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

      Journal of Molecular Biology

      Bacterial Proteins, chemistry, Computational Biology, methods, Databases as Topic, Fungal Proteins, Genome, Internet, Markov Chains, Membrane Proteins, Solubility, Plant Proteins, Porins, Protein Sorting Signals, Protein Structure, Secondary, Reproducibility of Results, Research Design, Sensitivity and Specificity, Software, Animals

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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.

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          Journal
          11152613
          10.1006/jmbi.2000.4315

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