Probing Chemical Shifts of Invisible States of Proteins with Relaxation Dispersion NMR Spectroscopy: How Well Can We Do?
Bibliographic record
Abstract
Carr-Purcell-Meiboom-Gill relaxation dispersion NMR spectroscopy has evolved into a powerful approach for the study of low populated, invisible conformations of biological molecules. One of the powerful features of the experiment is that chemical shift differences between the exchanging conformers can be obtained, providing structural information about invisible excited states. Through the development of new labeling approaches and NMR experiments it is now possible to measure backbone 13C(alpha) and 13CO relaxation dispersion profiles in proteins without complications from 13C-13C couplings. Such measurements are presented here, along with those that probe exchange using 15N and 1HN nuclei. A key experimental design has been the choice of an exchanging system where excited-state chemical shifts were known from independent measurement. Thus it is possible to evaluate quantitatively the accuracy of chemical shift differences obtained in dispersion experiments and to establish that in general very accurate values can be obtained. The experimental work is supplemented by computations that suggest that similarly accurate shifts can be measured in many cases for systems with exchange rates and populations that fall within the range of those that can be quantified by relaxation dispersion. The accuracy of the extracted chemical shifts opens up the possibility of obtaining quantitative structural information of invisible states of the sort that is now available from chemical shifts recorded on ground states of 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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.022 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".