Curvature-based sorting of eight lipid types in asymmetric buckled plasma membrane models
Bibliographic record
Abstract
Curvature is a fundamental property of biological membranes and has essential roles in cellular function. Bending of membranes can be induced by their lipid and protein compositions, as well as peripheral proteins, such as those that make up the cytoskeleton. An important aspect of membrane function is the grouping of lipid species into microdomains, or rafts, which serve as platforms for specific biochemical processes. The fluid mosaic model of membranes has evolved to recognize the importance of curvature and leaflet asymmetry, and there are efforts toward evaluating their functional roles. This work investigates the effect of curvature on the sorting of lipids in buckled asymmetric bilayers containing eight lipid types, approximating an average mammalian plasma membrane, through coarse-grained (CG) molecular dynamics (MD) simulations with the Martini force field. The simulations reveal that 1) leaflet compositional asymmetry can induce curvature asymmetry, 2) lipids are sorted by curvature to different extents, and 3) curvature-based partitioning trends show moderate to strong correlations with lipid molecular volumes and head to tail bead ratios, respectively. The findings provide unique insights into the role of curvature in membrane organization, and the curvature-based sorting trends should be useful references for later investigations and potentially interpreting the functional roles of specific lipids.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".