Trends in Biotechnology
Research FocusEnhanced functional information from predicted protein networks
Section snippets
Utility of protein-interaction networks
Several experimental techniques have been used to derive protein-interaction networks for yeast and Helicobacter pylori 8, 9, 10 and these networks exhibit a specific topology and functional modularity 2, 11. The interactions between complexes in specific pathways are highlighted and many previously uncharacterized proteins can be associated with known pathways. Other features of the networks are interesting for biologists, including the observation that highly connected proteins in the yeast
Prediction of protein networks
A number methods based on evolutionary and/or contextual sequence information have been developed to predict protein–protein interaction and functional relationship networks in novel genomes 5, 6, 13, 14, 15, 16. Contextual methods include examining patterns of domain fusion across genomes, operon association and gene-order analysis 5, 6. Evolutionary methods include experimental similarity methods (i.e. the identification of pairs of proteins encoded by a target genome similar to pairs of
Functional annotation using protein-context networks
Several methods have been described for providing functional annotation for uncharacterized proteins using protein networks 2, 6, 11, 17. Function prediction based on protein-interaction networks assumes that interacting proteins are likely to share similar functions. The ‘majority rule’ method annotates a protein by surveying the functions of all the proteins predicted to interact with it and choosing the most frequently occurring function [2]. A more sophisticated method designed for use on
Network comparison
Figure 1a shows the largest predicted E. coli network generated using Date and Marcotte's phylogenetic profile linkages [Date–Marcotte (DM) network] consisting of 1751 proteins and 12 874 linkages [4, supplementary information]. Figure 1b is the E. coli network predicted by the Bioverse (511 proteins; 4075 interactions), based on similarity to experimentally derived interactions. Proteins are colored by broad gene ontology (GO) [19] categories and the 220 proteins shared by both networks are
Implications of network-based functional annotation
Date and Marcotte described a technique for predicting genomic-scale protein networks based on evolutionary information and they have used it to elucidate novel, uncharacterized pathways from genomes. In prokaryotes, these networks provide more coverage than networks predicted by similarity to experimentally determined interactions but the similarity-derived network contains 291 proteins not included in the DM network. In addition the functional resolution of the DM networks is less specific
Acknowledgements
We acknowledge support from the following sources: a Searle Scholar Award (to R.S.), NSF Grant DBI-0217241 and the University of Washington's Advanced Technology Initiative in Infectious Diseases.
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