PS membrane asymmetry influences the folding and insertion of a transmembrane helix
Bibliographic record
Abstract
Abstract The plasma membrane (PM) contains an asymmetric distribution of lipids between the inner and outer leaflets of its bilayer. A lipid of special interest in eukaryotic cells is the negatively charged phosphatidylserine (PS). In healthy cells, PS is actively sequestered to the inner leaflet of the PM but can redistribute to the outer leaflet when the cell is damaged or at the onset of apoptosis. The influence of PS asymmetry and its loss on membrane protein structure and organization have not been widely addressed. Marginally hydrophobic membrane proteins contain acidic residues in their transmembrane sequence, which can enable topological transitions after membrane association. The pH low insertion peptide (pHLIP), which undergoes a topological reorientation and inserts into the membrane at acidic pH – as its name implies, is a useful and well-characterized model for studying these transitions. Although it is known that the inclusion of PS in symmetric vesicles affects the membrane insertion process of pHLIP by lowering the pH midpoint of insertion, it is unclear how PS asymmetry influences these topological transitions. Here, we studied pHLIP’s topology using freely-floating asymmetric phosphatidylcholine (PC)/PS vesicles with PS enriched in the inner leaflet. We developed a modified protocol to create asymmetric vesicles containing PS and employed Annexin V labeled with an Alexa 568 fluorophore as a new probe to quantifying PS asymmetry. For pHLIP, membrane insertion was affected by the surface charge difference between bilayer leaflets caused by the asymmetric distribution of charged lipids between the leaflets. We thus conclude that lipid asymmetry can have consequences for the behavior of membrane-associated proteins. A corollary is that model studies using symmetric bilayers to mimic the PM may fail to capture important aspects of protein-membrane 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) | 0.001 | 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".