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Online Journal of Bioinformatics©

Volume 6 : 51-64, 2005.

From prediction of subcellular localization to functional classification: Discrimination of DNA-packing and other nuclear proteins


Höglund A, Dönnes P, Adolph HW, Kohlbacher O.


Department(s) of Simulation of Biological Systems, WSI/ZBIT, Eberhard Karls University Tübingen, Sand 14, D-72076 Tübingen and Biochemistry and Center for Bioinformatics, Saarland University, D-66041 Saabrücken, Germany.




Höglund A, Dönnes P, Adolph HW, Kohlbacher O. From prediction of subcellular localization to functional classification: Discrimination of DNA-packing and other nuclear proteins, Onl J Bioinform., 6:51-64, 2005. Subcellular localization is related to protein function. Computational methods have shown that different chemical environments in the cell lead to evolutionary adaptation of amino acid composition for cytoplasmic, extracellular, mitochondrial, and nuclear proteins. In this study, the division of nuclear proteins into functional groups and, the influence of sequence homology in the assessment of prediction accuracy was investigated.  Results showed that histones and histone-like proteins, all involved in DNA-packing in eukaryotic cells could be separated from other proteins with high statistical significance. The proteins are a distinct separate group among the nuclear proteins, extending the classification of subcellular localization with functional annotation. On homology-reduced data the clear separation of proteins from different localizations as reported in previous studies was not found. A nearest neighbour classifier performs even better than a machine learning approach on the original data set. The findings suggest that performance should be evaluated at different levels of sequence homology in order to provide a measure of the robustness of the method.


KEYWORDS: protein, subcellular localization, nuclear protein, machine learning