Incorporating <sup>17</sup>O isotopes onto amino acid sidechains: a convenient synthesis of [3-<sup>17</sup>O]-<scp>l</scp>-serine and [3-<sup>17</sup>O]-<scp>l</scp>-threonine and their <sup>17</sup>O NMR characterization
Bibliographic record
Abstract
We report a convenient synthesis for introducing 17O isotopes onto the hydroxyl groups of two amino acid sidechains: [3-17O]-l-serine and [3-17O]-l-threonine. Our synthetic strategy, based on the Mitsunobu reaction, is a simple one-pot synthesis with reasonable yields of 70% and 30% for [3-17O]-l-serine and [3-17O]-l-threonine, respectively. The final products of [3-17O]-l-serine and [3-17O]-l-threonine contain 8% 17O, which represents a nearly quantitative isotopic transfer from the starting materials, 10% 17O-labeled water. This work represents a significant improvement over the multi-step and low-yield reactions reported only for less costly 18O-labelling of l-serine and l-threonine in the literature. We also obtained solid-state 17O NMR spectra for [3-17O]-l-serine and [3-17O]-l-threonine. This is the first time that the hydroxyl groups in amino acid sidechains are fully characterized by both solution and solid-state 17O NMR. We further discussed the general dependence of 17O chemical shifts for hydroxyl groups on hydrogen bonding interactions. We anticipate that [3-17O]-l-serine and [3-17O]-l-threonine can then be used as precursors for incorporating 17O-labeled amino acid sidechains into proteins.
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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 | 0.000 |
| 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".