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OJB©
Online Journal of Bioinformatics©
Volume 6 : 51-64, 2005.
Höglund A, Dönnes P, Adolph HW, Kohlbacher O.
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
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