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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.