Predicting protein cellular localization using a domain projection method

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Item Type:	Article
Title:	Predicting protein cellular localization using a domain projection method
Creators Name:	Mott, R., Schultz, J., Bork, P. and Ponting, C.P.
Abstract:	We investigate the co-occurrence of domain families in eukaryotic proteins to predict protein cellular localization. Approximately half (300) of SMART domains form a “small-world network”, linked by no more than seven degrees of separation. Projection of the domains onto two-dimensional space reveals three clusters that correspond to cellular compartments containing secreted, cytoplasmic, and nuclear proteins. The projection method takes into account the existence of “bridging” domains, that is, instances where two domains might not occur with each other but frequently co-occur with a third domain; in such circumstances the domains are neighbors in the projection. While the majority of domains are specific to a compartment (“locale”), and hence may be used to localize any protein that contains such a domain, a small subset of domains either are present in multiple locales or occur in transmembrane proteins. Comparison with previously annotated proteins shows that SMART domain data used with this approach can predict, with 92% accuracy, the localizations of 23% of eukaryotic proteins. The coverage and accuracy will increase with improvements in domain database coverage. This method is complementary to approaches that use amino-acid composition or identify sorting sequences; these methods may be combined to further enhance prediction accuracy.
Keywords:	Benchmarking, Cell Compartmentation, Cluster Analysis, Computational Biology, Cytoplasm, Internet, Membrane Proteins, Nuclear Proteins, Probability, Protein Databases, Protein Sorting Signals, Proteins, Software, Tertiary Protein Structure, Animals
Source:	Genome Research
ISSN:	1088-9051
Publisher:	Cold Spring Harbor Laboratory Press
Volume:	12
Number:	8
Page Range:	1168-1174
Date:	August 2002
Official Publication:	https://doi.org/10.1101/gr.96802
PubMed:	View item in PubMed

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