Novel inhibitors of dihydrodipicolinate synthase

Dihydrodipicolinate synthase (DHDPS) catalyzes the first committed step of L-lysine and meso-diaminopimelate biosynthesis, which is the condensation of (S)-aspartate-β-semialdehyde (ASA) and pyruvate into dihydrodipicolinate via an unstable heterocyclic intermediate, (4S)-hydroxy-2,3,4,5-tetrahydro-(2S)-dipicolinic acid. DHDPS has been an attractive antibiotic target because L-lysine and meso-diaminopimelate are cross-linking components between peptidoglycan heteropolysaccharide chains in bacterial cell walls. Studies revealed that mutant auxotrophs for diaminopimelate undergo lysis in the absence of diaminopimelate in the medium; therefore the assumption is that strong inhibition of DHDPS would result in disruption of meso-diaminopimelate and L-lysine biosynthesis in bacteria and would stop or decrease bacterial growth (eventually leading to bacterial death). In this work, the DHDPS inhibitor design is focused on the allosteric site of the enzyme. It was proposed that a compound mimicking binding of two L-lysine molecules at the allosteric site at the enzyme’s dimer-dimer interface would be a more potent inhibitor than the natural allosteric inhibitor of this enzyme, L-lysine. This inhibitor (R,R-bislysine) was synthesized as a racemic mixture, which was then separated with the aid of chiral HPLC. The mechanism of feedback inhibition of DHDPS from Campylobacter jejuni with its natural allosteric modulator, L-lysine, and its synthetic mimic, R,R-bislysine, is studied in detail. It is found that L-lysine is a partial uncompetitive inhibitor with respect to pyruvate and a partial mixed inhibitor with respect to ASA. R,R-bislysine is a mixed partial inhibitor with respect to pyruvate and a noncompetitive partial inhibitor with respect to ASA, with an inhibition constant of 200 nM. Kinetic evaluation of each DHDPS mutants (Y110F, H56A, H56N, H59A and H59N) has revealed amino acids responsible for the inhibitory effect of L-lysine, R,R-bislysine, and we have found that R,R-bislysine is a strong submicromolar inhibitor of Y110F, H56A, H56N and H59N

Dihydrodipicolinate Synthase from <i>Campylobacter jejuni</i>: Kinetic Mechanism of Cooperative Allosteric Inhibition and Inhibitor-Induced Substrate Cooperativity

Dihydrodipicolinate synthase (DHDPS), an enzyme of the <i>meso</i>-diaminopimelate pathway of lysine biosynthesis, is essential for bacterial growth and is considered a target for novel antibiotics. We have studied DHDPS from <i>Campylobacter jejuni</i> for the first time, determining the kinetic mechanism of catalysis and inhibition with its natural allosteric feedback inhibitor (<i>S</i>)-lysine. The tetrameric enzyme is known to have two allosteric sites, each of which binds two molecules of lysine. The results suggest that lysine binds highly cooperatively, and primarily to the F form of the enzyme during the ping–pong mechanism. By applying graphical methods and nonlinear regression, we have discriminated between the possible kinetic models and determined the kinetic and inhibition constants and Hill coefficients. We conclude that (<i>S</i>)-lysine is an uncompetitive partial inhibitor with respect to its first substrate, pyruvate, and a mixed partial inhibitor with respect to its second substrate, (<i>S</i>)-aspartate-β-semialdehyde (ASA), which differs from the kinetic models for inhibition reported for DHDPS from other sources. The Hill coefficients for the binding of lysine to different forms of the enzyme are all greater than 2, suggesting that the two allosteric sites are not independent. It has been found that ASA binds cooperatively in the presence of (<i>S</i>)-lysine, and the cooperativity of binding increases at near-<i>K</i>_M concentrations of pyruvate. The incorporation of Hill coefficients into the kinetic equations was crucial for determining the kinetic model for this enzyme

Mapping of UK Biobank clinical codes: Challenges and possible solutions.

ObjectiveThe UK Biobank provides a rich collection of longitudinal clinical data coming from different healthcare providers and sources in England, Wales, and Scotland. Although extremely valuable and available to a wide research community, the heterogeneous dataset contains inconsistent medical terminology that is either aligned to several ontologies within the same category or unprocessed. To make these data useful to a research community, data cleaning, curation, and standardization are needed. Significant efforts to perform data reformatting, mapping to any selected ontologies (such as SNOMED-CT) and harmonization are required from any data user to integrate UK Biobank hospital inpatient and self-reported data, data from various registers with primary care (GP) data. The integrated clinical data would provide a more comprehensive picture of one's medical history.Materials and methodsWe evaluated several approaches to map GP clinical Read codes to International Classification of Diseases (ICD) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) terminologies. The results were compared, mapping inconsistencies were flagged, a quality category was assigned to each mapping to evaluate overall mapping quality.ResultsWe propose a curation and data integration pipeline for harmonizing diagnosis. We also report challenges identified in mapping Read codes from UK Biobank GP tables to ICD and SNOMED CT.Discussion and conclusionSome of the challenges-the lack of precise one-to-one mapping between ontologies or the need for additional ontology to fully map terms-are general reflecting trade-offs to be made at different steps. Other challenges are due to automatic mapping and can be overcome by leveraging existing mappings, supplemented with automated and manual curation