alexa Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Proteomics & Bioinformatics

Author(s): Krogh A, Larsson B, von Heijne G, Sonnhammer EL

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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/. Copyright 2001 Academic Press. This article was published in J Mol Biol and referenced in Journal of Proteomics & Bioinformatics

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