Molecular Simulations of Lipid Flip-Flop in the Presence of Model Transmembrane Helices
Bibliographic record
Abstract
The transport of lipids between membrane leaflets, also known as flip-flop, is a key process in regulating the lipid composition of biological membranes. It is also important for the growth of biogenic membranes that are the site for lipid synthesis. It has been shown that the mere presence of transmembrane alpha-helical peptides or proteins enhances the rate lipid flip-flop [Kol et al. (2001) Biochemistry 40, 10500-10506]. Using computational models of natural phospholipids with different headgroups, we calculated the free energy profiles for transferring single phospholipids from bulk water to the center of a dioleylphosphatidylcholine (DOPC) bilayer in the presence of transmembrane helices. The free energy barrier for phosphatidylethanolamine (PE) and phosphatidylglycerol (PG) flip-flop decreased by a few kilojoules per mole when a WALP23 or KALP23 peptide was present in the membrane, while the barrier for PC was not affected. We observed large bilayer deformations during lipid flip-flop when the hydrophilic headgroup is in the hydrophobic interior of the bilayer. The presence of KALP23 or WALP23 decreased the size and stability of these defects, suggesting integral membrane proteins affect the mechanism of flip-flop. There was a large decrease in the free energy of desorption for PE and PG when transmembrane peptides were present. This suggests specific PE and PG interactions with the peptide have a large affect on their stability in the membrane, with implications on cellular lipid and protein trafficking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".