Structure of a small-molecule inhibitor complexed with GlmU from Haemophilus influenzae reveals an allosteric binding site.

N-Acetylglucosamine-1-phosphate uridyltransferase (GlmU) is an essential enzyme in aminosugars metabolism and an attractive target for antibiotic drug discovery. GlmU catalyzes the formation of uridine-diphospho-N-acetylglucosamine (UDP-GlcNAc), an important precursor in the peptidoglycan and lipopolisaccharide biosynthesis in both Gram-negative and Gram-positive bacteria. Here we disclose a 1.9 A resolution crystal structure of a synthetic small-molecule inhibitor of GlmU from Haemophilus influenzae (hiGlmU). The compound was identified through a high-throughput screening (HTS) configured to detect inhibitors that target the uridyltransferase active site of hiGlmU. The original HTS hit exhibited a modest micromolar potency (IC(50) approximately 18 microM in a racemic mixture) against hiGlmU and no activity against Staphylococcus aureus GlmU (saGlmU). The determined crystal structure indicated that the inhibitor occupies an allosteric site adjacent to the GlcNAc-1-P substrate-binding region. Analysis of the mechanistic model of the uridyltransferase reaction suggests that the binding of this allosteric inhibitor prevents structural rearrangements that are required for the enzymatic reaction, thus providing a basis for structure-guided design of a new class of mechanism-based inhibitors of GlmU.

Identifying promising compounds in drug discovery: genetic algorithms and some new statistical techniques.

Throughout the drug discovery process, discovery teams are compelled to use statistics for making decisions using data from a variety of inputs. For instance, teams are asked to prioritize compounds for subsequent stages of the drug discovery process, given results from multiple screens. To assist in the prioritization process, we propose a desirability function to account for a priori scientific knowledge; compounds can then be prioritized based on their desirability scores. In addition to identifying existing desirable compounds, teams often use prior knowledge to suggest new, potentially promising compounds to be created in the laboratory. Because the chemistry space to search can be dauntingly large, we propose the sequential elimination of level combinations (SELC) method for identifying new optimal compounds. We illustrate this method on a combinatorial chemistry example.

Discovery and characterization of a novel inhibitor of matrix metalloprotease-13 that reduces cartilage damage in vivo without joint fibroplasia side effects.

Matrix metalloproteinase-13 (MMP13) is a Zn(2+)-dependent protease that catalyzes the cleavage of type II collagen, the main structural protein in articular cartilage. Excess MMP13 activity causes cartilage degradation in osteoarthritis, making this protease an attractive therapeutic target. However, clinically tested MMP inhibitors have been associated with a painful, joint-stiffening musculoskeletal side effect that may be due to their lack of selectivity. In our efforts to develop a disease-modifying osteoarthritis drug, we have discovered MMP13 inhibitors that differ greatly from previous MMP inhibitors; they do not bind to the catalytic zinc ion, they are noncompetitive with respect to substrate binding, and they show extreme selectivity for inhibiting MMP13. By structure-based drug design, we generated an orally active MMP13 inhibitor that effectively reduces cartilage damage in vivo and does not induce joint fibroplasias in a rat model of musculoskeletal syndrome side effects. Thus, highly selective inhibition of MMP13 in patients may overcome the major safety and efficacy challenges that have limited previously tested non-selective MMP inhibitors. MMP13 inhibitors such as the ones described here will help further define the role of this protease in arthritis and other diseases and may soon lead to drugs that safely halt cartilage damage in patients.