Comprehensive and Cost-Effective NMR Spectroscopy of Methyl Groups in Large Proteins
Bibliographic record
Abstract
An NMR approach is described which yields the methyl resonance assignments of alanine, threonine, valine, leucine, and isoleucine residues in proteins with high sensitivity and excellent resolution. The method relies on protein samples produced by bacterial expression using [(1)H,(13)C]-D-glucose and approximately 100% D(2)O, which is cost-effective and ensures the isotopic enrichment of all possible methyl groups. Magnetization transfer throughout the methyl-containing side chains is possible with this labeling scheme due to the high level of deuteration along the amino acid side chain, coupled with the selection of the favorable CHD(2) methyl isotopomer for detection. In an application to the 34 kDa periplasmic binding protein FepB 164 out of 195 methyl groups (85%) were assigned sequence-specifically and stereospecifically. This percentage increases to 91% when taking into account that not all backbone assignments are available for this system. The remaining unassigned methyl groups belong to six leucine residues, caused by low cross-peak intensities, and four alanine residues due to degeneracy of the (13)C(alpha)/(13)C(beta) frequencies. Our results demonstrate that NMR spectroscopic investigations of protein structure, dynamics, and interactions can be extended to include all methyl-containing amino acids also for larger 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".