Trends in Biotechnology
Volume 22, Issue 2, February 2004, Pages 60-62
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Enhanced functional information from predicted protein networks

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Abstract

Experimentally derived genome-wide protein interaction networks have been useful in the elucidation of functional information that is not evident from examining individual proteins but determination of these networks is complex and time consuming. To address this problem, several computational methods for predicting protein networks in novel genomes have been developed. A recent publication by Date and Marcotte describes the use of phylogenetic profiling for elucidating novel pathways in proteomes that have not been experimentally characterized. This method, in combination with other computational methods for generating protein-interaction networks, might help identify novel functional pathways and enhance functional annotation of individual proteins.

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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