REDOR recoupling in polymer-stabilized liquid crystals undergoing MAS — Two-dimensional NMR applications with strongly aligned proteins
Bibliographic record
Abstract
We recently introduced a technique, termed SAD-REDOR, for recoupling residual dipolar couplings in a single-domain polymer-stabilized liquid crystal (PSLC) undergoing magic-angle spinning (MAS). The experiment was demonstrated with 1H–15N dipolar couplings in a small molecule. Here, we show the applicability of the technique to a strongly aligned protein and we describe a novel two-dimensional experiment to generate in-phase and anti-phase (IPAP) doublets in the 1H dimension of an HSQC NMR spectrum. This pulse sequence, SAD-IPAP, was tested on a sample of 15N-labeled ubiquitin (5 mmol/L) in a polyacrylamide-stabilized Pf1 phage liquid crystal (20 mg/mL). 15N–1H residual dipolar couplings (RDCs) were measured with the SAD-IPAP pulse sequence at spinning speeds of 1000 and 1250 Hz. RDSs were also measured using the conventional HSQC-IPAP method in a sample of 15N-ubiquitin dissolved in a solution of Pf1 phage (1 mg/mL). The resulting RDCs were fitted to the solution structure of ubiquitin to estimate the alignment tensor and to determine the accuracy of the measured couplings. The results highlight the benefits of SAD-REDOR for the measurement of RDCs in strongly aligned macromolecules.Key words: residual dipolar couplings (RDCs), polymer-stabilized liquid crystals (PSLCs), rotational echo double resonance (REDOR) recoupling, magic-angle spinning (MAS), ubiquitin, biomolecular nuclear magnetic resonance (NMR), 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".