Design and Characterization of a Membrane Protein Unfolding Platform in Lipid Bilayers
Bibliographic record
Abstract
Accurate measurement of membrane protein stability--and particularly how it may vary as a result of disease-phenotypic mutations--ideally requires a denaturant that can unfold a membrane-embedded structure while leaving the solubilizing environment unaffected. The steric trap method fulfills this requirement by using monovalent streptavidin (mSA) molecules to unfold membrane proteins engineered with two spatially close biotin tags. Here we adapted this method to an 87-residue helix-loop-helix (hairpin) construct derived from helices 3 and 4 in the transmembrane domain of the human cystic fibrosis transmembrane conductance regulator (CFTR), wherein helix-helix tertiary interactions are anticipated to confer a portion of construct stability. The wild type CFTR TM3/4 hairpin construct was modified with two accessible biotin tags for mSA-induced unfolding, along with two helix-terminal pyrene labels to monitor loss of inter-helical contacts by pyrene excimer fluorescence. A series of eight constructs with biotin tags at varying distances from the helix-terminal pyrene labels were expressed, purified and labeled appropriately; all constructs exhibited largely helical circular dichroism spectra. We found that addition of mSA to an optimized construct in lipid vesicles led to a complete and reversible loss in pyrene excimer fluorescence and mSA binding, and hence hairpin unfolding--results further supported by SDS-PAGE visualization of mSA bound and unbound species. While some dimeric/oligomeric populations persist that may affect quantitation of the unfolding step, our characterization of the design yields a promising prototype of a future platform for the systematic study of membrane protein folding in a lipid bilayer environment.
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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) | 0.000 | 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 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".