Reference - PMID:11152613 - Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.
Reference summary
- PubMed ID
- PMID:11152613
- Title
- Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.
- Authors
- Krogh A, Larsson B, von Heijne G, Sonnhammer EL
- Citation
- J Mol Biol 2001 Jan 19;305(3):567-80
- Publication year
- 2001
- 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/.
