Predicting nuclear localization.
Nuclear localization of proteins is a crucial element in the dynamic life of the cell. It is complicated by the massive diversity of targeting signals and the existence of proteins that shuttle between the nucleus and cytoplasm. Nevertheless, a majority of subcellular localization tools that predict nuclear proteins have been developed without involving dual localized proteins in the data sets. Hence, in general, the existing models are focused on predicting statically nuclear proteins, rather than nuclear localization itself. We present an independent analysis of existing nuclear localization predictors, using a nonredundant data set extracted from Swiss-Prot R50.0. We demonstrate that accuracy on truly novel proteins is lower than that of previous estimations, and that existing models generalize poorly to dual localized proteins. We have developed a model trained to identify nuclear proteins including dual localized proteins. The results suggest that using more recent data and including dual localized proteins improves the overall prediction. The final predictor NUCLEO operates with a realistic success rate of 0.70 and a correlation coefficient of 0.38, as established on the independent test set. (NUCLEO is available at: http://pprowler.itee.uq.edu.au.).
Hawkins
John
J
ARC Centre for Complex Systems, School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Australia. jhawkins@itee.uq.edu.au
Davis
Lynne
L
Bodén
Mikael
M
D002467:Cell Nucleus / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D019913:Nuclear Localization Signals / Q000737:chemistry*; D009687:Nuclear Proteins / Q000032:analysis*; D020539:Sequence Analysis, Protein / Q000379:methods*
D019913:Nuclear Localization Signals; D009687:Nuclear Proteins