An Analysis of the Effects of <sup>1</sup>H<sup>N</sup>−<sup>1</sup>H<sup>N</sup> Dipolar Couplings on the Measurement of Amide Bond Vector Orientations in Invisible Protein States by Relaxation Dispersion NMR
Bibliographic record
Abstract
Marginally and transiently populated conformational states of biomolecules can play important functional roles in biochemical processes. It is of significant interest, therefore, to develop tools for characterizing the structural and dynamical properties of these excited states. One recent development has been the emergence of spin-state-selective relaxation dispersion methods for quantifying dipolar vector orientations in invisible excited-state conformers through measurement of residual dipolar couplings (RDCs). Particularly powerful are 1HN-(15)N RDCs that can be measured with high sensitivity on fractionally aligned, deuterated, uniformly 15N-labeled protein samples. Fractional alignment also produces nonzero 1HN-(1)HN dipolar couplings. These can be problematic for the extraction of robust 1HN-(15)N RDC values, and hence amide bond vector orientations, in cases where the amide proton of interest and a proximal amide proton have small chemical shift differences and a significant 1HN-(1)HN dipolar coupling. Here, we show that while this strong coupling effect leads to aberrant relaxation dispersion profiles, extracted excited-state 1HN-(15)N RDCs are for the most part only marginally affected. Experimental examples of such aberrant profiles are provided, as well as a theoretical consideration of the influence of this strong coupling effect and numerical simulations that assess its impact on extracted parameters.
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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 | 0.002 |
| 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) | 0.001 | 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".