Thermodynamic Analysis of the Effect of Cholesterol on Dipalmitoylphosphatidylcholine Lipid Membranes
Bibliographic record
Abstract
Cholesterol is an important component of eukaryotic cellular membranes. Despite extensive literature on the physiochemical effects of cholesterol on membranes, much remains unknown about the precise role of cholesterol and its molecular interactions in membranes. Regular thermal fluctuations of lipids normal to the plane of the membrane are biologically relevant for many processes, such as interactions with enzymes, elastic properties, and hydrophobic matching, while larger fluctuations are involved in vesicle budding and fusion, passive lipid flip-flop, and pore formation. Here we used molecular dynamics simulations to investigate the thermodynamic effect of the cholesterol concentration on dipalmitoylphosphatidylcholine (DPPC) bilayers. We calculated the potentials of mean force for DPPC partitioning in DPPC bilayers containing 20 and 40 mol % cholesterol. Increasing the cholesterol content increases the free energy barrier for transferring the headgroup of DPPC to the center of the bilayer and slows the rate of DPPC flip-flop by orders of magnitude. Cholesterol increases the order, thickness, and rigidity of the bilayers, which restricts bilayer deformations and prevents pore formation. While DPPC flip-flop is pore-mediated in a pure bilayer, we do not observe pores in the 20 and 40 mol % bilayers. Increasing the cholesterol concentration causes a decrease in the free energy to transfer DPPC from its equilibrium position into bulk waterindicating that DPPC prefers to be in cholesterol-free bilayers. We also observe a reduction in small fluctuations of DPPC normal to the bilayer as the cholesterol concentration is increased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".