title: Characterization and prediction of protein nucleolar localization sequences.
abstract: Although the nucleolar localization of proteins is often believed to be mediated primarily by non-specific retention to core nucleolar components, many examples of short nucleolar targeting sequences have been reported in recent years. In this article, 46 human nucleolar localization sequences (NoLSs) were collated from the literature and subjected to statistical analysis. Of the residues in these NoLSs 48% are basic, whereas 99% of the residues are predicted to be solvent-accessible with 42% in α-helix and 57% in coil. The sequence and predicted protein secondary structure of the 46 NoLSs were used to train an artificial neural network to identify NoLSs. At a true positive rate of 54%, the predictor's overall false positive rate (FPR) is estimated to be 1.52%, which can be broken down to FPRs of 0.26% for randomly chosen cytoplasmic sequences, 0.80% for randomly chosen nucleoplasmic sequences and 12% for nuclear localization signals. The predictor was used to predict NoLSs in the complete human proteome and 10 of the highest scoring previously unknown NoLSs were experimentally confirmed. NoLSs are a prevalent type of targeting motif that is distinct from nuclear localization signals and that can be computationally predicted.
authors.affiliation: Division of Biological Chemistry and Drug Discovery and Wellcome Trust Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK. michelle@compbio.dundee.ac.uk
authors.forename: Michelle S
authors.initials: MS
authors.lastname: Scott
authors.forename: François-Michel
authors.initials: FM
authors.lastname: Boisvert
authors.forename: Mark D
authors.initials: MD
authors.lastname: McDowall
authors.forename: Angus I
authors.initials: AI
authors.lastname: Lamond
authors.forename: Geoffrey J
authors.initials: GJ
authors.lastname: Barton
mesh_terms: D045744:Cell Line, Tumor; D002466:Cell Nucleolus / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods; D006801:Humans; D016571:Neural Networks, Computer; D019913:Nuclear Localization Signals; D009687:Nuclear Proteins / Q000032:analysis / Q000737:chemistry*; D021382:Protein Sorting Signals; D014764:Viral Proteins / Q000032:analysis / Q000737:chemistry
chemical_list: D019913:Nuclear Localization Signals; D009687:Nuclear Proteins; D021382:Protein Sorting Signals; D014764:Viral Proteins