Magic Angle Spinning Solid-State NMR Experiments for Structural Characterization of Proteins
Bibliographic record
Abstract
Solid-state nuclear magnetic resonance (SSNMR) has become a prominent method in biology and is suitable for the characterization of insoluble proteins and protein aggregates such as amyloid fibrils, membrane-lipid complexes, and precipitated proteins. Often, the initial and the most critical step is to obtain spectroscopic assignments, that is, to determine chemical shifts of individual atoms. The procedures for SSNMR spectroscopic assignments are now well established for small microcrystalline proteins, where high signal-to-noise can be obtained. The sensitivity of the experiments and spectral resolution decrease with the increasing molecular weight, which makes setting SSNMR experiments in large proteins a much more challenging and demanding procedure. Here, we describe the protocol for the most common set of 3D magic angle spinning (MAS) SSNMR experiments. While the procedures described in the text are well known to SSNMR practitioners, we hope they will be of interest to scientists interested in extending their repertoire of biophysical techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".