Racing toward Fast and Effective <sup>17</sup>O Isotopic Labeling and Nuclear Magnetic Resonance Spectroscopy of N-Formyl-MLF-OH and Associated Building Blocks
Bibliographic record
Abstract
Solid-state 1H, 13C, and 15N nuclear magnetic resonance (NMR) spectroscopy has been an essential analytical method in studying complex molecules and biomolecules for decades. While oxygen-17 (17O) NMR is an ideal and robust candidate to study hydrogen bonding within secondary and tertiary protein structures for example, it continues to elude many. We discuss an improved multiple-turnover labeling procedure to develop a fast and cost-effective method to 17O label fluoroenylmethyloxycarbonyl (Fmoc)-protected amino acid building blocks. This approach allows for inexpensive ($0.25 USD/mg) insertion of 17O labels, an important barrier to overcome for future biomolecular studies. The 17O NMR results of these building blocks and a site-specific strategy for labeled N-acetyl-MLF-OH and N-formyl-MLF-OH tripeptides are presented. We showcase growth in NMR development for maximizing sensitivity gains using emerging sensitivity enhancement techniques including population transfer, high-field dynamic nuclear polarization, and cross-polarization magic-angle spinning cryoprobes.
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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".