title: Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.
abstract: We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/.
authors.affiliation: Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, 2800 Lyngby, Denmark. krogh@cbs.dtu.dk
authors.forename: A
authors.initials: A
authors.lastname: Krogh
authors.forename: B
authors.initials: B
authors.lastname: Larsson
authors.forename: G
authors.initials: G
authors.lastname: von Heijne
authors.forename: E L
authors.initials: EL
authors.lastname: Sonnhammer
mesh_terms: D000818:Animals; D001426:Bacterial Proteins / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D019992:Databases as Topic; D005656:Fungal Proteins / Q000737:chemistry; D016678:Genome; D020407:Internet; D008390:Markov Chains; D008565:Membrane Proteins / Q000737:chemistry*; D010940:Plant Proteins / Q000737:chemistry; D018272:Porins / Q000737:chemistry; D021382:Protein Sorting Signals; D017433:Protein Structure, Secondary; D015203:Reproducibility of Results; D012107:Research Design; D012680:Sensitivity and Specificity; D012984:Software; D012995:Solubility
chemical_list: D001426:Bacterial Proteins; D005656:Fungal Proteins; D008565:Membrane Proteins; D010940:Plant Proteins; D018272:Porins; D021382:Protein Sorting Signals