Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks. ↗
Zhao S, Sakai A, Zhang X, Vetting MW, Kumar R, Hillerich B, San Francisco B, Solbiati J, Steves A, Brown S, Akiva E, Barber A, Seidel RD, Babbitt PC, Almo SC, Gerlt JA, Jacobson MP.
Vad undersöktes?
- Hur undersöktes det?
- bioinformatic approach (genome neighborhood network; GNN)
- Vilka undersöktes?
- proline racemase superfamily (PRS)
- Vilken ämnesform?
- Ingen uppgift i sammanställningen
- Vilken mängd användes?
- 51 proteins
- Hur länge pågick studien?
- Ingen uppgift i sammanställningen
Studieuppgifterna återges som originalcitat. Uppgifter som saknas här kan finnas i originalutdraget.
Vad kom studien fram till?
Författarna rapporterar att de utvecklade en bioinformativ metod (genome neighbourhood network) för att förutsäga in vitro-enzymatiska aktiviteter och in vivo-fysiologiska funktioner för oidentifierade enzymer i proteinfamiljer, och verifierade förutsägelserna genom att mäta in vitro-aktiviteter för 51 proteiner i 12 familjer i proline racemase superfamily (PRS) som representerar cirka 85 % av sekvenserna.
Vad behöver resultatet vägas mot?
Underlaget omfattar endast utvalda artikelstycken; någon samlad bedömning kan inte göras.
Läs de originalstycken som sammanställningen bygger på
AbstractMetabolic 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.
IntroductionIn this manuscript, we describe the use of a new bioinformatic strategy, genome neighborhood networks (GNNs), to discover the enzymes, transport systems, and transcriptional regulators that constitute metabolic pathways, thereby facilitating prediction of their individual in vitro activities and combined in vivo metabolic functions. As the first demonstration of its use, we applied this approach to the functionally diverse proline racemase superfamily (PRS) and predicted functions for >85% of its members. The predictions were verified using high-throughput protein expression and purification, in vitro enzyme activity measurements, microbiology (phenotypes and transcriptomics), and X-ray crystallography.
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