Measuring Kinetic Isotope Effects in Enzyme Reactions Using Time-Resolved Electrospray Mass Spectrometry
Bibliographic record
Abstract
Kinetic isotope effect (KIE) measurements are a powerful tool for studying enzyme mechanisms; they can provide insights into microscopic catalytic processes and even structural constraints for transition states. However, KIEs have not come into widespread use in enzymology, due in large part to the requirement for prohibitively cumbersome experimental procedures and daunting analytical frameworks. In this work, we introduce time-resolved electrospray ionization mass spectrometry (TRESI-MS) as a straightforward, precise, and inexpensive method for measuring KIEs. Neither radioisotopes nor large amounts of material are needed and kinetic measurements for isotopically "labeled" and "unlabeled" species are acquired simultaneously in a single "competitive" assay. The approach is demonstrated first using a relatively large isotope effect associated with yeast alcohol dehydrogenase (YADH) catalyzed oxidation of ethanol. The measured macroscopic KIE of 2.19 ± 0.05 is consistent with comparable measurements in the literature but cannot be interpreted in a way that provides insights into isotope effects in individual microscopic steps. To demonstrate the ability of TRESI-MS to directly measure intrinsic KIEs and to characterize the precision of the technique, we measure a much smaller (12)C/(13)C KIE associated specifically with presteady state acylation of chymotrypsin during hydrolysis of an ester substrate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".