Molecular View of Cholesterol Flip-Flop and Chemical Potential in Different Membrane Environments
Bibliographic record
Abstract
The relative stability of cholesterol in cellular membranes and the thermodynamics of fluctuations from equilibrium have important consequences for sterol trafficking and lateral domain formation. We used molecular dynamics computer simulations to investigate the partitioning of cholesterol in a systematic set of lipid bilayers. In addition to atomistic simulations, we undertook a large set of coarse grained simulations, which allowed longer time and length scales to be sampled. Our results agree with recent experiments (Steck, T. L.; et al. Biophys. J. 2002, 83, 2118-2125) that the rate of cholesterol flip-flop can be fast on physiological time scales, while extending our understanding of this process to a range of lipids. We predicted that the rate of flip-flop is strongly dependent on the composition of the bilayer. In polyunsaturated bilayers, cholesterol undergoes flip-flop on a submicrosecond time scale, while flip-flop occurs in the second range in saturated bilayers with high cholesterol content. We also calculated the free energy of cholesterol desorption, which can be equated to the excess chemical potential of cholesterol in the bilayer compared to water. The free energy of cholesterol desorption from a DPPC bilayer is 80 kJ/mol, compared to 67 kJ/mol for a DAPC bilayer. In general, cholesterol prefers more ordered and rigid bilayers and has the lowest affinity for bilayers with two polyunsaturated chains. Overall, the simulations provide a detailed molecular level thermodynamic description of cholesterol interactions with lipid bilayers, of fundamental importance to eukaryotic life.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".