Membrane proteins in their native habitat as seen by solid‐state NMR spectroscopy
Bibliographic record
Abstract
Membrane proteins play many critical roles in cells, mediating flow of material and information across cell membranes. They have evolved to perform these functions in the environment of a cell membrane, whose physicochemical properties are often different from those of common cell membrane mimetics used for structure determination. As a result, membrane proteins are difficult to study by traditional methods of structural biology, and they are significantly underrepresented in the protein structure databank. Solid-state Nuclear Magnetic Resonance (SSNMR) has long been considered as an attractive alternative because it allows for studies of membrane proteins in both native-like membranes composed of synthetic lipids and in cell membranes. Over the past decade, SSNMR has been rapidly developing into a major structural method, and a growing number of membrane protein structures obtained by this technique highlights its potential. Here we discuss membrane protein sample requirements, review recent progress in SSNMR methodologies, and describe recent advances in characterizing membrane proteins in the environment of a cellular membrane.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".