Structural and Dynamic Analysis of Residual Dipolar Coupling Data for Proteins
Bibliographic record
Abstract
The measurement of residual dipolar couplings in weakly aligned proteins can potentially provide unique information on their structure and dynamics in the solution state. The challenge is to extract the information of interest from the measurements, which normally reflect a convolution of the structural and dynamic properties. We discuss here a formalism which allows a first order separation of their effects, and thus, a simultaneous extraction of structural and motional parameters from residual dipolar coupling data. We introduce some terminology, namely a generalized degree of order, which is necessary for a meaningful discussion of the effects of motion on residual dipolar coupling measurements. We also illustrate this new methodology using an extensive set of residual dipolar coupling measurements made on (15)N,(13)C-labeled human ubiquitin solvated in a dilute bicelle solution. Our results support a solution structure of ubiquitin which on average agrees well with the X-ray structure (Vijay-Kumar, et al., J. Mol. Biol. 1987, 194, 531--544) for the protein core. However, the data are also consistent with a dynamic model of ubiquitin, exhibiting variable amplitudes, and anisotropy, of internal motions. This work suggests the possibility of primary use of residual dipolar couplings in characterizing both structure and anisotropic internal motions of proteins in the solution state.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".