2D and 3D maximum-quantum NMR and diffusion spectroscopy for the characterization of enzymatic reaction mixtures
Bibliographic record
Abstract
H NMR spectroscopy has been widely used to monitor enzymatic activity by recording the evolution of the spectra of substrates and/or products, thanks to the linear response of NMR. For complex systems involving the coexistence of multiple compounds (substrate, final product and various intermediates), the identification and quantification can be a more arduous task. Here, we present a simple analytical method for the rapid characterization of reaction mixtures involving enzymatic complexes using Maximum Quantum (MaxQ) NMR, accelerated with the Non-Uniform Sampling (NUS) acquisition procedure. Specifically, this approach enables, in the first analytical step, the counting of the molecules present in the samples. We also show, using two different enzymatic systems, that the implementation of these pulse sequences implies precautions related to the short relaxation times due to the presence of metallo-enzymes or paramagnetic catalysts. Finally, the combination of MaxQ and diffusion experiments, which leads to a 3D chart, greatly improves the resolution and offers an extreme simplification of the spectra while giving valuable indications on the affinity of the enzymes to the different compounds present in the reaction mixture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".