Discovery of new enzymes and metabolic pathways using structure and genome context

Assigning valid functions to proteins identified in genome projects is challenging, with over-prediction and database annotation errors major concerns(1). We, and others(2), are developing computation-guided strategies for functional discovery using “metabolite docking” to experimentally derived(3) or homology-based(4) three-dimensional structures. Bacterial metabolic pathways often are encoded by “genome neighborhoods” (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by “predicting” the intermediates in the glycolytic pathway in E. coli(5). Metabolite docking to multiple binding proteins/enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. We report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed i) the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans-4-hydroxy-L-proline betaine (tHyp-B) and cis-4-hydroxy-D-proline betaine (cHyp-B), and ii) the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses; the intermediates were identified by metabolomics; and repression of the genes encoding the pathway by high salt was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guide functional predictions to enable the discovery of new metabolic pathways.