Design of Inhibitors of Orotidine Monophosphate Decarboxylase Using Bioisosteric Replacement and Determination of Inhibition Kinetics
Bibliographic record
Abstract
Inhibitors of orotidine monophosphate decarboxylase (ODCase) have applications in RNA viral, parasitic, and other infectious diseases. ODCase catalyzes the decarboxylation of orotidine monophosphate (OMP), producing uridine monophosphate (UMP). Novel inhibitors 6-amino-UMP and 6-cyano-UMP were designed on the basis of the substructure volumes in the substrate OMP and in an inhibitor of ODCase, barbituric acid monophosphate, BMP. A new enzyme assay method using isothermal titration calorimetry (ITC) was developed to investigate the inhibition kinetics of ODCase. The reaction rates were measured by monitoring the heat generated during the decarboxylation reaction of orotidine monophosphate. Kinetic parameters (k(cat) = 21 s(-1) and KM = 5 microM) and the molar enthalpy (DeltaH(app) = 5 kcal/mol) were determined for the decarboxylation of the substrate by ODCase. Competitive inhibition of the enzyme was observed and the inhibition constants (Ki) were determined to be 12.4 microM and 29 microM for 6-aza-UMP and 6-cyano-UMP, respectively. 6-Amino-UMP was found to be among the potent inhibitors of ODCase, having an inhibition constant of 840 nM. We reveal here the first inhibitors of ODCase designed by the principles of bioisosterism and a novel method of using isothermal calorimetry for enzyme inhibition studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".