Increasing the Exchange Time-Scale That Can Be Probed by CPMG Relaxation Dispersion NMR
Bibliographic record
Abstract
Carr-Purcell-Meiboom-Gill relaxation dispersion NMR spectroscopy has emerged as a valuable tool to characterize conformational exchange between major and minor states in a large variety of biomolecules. The window of exchange that is amenable for study, corresponding to rates on the order of 2000 s(-1) or less, is limiting, however. Here we show that a combined analysis of both amide (15)N and (1)H(N) CPMG profiles and major state exchange induced (15)N chemical shift changes leads to significant increases in the exchange time scale for which accurate exchange parameters and chemical shift differences between the interconverting states can be obtained. The utility of the approach is illustrated with examples involving a pair of protein systems that are in the moderately fast exchange regime. In these cases the analysis of dispersion profiles alone is not sufficient to obtain robust measures of exchange parameters and chemical shift differences. Inclusion of major state exchange induced (15)N chemical shift changes measured in ((15)N-(1)H(N)) HMQC and HSQC data sets in addition to the (15)N and (1)H(N) dispersion profiles in the analysis "breaks" the correlation in parameters, allowing accurate values to be obtained. The approach is straightforward to implement and makes use of HMQC/HSQC data sets that are recorded as a matter of routine to obtain chemical shifts of the excited state. It promises to increase the range of exchanging systems involving low populated, transiently formed excited states that can be studied by relaxation dispersion NMR.
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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".