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PMID: 12634065 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

Reliability measures for membrane protein topology prediction algorithms.

Journal of molecular biology ·Vol. 327 ·No. 3 ·2003-03-28 ·Pages 735-44

Melén K, Krogh A, von Heijne G

Abstract

We have developed reliability scores for five widely used membrane protein topology prediction methods, and have applied them both on a test set of 92 bacterial plasma membrane proteins with experimentally determined topologies and on all predicted helix bundle membrane proteins in three fully sequenced genomes: Escherichia coli, Saccharomyces cerevisiae and Caenorhabditis elegans. We show that the reliability scores work well for the TMHMM and MEMSAT methods, and that they allow the probability that the predicted topology is correct to be estimated for any protein. We further show that the available test set is biased towards high-scoring proteins when compared to the genome-wide data sets, and provide estimates for the expected prediction accuracy of TMHMM across the three genomes. Finally, we show that the performance of TMHMM is considerably better when limited experimental information (such as the in/out location of a protein's C terminus) is available, and estimate that at least ten percentage points in overall accuracy in whole-genome predictions can be gained in this way.

MeSH Terms
Algorithms Animals Caenorhabditis elegans/metabolism Cell Membrane/metabolism Computational Biology/methods Databases as Topic Escherichia coli/metabolism Protein Conformation Protein Structure, Tertiary Proteome Reproducibility of Results Saccharomyces cerevisiae/metabolism Software
Chemicals
Proteome
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Melén Karin
Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, Stockholm University, SE-106 91 Stockholm, Sweden.
Krogh Anders
von Heijne Gunnar
Article Info
Journal
Journal of molecular biology
Abbr.
J Mol Biol
ISSN
0022-2836
Published
2003-03-28
Pages
735-44
Language
English
Region
England
NLM ID
2985088R
Subset
IM
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