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

Suwen Zhao, Ayano Sakai, Xinshuai Zhang, Matthew W. Vetting, Ritesh Kumar, Brandan Hillerich, Brian San Francisco, Jose Solbiati, Adam Steves, Shoshana Brown, Eyal Akiva, Alan Barber, Ronald D. Seidel, Patricia C. Babbitt, Steven C. Almo, John A. Gerlt, Matthew P. Jacobson

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number03275
JournaleLife
Volume2014
Issue number3
DOIs
StatePublished - Jun 30 2014

ASJC Scopus subject areas

  • Neuroscience(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)

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