Self-association of Transmembrane α-Helices in Model Membranes
Bibliographic record
Abstract
Interactions between transmembrane helices play a key role in almost all cellular processes involving membrane proteins. We have investigated helix-helix interactions in lipid bilayers with synthetic tryptophan-flanked peptides that mimic the membrane spanning parts of membrane proteins. The peptides were functionalized with pyrene to allow the self-association of the helices to be monitored by pyrene fluorescence and Trp-pyrene fluorescence resonance energy transfer (FRET). Specific labeling of peptides at either their N or C terminus has shown that helix-helix association occurs almost exclusively between antiparallel helices. Furthermore, computer modeling suggested that antiparallel association arises primarily from the electrostatic interactions between alpha-helix backbone atoms. We propose that such interactions may provide a force for the preferentially antiparallel association of helices in polytopic membrane proteins. Helix-helix association was also found to depend on the lipid environment. In bilayers of dioleoylphosphatidylcholine, in which the hydrophobic length of the peptides approximately matched the bilayer thickness, association between the helices was found to require peptide/lipid ratios exceeding 1/25. Self-association of the helices was promoted by either increasing or decreasing the bilayer thickness, and by adding cholesterol. These results indicate that helix-helix association in membrane proteins can be promoted by unfavorable protein-lipid interactions.
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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".