eNet

eNet constructs a systems-level functional atlas of Escherichia coli K-12 to assign functions to previously uncharacterized "orphan" proteins (one-third of the 4,225 protein-coding genes) by integrating proteomic and genomic evidence.

Key Features:

• Extensive Proteomic Survey: Uses affinity-tagged Escherichia coli strains to perform a proteomic survey that systematically probes orphan protein interactions and associations.
• Comprehensive Genomic Context Inferences: Produces a high-confidence compendium including 5,993 putative physical interactions and 74,776 putative functional associations derived from genomic context analyses.
• Probabilistic Network Clustering: Clusters probabilistic interaction networks to identify orphan proteins as members of discrete multiprotein complexes and functional modules alongside annotated gene products.
• Machine-Learning Integration: Applies a machine-learning strategy based on network integration to associate orphan proteins with specific biological processes.
• Experimental Validation: Provides experimental evidence linking orphan proteins to processes including protein synthesis, amino acid metabolism, biofilm formation, motility, and bacterial cell envelope assembly.

Scientific Applications:

• Proteome-wide functional annotation: Assigns functions across the Escherichia coli K-12 proteome, prioritizing previously uncharacterized orphan proteins.
• Evolutionary and strain-specific studies: Illuminates the evolutionary significance of orphan proteins and their potential roles in strain-specific adaptations, including pathogenic strains.
• Microbial physiology and pathogenesis research: Supports investigations into microbial physiology, biofilm formation, motility, metabolism, and bacterial cell envelope assembly.

Methodology:

Affinity-tagged proteomic surveys, genomic context inference, construction and probabilistic clustering of interaction networks, and a machine-learning network-integration strategy.