A Phenylboronic Acid-Based Transition State Analogue Yields Nanomolar Inhibition of Mandelate Racemase
Bibliographic record
Abstract
Mandelate racemase (MR) catalyzes the Mg 2+ -dependent interconversion of ( R )- and ( S )-mandelate by stabilizing the altered substrate in the transition state (TS) by ∼26 kcal/mol. The enzyme has been employed as a model to explore the limits to which the free energy of TS stabilization may be captured by TS analogues to effect strong binding. Herein, we determined the thermodynamic parameters accompanying binding of a series of bromo-, chloro-, and fluoro-substituted phenylboronic acids (PBAs) by MR and found that binding was predominately driven by favorable entropy changes. 3,4-Dichloro-PBA was discovered to be the most potent inhibitor yet identified for MR, binding with a K d app value of 11 ± 2 nM and exceeding the binding of the substrate by ∼72,000-fold. The Δ C p value accompanying binding (−488 ± 18 cal·mol –1 K –1 ) suggested that dispersion forces contribute significantly to the binding. The pH-dependence of the inhibition revealed that MR preferentially binds the anionic, tetrahedral form of 3,4-dichloro-PBA with a pH-independent K i value of 5.7 ± 0.5 nM, which was consistent with the observed upfield shift of the 11 B NMR signal. The linear free energy relationship between log( k cat / K m ) and log(1/ K i ) for wild-type and 11 MR variants binding 3,4-dichloro-PBA had a slope of 0.8 ± 0.2, indicating that MR recognizes the inhibitor as an analogue of the TS. Hence, halogen substitution may be utilized to capture additional free energy of TS stabilization arising from dispersion forces to enhance the binding of boronic acid inhibitors by MR.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".