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 2 H, 13 C, and 15 N isotopically labeled proteins in residue assignment strategies. For modestly sized proteins, the assignments can be accomplished by standard homonuclear 1 H 2D methodology ( 1 ). As the size of the proteins exceeds 10 kDa, the NMR spectra become more crowded with overlapping signals. With 13 C and 15 N 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 13 C and 1 H T 2 relaxation times. The most significant contribution to 13 C T 2 relaxation is the strong dipolar coupling between the 13C- 1 H spin pairs. 1 H T 2 relaxation arises from proton-proton dipolar couplings. Replacement of 1 H by 2 H can increase the T 2 relaxation times significantly. Thus, incorporation of 2 H 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 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".