A platform for chemical modification of mandelate racemase: characterization of the C92S/C264S and γ-thialysine 166 variants
Bibliographic record
Abstract
Mandelate racemase (MR) serves as a paradigm for our understanding of enzyme-catalyzed deprotonation of a carbon acid substrate. To facilitate structure-function studies on MR using non-natural amino acid substitutions, we engineered the Cys92Ser/Cys264Ser variant (dmMR) as a platform for introducing Cys residues at specific locations for subsequent covalent modification. While the highly reactive thiol of Cys furnishes a site for chemical modification, site-specificity requires that other Cys residues be non-reactive or replaced by a non-reactive amino acid, especially if chemical modification is conducted under denaturing conditions. The catalytic efficiency of dmMR is reduced only ~2-fold relative to wild-type MR, making dmMR a viable platform for the site-specific introduction of Cys. As an example, the inactive Lys166Cys variant of dmMR was treated with ethylenimine under denaturing conditions to replace the Brønsted acid-base catalyst Lys 166 with the non-natural amino acid γ-thialysine. Comparison of the pH-activity profiles of dmMR and the active γ-thialysine variant revealed a reduction in the pKa for the side chain amino group of ~0.4 units for the latter variant. Unlike wild-type MR for which diffusion is partially rate-limiting, dmMR and the γ-thialysine variant showed no dependence on the solvent viscosity suggesting that the chemical step is fully rate-limiting.
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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".