Measurement of Proton Chemical Shifts in Invisible States of Slowly Exchanging Protein Systems by Chemical Exchange Saturation Transfer
Bibliographic record
Abstract
Chemical exchange saturation transfer (CEST) NMR spectroscopy has emerged as a powerful technique for studies of transiently formed, sparsely populated (excited) conformational states of protein molecules in slow exchange with a dominant structure. The most popular form of the experiment, and the version originally developed, uses a weak (1)H radio frequency field to perturb longitudinal magnetization of one state with the effect transferred to magnetization in the second conformation via chemical exchange. A significant limitation of the method for protein applications emerges from (1)H magnetization transfer via dipolar relaxation (NOE effect) that can severely complicate analysis of the resulting CEST profile. This is particularly an issue since the (1)H chemical shifts of the excited state, critical for structural studies of these elusive conformers, become difficult to extract. Here we present a method for measurement of these shifts via CEST experiments in which the NOE effect is not an issue. The methodology is illustrated through applications to a pair of exchanging systems where the results are cross-validated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".