Membrane Protein Structure Determination Using Solid-State NMR
Bibliographic record
Abstract
Solid-state NMR is emerging as a method for resolving structural information for large biomolecular complexes, such as membrane-embedded proteins. In principle, there is no molecular weight limit to the use of the approach, although the complexity and volume of data is still outside complete assignment and structural determinations for any large (Mr > approx 30,000) complex unless specific methods to reduce the information content to a manageable amount are employed. Such methods include specific residue-type labeling, labeling of putative segments of a protein, or examination of complexes made up of smaller, manageable units, such as oligomeric ion channels. Labeling possibilities are usually limited to recombinant or synthesized proteins, and labeling strategies often follow models from a bioinformatics approach. In all cases, and in common with most membrane studies, sample preparation is vital, and this activity alone can take considerable effort before NMR can be applied--peptide or protein production (synthesis or expression) followed by reconstitution into bilayers and resolution of suitable sample geometry is still technically challenging. As experience is gained in the field, this development time should decrease. Here, the practical aspects of the use of solid-state NMR for membrane protein structural determinations are presented, as well as how the methodology can be applied. Some successes to date are discussed, with an indication of how the area might develop.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".