Multiple-Quantum Relaxation Dispersion NMR Spectroscopy Probing Millisecond Time-Scale Dynamics in Proteins: Theory and Application
Bibliographic record
Abstract
New relaxation dispersion experiments are presented that probe millisecond time-scale dynamical processes in proteins. The experiments measure the relaxation of (1)H-(15)N multiple-quantum coherence as a function of the rate of application of either (1)H or (15)N refocusing pulses during a constant time relaxation interval. In contrast to the dispersion profiles generated from more conventional (15)N((1)H) single-quantum relaxation experiments that depend on changes in (15)N((1)H) chemical shifts between exchanging states, (1)H-(15)N multiple-quantum dispersions are sensitive to changes in the chemical environments of both (1)H and (15)N spins. The resulting multiple-quantum relaxation dispersion profiles can, therefore, be quite different from those generated by single-quantum experiments, so that an analysis of both single- and multiple-quantum profiles together provides a powerful approach for obtaining robust measures of exchange parameters. This is particularly the case in applications to protonated proteins where other methods for studying exchange involving amide proton spins are negatively influenced by contributions from neighboring protons. The methodology is demonstrated on protonated and perdeuterated samples of a G48M mutant of the Fyn SH3 domain that exchanges between folded and unfolded states in solution.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".