MANET

MANET reconstructs evolutionary histories of enzyme structural domains within metabolic networks by integrating KEGG, SCOP, PDBsum data and structural phylogenomics to trace enzyme recruitment and network evolution.

Key Features:

• Data integration: Version 3.0 aggregates KEGG and SCOP data and links enzyme entries to PDBsum while using SCOP fold family classification to group structurally similar domains.
• Hierarchical network representation: Enzymatic activities are organized into hierarchical systems of subnetworks and mesonetworks aligned with KEGG classifications and represented as bipartite networks and one-mode projections at enzyme, subnetwork, and mesonetwork levels.
• Structural phylogenomics: Structural phylogenomic reconstruction is applied to infer the deep history of enzyme structural domains within metabolic pathways.
• Network analyses: Network properties including scale-freeness, randomness, and small-world characteristics are quantified to characterize metabolic network architecture.
• Evolutionary pattern detection: The resource detects enzyme sharing across mesonetwork boundaries and patterns consistent with the patchwork model of metabolic evolution.
• Hierarchical modularity and granularity: MANET documents increases in hierarchical modularity and scale-free behavior over evolutionary time, with stronger constraints at lower levels of metabolic organization (principle of granularity).

Scientific Applications:

• Enzyme recruitment analysis: Tracing the ancestry of enzyme structural domains to study recruitment events that shaped modern metabolism.
• Metabolic module evolution: Examining growth patterns and temporal diversification of subnetworks and mesonetworks to investigate module innovation.
• Comparative evolutionary studies: Comparing network and domain histories across lineages and superkingdoms to infer broad evolutionary trends in metabolism.

Methodology:

MANET integrates KEGG, SCOP (fold family level), and PDBsum data, applies structural phylogenomics to reconstruct domain ancestries, maps enzymatic activities into bipartite networks and their one-mode projections at enzyme, subnetwork, and mesonetwork levels, and analyzes network properties such as scale-freeness, randomness, small-world characteristics, and hierarchical modularity over evolutionary time.