Structure Determination by NMR: Isotope Labeling
Bibliographic record
Abstract
Solution NMR spectroscopy is used widely to determine the structure of proteins. The size of the proteins that can be studied has increased dramatically in the past decade as advances in pulse sequences, probe design, and instrumentation has been made. One major contributing factor to these advances has been the ability to utilize 2H, 13C, and 15N isotopically labeled proteins in residue assignment strategies. For modestly sized proteins, the assignments can be accomplished by standard homonuclear 1H 2D methodology (1). As the size of the proteins exceeds 10 kDa, the NMR spectra become more crowded with overlapping signals. With 13C and 15N labeling, the heteronuclear experiments have allowed the spectra to spread into two, three, or four dimensions, thus increasing the resolution and decreasing the assignment ambiguities (2). Another problem accompanying increasing protein size is sensitivity loss as a result of line broadening because of the decrease in 13C and 1H T2 relaxation times. The most significant contribution to 13C T2 relaxation is the strong dipolar coupling between the 13C-1H spin pairs. 1H T2 relaxation arises from proton-proton dipolar couplings. Replacement of 1H by 2H can increase the T2 relaxation times significantly. Thus, incorporation of 2H into large proteins has been widely used to improve the quality of spectra by a reduction in the number of peaks and concomitant narrowing of linewidths (3). In addition to structural determination, heteronuclear multidimensional NMR has also been widely used to study protein dynamics and interactions of these molecules (3).
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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".