Structure determination of membrane proteins by NMR spectroscopy
Bibliographic record
Abstract
Current strategies for determining the structures of membrane proteins in lipid environments by NMR spectroscopy rely on the anisotropy of nuclear spin interactions, which are experimentally accessible through experiments performed on weakly and completely aligned samples. Importantly, the anisotropy of nuclear spin interactions results in a mapping of structure to the resonance frequencies and splittings observed in NMR spectra. Distinctive wheel-like patterns are observed in two-dimensional 1H-15N heteronuclear dipolar/15N chemical shift PISEMA (polarization inversion spin-exchange at the magic angle) spectra of helical membrane proteins in highly aligned lipid bilayer samples. One-dimensional dipolar waves are an extension of two-dimensional PISA (polarity index slant angle) wheels that map protein structures in NMR spectra of both weakly and completely aligned samples. Dipolar waves describe the periodic wave-like variations of the magnitudes of the heteronuclear dipolar couplings as a function of residue number in the absence of chemical shift effects. Since weakly aligned samples of proteins display these same effects, primarily as residual dipolar couplings, in solution NMR spectra, this represents a convergence of solid-state and solution NMR approaches to structure determination.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".