Current Applications of Bicelles in NMR Studies of Membrane-Associated Amphiphiles and Proteins<sup>,</sup>
Bibliographic record
Abstract
This review covers current trends in studies of membrane amphiphiles and membrane proteins using both fast tumbling bicelles and magnetically aligned bicelle media for both solution state and solid state NMR. The fast tumbling bicelles provide a versatile biologically mimetic membrane model, which in many cases is preferable to micelles, both because of the range of lipids and amphiphiles that may be combined and because radius of curvature effects and strain effects common with micelles may be avoided. Drug and small molecule binding and partitioning studies should benefit from their application in fast tumbling bicelles, tailored to mimic specific membranes. A wide range of topology and immersion depth studies have been shown to be effective in fast tumbling bicelles, while residual dipolar couplings add another dimension to structure refinement possibilities, particularly for situations in which the peptide is uniformly labeled with 15N and 13C. Solid state NMR studies of polytopic transmembrane proteins demonstrate that it is possible to express, purify, and reconstitute membrane proteins, ranging in size from single transmembrane domains to seven-transmembrane GPCRs, into bicelles. The line widths and quality of the resulting 15NH dipole-15N chemical shift spectra demonstrate that there are no insurmountable obstacles to the study of large membrane proteins in magnetically aligned media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".