COLLABORATIVE CENTER FOR AN ENZYME FUNCTION INITIATIVE

DESCRIPTION (provided by applicant): The Enzyme Function Initiative (EFI) will develop a robust sequence/structure-based strategy for facilitating discovery of in vitro enzymatic and in vivo metabolic/physiological functions of unknown enzymes discovered in genome projects, a crucial limitation in genomic biology. The EFI will accomplish this goal by integrating bioinformatics, structural biology, and computation with enzymology, genetics, and metabolomics. The EFI will establish five Scientific Cores for: 1) directing target selection as well as devising strategies for functional assignment based on sequence relationships and genome context; 2) expression and purification of targets; 3) experimental determination of structures of targets; 4) computational determination of structures of targets (homology modeling) and, also, in silico docking of ligand libraries to direct experimental assignment of in vitro functions by focused library screening; and 5) microbiological and metabolomic characterization of the in vivo roles of the in vitro assigned functions. The functional predictions will be tested by five Bridging Projects that focus on the functionally diverse amidohydrolase (AH), enolase (EN), glutathione transferase (GST), haloalkanoic acid dehalogenase (HAD), and isoprenoid synthase (IS) superfamilies. These superfamilies were selected because functional assignment cannot be accomplished by transfer of prior annotations based only on sequence or structural similarity: the reactions within each superfamily share conserved partial reactions but the identities of the substrates/products are not conserved. The EFI will disseminate to the scientific community the intellectual, computational, and experimental tools, protocols, materials, and guidelines for determining in vitro and in vivo functions of unknown enzymes. In achieving this goal, the EFI will nucleate and enable a larger consortium of investigators working toward the goal of realizing the biomedical potential of the vast amount of sequence data provided by genome projects.