PMID:11152613
| Citation |
Krogh, A, Larsson, B, von Heijne, G and Sonnhammer, EL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305:567-80 |
|---|---|
| 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/. |
| Links |
PubMed Online version:10.1006/jmbi.2000.4315 |
| Keywords |
Animals; Bacterial Proteins/chemistry; Computational Biology/methods; Databases as Topic; Fungal Proteins/chemistry; Genome; Internet; Markov Chains; Membrane Proteins/chemistry; Plant Proteins/chemistry; Porins/chemistry; Protein Sorting Signals; Protein Structure, Secondary; Reproducibility of Results; Research Design; Sensitivity and Specificity; Software; Solubility |
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