Next-Generation Tags for Fluorine Nuclear Magnetic Resonance: Designing Amplification of Chemical Shift Sensitivity
Bibliographic record
Abstract
Fluorine NMR is a highly sensitive technique for delineating the conformational states of biomolecules and has shown great utility in drug screening and in understanding protein function. Current fluorinated protein tags leverage the intrinsic chemical shift sensitivity of the 19 F nucleus to detect subtle changes in protein conformation and topology. This chemical shift sensitivity can be amplified by embedding the fluorine or trifluoromethyl reporter within a pyridone. Due to their polarizability and rapid tautomerization, pyridones exhibit a greater range of electron delocalization and correspondingly greater 19 F NMR chemical shift dispersion. To assess the chemical shift sensitivity of these tautomeric probes to the local environment, 19 F NMR spectra of all possible monofluorinated and trifluoromethyl-tagged versions of 2-pyridone were recorded in methanol/water mixtures ranging from 100% methanol to 100% water. 4-Fluoro-2-pyridone and 6-(trifluoromethyl)-2-pyridone (6-TFP) displayed the greatest sensitivity of the monofluorinated and trifluoromethylated pyridones, exceeding that of known conventional CF 3 reporters. To evaluate the utility of tautomeric pyridone tags for 19 F NMR of biomolecules, the alpha subunit of the stimulatory G protein (G s α) and human serum albumin (HSA) were each labeled with a thiol-reactive derivative of 6-TFP and the spectra were recorded as a function of various adjuvants and drugs. The tautomeric tag outperformed the conventional tag, 2-bromo- N -(4-(trifluoromethyl)phenyl)acetamide through the improved resolution of several functional states.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".