Predicting nuclear localization.

@article{citeulike:1925152, abstract = {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.).}, address = {ARC Centre for Complex Systems, School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Australia. jhawkins@itee.uq.edu.au}, author = {Hawkins, J. and Davis, L. and Bod\'{e}n, M. }, citeulike-article-id = {1925152}, doi = {10.1021/pr060564n}, issn = {1535-3893}, journal = {J Proteome Res}, keywords = {bioinformatics, localisation, nls, nuclear, prediction}, month = {April}, number = {4}, pages = {1402--1409}, posted-at = {2007-11-16 06:54:44}, priority = {2}, title = {Predicting nuclear localization.}, url = {http://dx.doi.org/10.1021/pr060564n}, volume = {6}, year = {2007} }

TY - JOUR ID - citeulike:1925152 L3 - citeulike-article-id:1925152 TI - Predicting nuclear localization. JF - J Proteome Res VL - 6 IS - 4 SP - 1402 EP - 1409 AD - ARC Centre for Complex Systems, School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Australia. jhawkins@itee.uq.edu.au SN - 1535-3893 N2 - 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.). KW - bioinformatics KW - localisation KW - nls KW - nuclear KW - prediction AU - Hawkins, J AU - Davis, L AU - Bodén, M PY - 2007/04// UR - http://dx.doi.org/10.1021/pr060564n ER -