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Carl R. Woese Institute for Genomic Biology

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Glue Grant will Enable Enzyme Function Discovery

A multi-institutional team of researchers, led by John Gerlt, Gutgsell Chair, Professor of Biochemistry and Chemistry and a member of the Mining Microbial Genomes Theme at the Institute for Genomic Biology at the University of Illinois, has received a prestigious and highly competitive "Glue grant" from the National Institutes of General Medical Sciences (NIGMS). Glue grants provide resources to tackle "complex problems that are of central importance to biomedical science but are beyond the means of any one research group," so these "glue" together multidisciplinary groups of investigators. This Glue grant, known as the Enzyme Function Initiative (EFI), will develop a strategy for discovering the functions of unknown enzymes discovered in genome sequencing projects. The EFI will receive $33.9 million in total costs for the five-year project.

"Genome projects have taught us that many of Nature's enzymes have unknown functions that need to be discovered," said Gerlt, an expert on the enolase superfamily of enzymes. "We don't know every metabolic pathway. Organisms have metabolic pathways that allow them to live under different conditions and we don't know what many of those pathways are and, therefore, the substrates, transformations, and intermediates in those pathways." Enzymes are proteins that catalyze the chemical reactions required for life and, also, enable organisms to live in complex environments and adapt to a variety of conditions. Determining an enzyme's substrate, the molecule to which the enzyme "docks" in order to begin a reaction, is vital to understanding an enzyme's function. And understanding an enzyme's function is fundamental to understanding the biology of an organism, as well as opening up enormous biomedical and commercial opportunities.

"We have sequences for more than 10 million proteins and we might know the specific functions of half of those," said Gerlt. "But what do the other half do? If we knew their functions, imagine how we might use them to identify new drug targets or provide catalysts used in industry. Presumably there is a lot of functional diversity; but, how can you know what the functions are?"

Gerlt and his longtime collaborator and colleague, Patricia Babbitt at the University of California, San Francisco (UCSF), have spearheaded a way to more efficiently determine an unknown, or uncharacterized, protein's function. Their approach uses computational methods to narrow the range of possible substrates for the enzyme. Gerlt says this project is a potentially powerful way to more fully exploit the sequence data that have not yet been deciphered and to learn more about metabolic pathways, so crucial to all organisms.

This approach focuses on identifying an enzyme's substrate. Typically, if an enzyme's substrate is unknown, investigators could try countless different substrates, in essentially a trial-and-error method.

"That approach, however, is not efficient," said Gerlt. "We'd like to predict the substrate, or restrict the number of possible substrates, so that instead of assaying every compound from the Sigma catalog we could try, say, 20 compounds."

For the EFI Glue grant, Gerlt and Babbitt have assembled a team of researchers from several disciplines who can determine the structure of an unknown enzyme and then, computationally, determine a "hit list" of possible substrates, numbering in the tens, rather than the thousands. The "hit list" will be evaluated by experimentalists, and the substrate will be identified. In addition, members of this team will knock out the gene encoding the enzyme to determine its biological function, as well as its role in a metabolic pathway.

Babbitt will lead a bioinformatics team that will manage and analyze the sequence data and help identify like "target" sequences of interest. Computational biologists, including Matthew Jacobson, Andrej Sali, and Brian Shoichet at UCSF, will use computers for "in silico" docking of possible substrates with the "target" and develop a "hit list" of possible substrates. A high-throughput protein production and structure determination group lead by Steven Almo at Albert Einstein College of Medicine will purify the "targets" and determine their structures by X ray crystallography. Wladek Minor at the University of Virginia will oversee a laboratory information management system to facilitate data exchange both within the EFI as well as with the scientific community.

The EFI team also includes experimentalists, like Gerlt, who have expertise in different enzyme families, to test the functional predictions. A team at Texas A & M led by Frank Raushel specializes in the amidohydrolase superfamily; the University of Utah group led by C. Dale Poulter specializes in the isoprenoid synthase superfamily; Richard Armstrong at Vanderbilt University School of Medicine is an expert in the glutathione transferase superfamily; and a team of scientists led by Karen Allen and Debra Dunaway-Mariano at Boston University and the University of New Mexico, respectively, specializes in the haloalkanoic acid dehalogenase superfamily.

The EFI also will have a microbiology group led by John Cronan, a professor in the Department of Microbiology at the University of Illinois, that will knock out the gene encoding a "target" to further help determine its metabolic function. U. of I. Chemistry Professor Jonathan Sweedler will determine how the knocked out gene alters the metabolism of the organism.

The first Glue grant was awarded in 2000. The EFI and an additional new Glue grant join only four that are currently supported by NIGMS. "This program gathers together an outstanding group of researchers who will use their expertise in enzymology, structural biology, computational modeling and bioinformatics to develop an approach to associate enzymatic functions with genes in thousands of organisms," said Warren Jones, Ph.D., who oversees the program at the NIH's National Institute of General Medical Sciences. "The effort will add considerable value to genome sequencing data by finding the functions of genes with unknown roles, advancing our understanding of life-sustaining biochemical processes and possibly suggesting important new targets for human therapeutics."

Associated Themes
Mining Microbial Genomes