Abstract

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.

Keywords

Hidden Markov modelSIGNAL (programming language)Markov chainSignal peptideCleavage (geology)Markov modelComputer scienceBiological systemSimple (philosophy)Pattern recognition (psychology)Artificial intelligenceAlgorithmMachine learningBiologyPeptide sequenceGenetics

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Year
1998
Type
article
Volume
6
Pages
122-30
Citations
528
Access
Closed

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Henrik Nielsen, Anders Krogh (1998). Prediction of signal peptides and signal anchors by a hidden Markov model.. PubMed , 6 , 122-30.