Journal
ELIFE
Volume 3, Issue -, Pages -Publisher
ELIFE SCIENCES PUBLICATIONS LTD
DOI: 10.7554/eLife.03275
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Funding
- NCI NIH HHS [P30 CA013330] Funding Source: Medline
- NIGMS NIH HHS [U54 GM093342, P01GM071790, P41-GM103311, U54 GM094662, P01 GM071790, U54GM074945, U54GM093342, U54 GM074945, P41 GM103311, U54GM094662] Funding Source: Medline
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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 similar to 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 networks(3) and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.
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