A Mutagenesis-Free Approach to Assignment of <sup>19</sup>F NMR Resonances in Biosynthetically Labeled Proteins
Bibliographic record
Abstract
Solution NMR studies of protein structure and dynamics using fluorinated amino acid probes are a valuable addition to the repertoire of existing (13)C, (15)N, and (1)H experiments. Despite the numerous advantages of the (19)F nucleus in NMR, protein studies are complicated by the dependence of resonance assignments on site-directed mutagenesis methods which are laborious and often problematic. Here we report an NMR-based route to the assignment of fluorine resonances in (13)C,(15)N-3-fluoro-l-tyrosine labeled calmodulin. The assignment begins with the correlation of the fluorine nucleus to the delta proton in the novel (13)C,(15)N-enriched probe which is achieved using a CT-HCCF-COSY experiment. Connection to the backbone is made through two additional solution NMR experiments, namely the (H(beta))C(beta)(C(gamma)C(delta))H(delta) and HNCACB. Assignments are completed using either previously published backbone chemical shift data or obtained experimentally provided uniform (13)C,(15)N labeling procedures are employed during protein expression. Additional benefits of the (13)C,(15)N-3-fluoro-l-tyrosine probe include the reduction of spectral overlap through ((13)C(19)F) CT-HSQCs, as well as the ability to monitor side chain dynamics using (19)F T(1), T(2), and the (13)C-(19)F NOE.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".