Biopolym. Cell. 2017; 33(2):147-154.
http://dx.doi.org/10.7124/bc.00094C
Bioinformatics
Comparative analysis of nuclear localization signal (NLS) prediction methods
1Lisitsyna O. M., 2Seplyarskiy V. B., 1, 3Sheval E. V.
  1. A. N. Belozersky Institute of Physico-Chemical Biology, M. V. Lomonosov Moscow State University
    Leninskie gory, house 1, building 40, Moscow, Russian Federation, 119992
  2. A. A. Kharkevich Institute for Information Transmission Problems,
    19/1, Bolshoy Karetny per. Moscow, Russian Federation, 127051the Russian Academy of Sciences (Kharkevich Institute), Bolshoi Karetny pereulok 19, 127994 Moscow, Russia.
  3. LIA 1066 French-Russian Joint Cancer Research Laboratory
    Villejuif, France–Moscow, Russian Federation

Abstract

Aim. Comparative analysis of six state-of-the-art nuclear localization signal (NLS) prediction methods (PSORT II, NucPred, cNLSMapper, NLStradamus, NucImport and seqNLS). Methods. Each program was tested for correct predictions using a dataset of 155 experimentally determined NLSs and for false-positives using a dataset of 155 transmembrane proteins, which putatively lack NLS. Results. The most suitable NLS predictors wer fond to be NucPred, NLStradamus and seqNLS; these programs provide the maximum rate of correct to wrong predictions among the tested programs. However, the best results obtained by these programs were only ~ 45 % of the correct predictions. Conclusion. The identification of novel NLSs by predictors still requires experimental verification.
Keywords: nuclear localization signal, prediction
Full text: (PDF, in English)

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