Peptide-Induced Lipid Flip-Flop in Asymmetric Liposomes Measured by Small Angle Neutron Scattering
Bibliographic record
Abstract
Despite the prevalence of lipid transbilayer asymmetry in natural plasma membranes, most biomimetic model membranes studied are symmetric. Recent advances have helped to overcome the difficulties in preparing asymmetric liposomes in vitro, allowing for the examination of a larger set of relevant biophysical questions. Here, we investigate the stability of asymmetric bilayers by measuring lipid flip-flop with time-resolved small-angle neutron scattering (SANS). Asymmetric large unilamellar vesicles with inner bilayer leaflets containing predominantly 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and outer leaflets composed mainly of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) displayed slow spontaneous flip-flop at 37 ◦C (half-time, t1/2 = 140 h). However, inclusion of peptides, namely, gramicidin, alamethicin, melittin, or pHLIP (i.e., pH-low insertion peptide), accelerated lipid flip-flop. For three of these peptides (i.e., pHLIP, alamethicin, and melittin), each of which was added externally to preformed asymmetric vesicles, we observed a completely scrambled bilayer in less than 2 h. Gramicidin, on the other hand, was preincorporated during the formation of the asymmetric liposomes and showed a time resolvable 8-fold increase in the rate of lipid asymmetry loss. These results point to a membrane surface-related (e.g., adsorption/insertion) event as the primary driver of lipid scrambling in the asymmetric model membranes of this study. We discuss the implications of membrane peptide binding, conformation, and insertion on lipid asymmetry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".