Characterization and prediction of protein nucleolar localization sequences

Michelle S. Scott (Lead / Corresponding author), Francois-Michel Boisvert, Mark D. McDowall, Angus I. Lamond, Geoffrey J. Barton

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    91 Citations (Scopus)

    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 alpha-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.

    Original languageEnglish
    Pages (from-to)7388-7399
    Number of pages12
    JournalNucleic Acids Research
    Volume38
    Issue number21
    DOIs
    Publication statusPublished - Nov 2010

    Keywords

    • TARGETING SEQUENCES
    • SIGNAL PEPTIDE
    • NUCLEAR EXPORT
    • MESSENGER-RNA
    • VIRUS TYPE-1
    • DATABASE
    • COMPLEX
    • BINDING
    • IDENTIFICATION
    • DOMAIN

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