Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ∼85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology.

Using a large-scale "genomic enzymology" approach, we (i) assigned novel ATP-dependent four-carbon acid sugar kinase functions to members of the DUF1537 protein family (domain of unknown function; Pfam families PF07005 and PF17042) and (ii) discovered novel catabolic pathways for d-threonate, l-threonate, and d-erythronate. The experimentally determined ligand specificities of several solute binding proteins (SBPs) for TRAP (tripartite ATP-independent permease) transporters for four-carbon acids, including d-erythronate and l-erythronate, were used to constrain the substrates for the catabolic pathways that degrade the SBP ligands to intermediates in central carbon metabolism. Sequence similarity networks and genome neighborhood networks were used to identify the enzyme components of the pathways. Conserved genome neighborhoods encoded SBPs as well as permease components of the TRAP transporters, members of the DUF1537 family, and a member of the 4-hydroxy-l-threonine 4-phosphate dehydrogenase (PdxA) oxidative decarboxylase, class II aldolase, or ribulose 1,5-bisphosphate carboxylase/oxygenase, large subunit (RuBisCO) superfamily. Because the characterized substrates of members of the PdxA, class II aldolase, and RuBisCO superfamilies are phosphorylated, we postulated that the members of the DUF1537 family are novel ATP-dependent kinases that participate in catabolic pathways for four-carbon acid sugars. We determined that (i) the DUF1537/PdxA pair participates in a pathway for the conversion of d-threonate to dihydroxyacetone phosphate and CO2 and (ii) the DUF1537/class II aldolase pair participates in pathways for the conversion of d-erythronate and l-threonate (epimers at carbon-3) to dihydroxyacetone phosphate and CO2 The physiological importance of these pathways was demonstrated in vivo by phenotypic and genetic analyses.

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology.