NLStradamus: a simple Hidden Markov Model for nuclear localization signal prediction.
BACKGROUND
Nuclear localization signals (NLSs) are stretches of residues within a protein that are important for the regulated nuclear import of the protein. Of the many import pathways that exist in yeast, the best characterized is termed the 'classical' NLS pathway. The classical NLS contains specific patterns of basic residues and computational methods have been designed to predict the location of these motifs on proteins. The consensus sequences, or patterns, for the other import pathways are less well-understood.


RESULTS
In this paper, we present an analysis of characterized NLSs in yeast, and find, despite the large number of nuclear import pathways, that NLSs seem to show similar patterns of amino acid residues. We test current prediction methods and observe a low true positive rate. We therefore suggest an approach using hidden Markov models (HMMs) to predict novel NLSs in proteins. We show that our method is able to consistently find 37% of the NLSs with a low false positive rate and that our method retains its true positive rate outside of the yeast data set used for the training parameters.


CONCLUSIONS
Our implementation of this model, NLStradamus, is made available at: (http://www.moseslab.csb.utoronto.ca/NLStradamus/).
Nguyen Ba
Alex N
AN
Department of Cell & Systems Biology, University of Toronto, Toronto, Canada. alex.nguyenba@utoronto.ca
Pogoutse
Anastassia
A
Provart
Nicholas
N
Moses
Alan M
AM
D021581:Active Transport, Cell Nucleus; D008390:Markov Chains; D008962:Models, Theoretical; D019913:Nuclear Localization Signals / Q000737:chemistry*; D012984:Software
D019913:Nuclear Localization Signals