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Elife. 2014 Jun 30;3. doi: 10.7554/eLife.03275.

Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks.

Author information

1
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, United States.
2
Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, United States.
3
Department of Biochemistry, Albert Einstein College of Medicine, New York, United States.
4
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States.

Abstract

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.

KEYWORDS:

biochemistry; functional assignment; genome neighborhood network; sequence similarity network

PMID:
24980702
PMCID:
PMC4113996
DOI:
10.7554/eLife.03275
[Indexed for MEDLINE]
Free PMC Article

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