cis-3-hydroxy-D-proline

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.

Introduction

In 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.