Skip to main content
eScholarship
Open Access Publications from the University of California

UCSF

UC San Francisco Previously Published Works bannerUCSF

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

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

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View