Effects of Cholesterol on the Thermodynamics and Kinetics of Passive Transport of Water through Lipid Membranes
Bibliographic record
Abstract
While it has long been known that cholesterol reduces the permeability of biological membranes to water, the exact mechanism by which cholesterol influences transmembrane permeation is still unclear. The thermodynamic and kinetic contributions to the transport of water across mixed DPPC/cholesterol bilayers of different composition are thus examined by molecular dynamics simulations. Our analyses show that cholesterol decreases transmembrane permeability to water mainly by altering the thermodynamics of water transport. In particular, the free-energy barrier to permeation is magnified in the dense bilayer interior and the partitioning of water is significantly lowered. The changes are observed to correlate quantitatively well with the cholesterol-dependent density and thickness of the bilayers. In contrast, diffusion coefficients are relatively insensitive to cholesterol concentration, except in the sparsely populated center of the bilayer. Diffusion of water in cholesterol-containing bilayers appears to be related to changes in the free area in the middle of the bilayer and to the solute cross-sectional area in the denser hydrophobic regions. Overall, cholesterol is found to have an inhibitory effect on the permeation of water at all concentrations investigated, although bilayers containing cholesterol concentrations up to 20 mol % display a more dramatic dependence on cholesterol content than at higher concentrations. Our results show that it is possible to quantitatively reproduce the relative effects of cholesterol on lipid bilayer permeability from molecular dynamics simulations.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".