Capturing the free energy of transition state stabilization: insights from the inhibition of mandelate racemase
Bibliographic record
Abstract
Abstract Mandelate racemase (MR) catalyses the Mg2+-dependent interconversion of (R)- and (S)-mandelate. To effect catalysis, MR stabilizes the altered substrate in the transition state (TS) by approximately 26 kcal mol–1 (–ΔGtx), such that the upper limit of the virtual dissociation constant of the enzyme-TS complex is 2 × 10–19 M. Designing TS analogue inhibitors that capture a significant amount of ΔGtx for binding presents a challenge since there are a limited number of protein binding determinants that interact with the substrate and the structural simplicity of mandelate constrains the number of possible isostructural variations. Indeed, current intermediate/TS analogue inhibitors of MR capture less than or equal to 30% of ΔGtx because they fail to fully capitalize on electrostatic interactions with the metal ion, and the strength and number of all available electrostatic and H-bond interactions with binding determinants present at the TS. Surprisingly, phenylboronic acid (PBA), 2-formyl-PBA, and para-chloro-PBA capture 31–38% of ΔGtx. The boronic acid group interacts with the Mg2+ ion and multiple binding determinants that effect TS stabilization. Inhibitors capable of forming multiple interactions can exploit the cooperative interactions that contribute to optimum binding of the TS. Hence, maximizing interactions with multiple binding determinants is integral to effective TS analogue inhibitor design. This article is part of the theme issue ‘Reactivity and mechanism in chemical and synthetic biology’.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".