Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks
- University of California, San Francisco, United States
- University of Illinois at Urbana-Champaign, United States
- Albert Einstein College of Medicine, 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.
Article and author information
Author details
Reviewing Editor
- Jon Clardy, Harvard Medical School, United States
Version history
- Received: May 5, 2014
- Accepted: June 26, 2014
- Accepted Manuscript published: June 30, 2014 (version 1)
- Version of Record published: July 30, 2014 (version 2)
Copyright
© 2014, Zhao et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks
eLife 3:e03275.
https://doi.org/10.7554/eLife.03275
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