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Gene function prediction and annotation of Escherichia coli K12 proteins using physical protein-protein interactions, genomic context methods and machine learning algorithms
Javier Diaz (Jdime) - Jun 29, 2009
Published paper (Hu et al. 2009) 
eNet is a database of gene function prediction in Escherichia coli K12. It exploits the topological properties of physical and functional protein interaction networks integrated via a machine learning algorithm explicitly developed for eNet. eNet provides specific protein functional prediction 'labels' using ontology resources (e.g. GO, MultiFun and COGs terms) for hypothesis-driven following-up studies. eNet also provides the underlying actual protein interactions and companion groupings of proteins (e.g. multimeric complexes and functional modules) delimited optimizing the Markov clustering algorithm. All eNet datasets and predictions are freely available and can be searched, browsed and downloaded in eNet dedicated web portal http://ecoli.med.utoronto.ca/ .
Over one-third of the 4,225 protein-coding genes of E. coli K-12 remain functionally unannotated (orphans). Many map to distant clades like Archaea, suggesting involvement in basic prokaryotic traits, while others appear restricted to E. coli, including pathogenic strains. To elucidate biological roles of these orphans, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated genes, while a machine-learning strategy based on network integration implicated the orphans in specific biological processes, such as protein synthesis, amino acid metabolism, biofilm formation, motility, assembly of the bacterial cell envelope, etc. This resource provides a "systems-wide" functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins.
From this website, you can access and download annotations, interactions (proteomic and genomic context-derived) and function predictions for all E. coli proteins analyzed in this study. Also you can explore protein complexes (from proteomic network), functional modules (from genomic context), and functional neighborhoods (from function predictions and preexisting annotations).
For more details you can visit eNet webpage: http://ecoli.med.utoronto.ca/
or take a look at our publication: http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1000096
The following options are available for searching and browsing genes/proteins at eNet:
-Gene Name / B-numbers (Blattner-number)
-Gene Product (current annotation)
-Multiple Gene Names / B-numbers (to work in semi-batch mode)
-Project (according to finished projects at the Emili lab)
-Prediction profile of a gene (according function predictions by Hu. et al 2009)
-Prediction profile for a specific functional category (all genes predicted to belong a functional category by Hu et al 2009)
-Download data (Supplementary material in text and PSI-ML formats, including function predictions, networks, clusters, etc)
-Gene Name / B-numbers (Blattner-number)
See eNet 'Help' section for details
See eNet 'Help' section for examples
- Hu, P et al. (2009) Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins. PLoS Biol. 7 e96 PubMed
Discussion of eNet on other websites
Featured in PortEco Release 1.6.0: http://www.PortEco.org/
Research highlight in Nature Methods (2009): http://www.nature.com/nmeth/journal/v6/n6/full/nmeth0609-402b.html