPLIT , 1999 .

[4]  Rolf Apweiler,et al.  A collection of well characterised integral membrane proteins , 2000, Bioinform..

[5]  Anders Krogh,et al.  Prediction of Signal Peptides and Signal Anchors by a Hidden Markov Model , 1998, ISMB.

[6]  Burkhard Rost,et al.  Refining Neural Network Predictions for Helical Transmembrane Proteins by Dynamic Programming , 1996, ISMB.

[7]  J. Lolkema,et al.  Membrane Topology and Insertion of Membrane Proteins: Search for Topogenic Signals , 2000, Microbiology and Molecular Biology Reviews.

[8]  Manuel G. Claros,et al.  TopPred II: an improved software for membrane protein structure predictions , 1994, Comput. Appl. Biosci..

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

[10]  David Eisenberg,et al.  The helical hydrophobic moment: a measure of the amphiphilicity of a helix , 1982, Nature.

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

[12]  T. Stevens,et al.  Do more complex organisms have a greater proportion of membrane proteins in their genomes? , 2000, Proteins.

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

[14]  G. Heijne,et al.  Genome‐wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms , 1998, Protein science : a publication of the Protein Society.

[15]  Gert Vriend,et al.  GPCRDB information system for G protein-coupled receptors , 2003, Nucleic Acids Res..

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

[17]  Patrick Argos,et al.  Prediction of Membrane Protein Topology Utilizing Multiple Sequence Alignments , 1997, Journal of protein chemistry.

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

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

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

[21]  A. Krogh,et al.  Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. , 2001, Journal of molecular biology.

[22]  Rolf Apweiler,et al.  The SWISS-PROT protein sequence data bank and its supplement TrEMBL , 1997, Nucleic Acids Res..

[23]  C DeLisi,et al.  The detection and classification of membrane-spanning proteins. , 1985, Biochimica et biophysica acta.

[24]  Gunnar von Heijne,et al.  Topological “frustration” in multispanning E. coli inner membrane proteins , 1994, Cell.

[25]  G von Heijne,et al.  Consensus predictions of membrane protein topology , 2000, FEBS letters.

[26]  H Nielsen,et al.  Machine learning approaches for the prediction of signal peptides and other protein sorting signals. , 1999, Protein engineering.

[27]  M. Kanehisa,et al.  A knowledge base for predicting protein localization sites in eukaryotic cells , 1992, Genomics.

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