Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes

We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of T MHMM's performance, and show that it correctly predicts 97-98% of the trans membrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, a lthough the accuracy drops when signal peptides are present. This high degr ee of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate tha t 20-30% of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins wit h N-in-C-in topologies are strongly preferred in all examined organisms, ex cept Caenorhabditis elegans, where the large number of 7TM receptors increa ses the counts for N-out-C-in topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechani sms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/servi ces/TMHMM/. (C) 2001 Academic Press.