Increasing the accuracy of exchange parameters reporting on slow dynamics by performing CEST experiments with high <i>B</i> <sub>1</sub> fields
Bibliographic record
Abstract
Abstract Over the last decade chemical exchange saturation transfer (CEST) NMR methods have emerged as powerful tools to characterize biomolecular conformational dynamics occurring between a visible major state and ‘invisible’ minor states. The ability of the CEST experiment to detect these minor states, and provide precise exchange parameters, hinges on using appropriate B 1 field strengths during the saturation period. Typically, a pair of B 1 fields with ω 1 (= 2 πB 1 ) values around the exchange rate k ex are chosen. Here we show that the transverse relaxation rate of the minor state resonance ( R 2, B ) also plays a crucial role in determining the B 1 fields that lead to the most informative datasets. Using , to guide the choice of B 1 , instead of k ex , leads to data wherefrom substantially more accurate exchange parameters can be derived. The need for higher B 1 fields, guided by K, is demonstrated by studying the conformational exchange in two mutants of the 71 residue FF domain with k ex ∼11 s -1 and ∼72 s -1 , respectively. In both cases analysis of CEST datasets recorded using B 1 field values guided by k ex lead to imprecise exchange parameters, whereas using B 1 values guided by K resulted in precise site-specific exchange parameters. The conclusions presented here will be valuable while using CEST to study slow processes at sites with large intrinsic relaxation rates, including carbonyl sites in small to medium sized proteins, amide 15 N sites in large proteins and when the minor state dips are broadened due to exchange among the minor states.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".