Cholesterol Induces Specific Spatial and Orientational Order in Cholesterol/Phospholipid Membranes
Bibliographic record
Abstract
BACKGROUND: In lipid bilayers, cholesterol facilitates the formation of the liquid-ordered phase and enables the formation of laterally ordered structures such as lipid rafts. While these domains have an important role in a variety of cellular processes, the precise atomic-level mechanisms responsible for cholesterol's specific ordering and packing capability have remained unresolved. METHODOLOGY/PRINCIPAL FINDINGS: Our atomic-scale molecular dynamics simulations reveal that this ordering and the associated packing effects in membranes largely result from cholesterol's molecular structure, which differentiates cholesterol from other sterols. We find that cholesterol molecules prefer to be located in the second coordination shell, avoiding direct cholesterol-cholesterol contacts, and form a three-fold symmetric arrangement with proximal cholesterol molecules. At larger distances, the lateral three-fold organization is broken by thermal fluctuations. For other sterols having less structural asymmetry, the three-fold arrangement is considerably lost. CONCLUSIONS/SIGNIFICANCE: We conclude that cholesterol molecules act collectively in lipid membranes. This is the main reason why the liquid-ordered phase only emerges for Chol concentrations well above 10 mol% where the collective self-organization of Chol molecules emerges spontaneously. The collective ordering process requires specific molecular-scale features that explain why different sterols have very different membrane ordering properties: the three-fold symmetry in the Chol-Chol organization arises from the cholesterol off-plane methyl groups allowing the identification of raft-promoting sterols from those that do not promote rafts.
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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 | 0.000 | 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) | 0.001 | 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".