Interactions between Ether Phospholipids and Cholesterol As Determined by Scattering and Molecular Dynamics Simulations
Bibliographic record
Abstract
Cholesterol and ether lipids are ubiquitous in mammalian cell membranes, and their interactions are crucial in ether lipid mediated cholesterol trafficking. We report on cholesterol's molecular interactions with ether lipids as determined using a combination of small-angle neutron and X-ray scattering, and all-atom molecular dynamics (MD) simulations. A scattering density profile model for an ether lipid bilayer was developed using MD simulations, which was then used to simultaneously fit the different experimental scattering data. From analysis of the data the various bilayer structural parameters were obtained. Surface area constrained MD simulations were also performed to reproduce the experimental data. This iterative analysis approach resulted in good agreement between the experimental and simulated form factors. The molecular interactions taking place between cholesterol and ether lipids were then determined from the validated MD simulations. We found that in ether membranes cholesterol primarily hydrogen bonds with the lipid headgroup phosphate oxygen, while in their ester membrane counterparts cholesterol hydrogen bonds with the backbone ester carbonyls. This different mode of interaction between ether lipids and cholesterol induces cholesterol to reside closer to the bilayer surface, dehydrating the headgroup's phosphate moiety. Moreover, the three-dimensional lipid chain spatial density distribution around cholesterol indicates anisotropic chain packing, causing cholesterol to tilt. These insights lend a better understanding of ether lipid-mediated cholesterol trafficking and the roles that the different lipid species have in determining the structural and dynamical properties of membrane associated biomolecules.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".