Prediction of signal peptides and signal anchors by a hidden Markov model.

Anders Stærmose Krogh, Henrik Nielsen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    A hidden Markov model of signal peptides has been developed. It contains submodels for the N-terminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our data, the length distributions for the three regions are significantly different from expectations. For instance, the assigned hydrophobic region is between 8 and 12 residues long in almost all eukaryotic signal peptides. This analysis also makes obvious the difference between eukaryotes, Gram-positive bacteria, and Gram-negative bacteria. The model can be used to predict the location of the cleavage site, which it finds correctly in nearly 70% of signal peptides in a cross-validated test--almost the same accuracy as the best previous method. One of the problems for existing prediction methods is the poor discrimination between signal peptides and uncleaved signal anchors, but this is substantially improved by the hidden Markov model when expanding it with a very simple signal anchor model.
    Original languageEnglish
    Title of host publication ISMB-98 Proceedings
    Number of pages9
    PublisherAAAI Press
    Publication date1998
    Publication statusPublished - 1998
    SeriesInternational Conference on Intelligent Systems for Molecular Biology. Proceedings


    • bacterial protein
    • signal peptide
    • article
    • artificial intelligence
    • artificial neural network
    • binding site
    • chemical structure
    • chemistry
    • eukaryotic cell
    • factual database
    • genetics
    • probability
    • sequence analysis
    • Artificial Intelligence
    • Bacterial Proteins
    • Binding Sites
    • Databases, Factual
    • Eukaryotic Cells
    • Markov Chains
    • Models, Molecular
    • Neural Networks (Computer)
    • Protein Sorting Signals
    • Sequence Analysis


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