The overall goal of this project is to identify potential drug targets and companion inhibitors that inhibit growth of Mycobacterium tuberculosis (Mtb), and other pathogenic bacteria in order to develop a foundation for antimicrobial drug discovery. We will accomplish this by identifying proteins that are targeted by whole cell growth inhibitors of Mtb discovered by a recently conducted high-throughput screen of over 200,000 drug-like small molecules. Proteins that are targets of inhibitors will be identified by genetic and complementary biochemical approaches. Structures will be determined, in collaboration with the Protein Structure Initiative Large-Scale Centers, of either the Mtb proteins that are targets of inhibitors or homologs of these proteins. In cases where a human homolog is available, that structure or that of a related eukaryotic protein will be determined as well. The structural information and inhibitor identities will be used along with annotations of related proteins to suggest molecular functions for each target protein. Assays for these molecular functions will be carried out to confirm both function and inhibition. Once targets are identified their inhibitors will additionally be analyzed for their activity against other pathogenic bacteria and eukaryotic cell lines. The targets will then be prioritized based on their novelty, the spectrum of activity of the companion growth inhibitor and the potency of the inhibitor. During the five years of the grant we expect to confirm the molecular targets for about 200 of the top whole cell active molecules and obtain the structures of these target proteins or members of the same sequence family. These structures, inhibitor-protein pairs, and biochemical information about the proteins will provide a rich resource of lead compounds and biochemistry for Mtb drug discovery, a foundation for understanding Mtb biology, and a path forward in the effort to improve human health by curing tuberculosis.
The relevance of this proposal to human health is that it will generate unique structural and functional data needed to support drug discovery for tuberculosis, which remains a leading cause of death worldwide.
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