Cationic Dimyristoylphosphatidylcholine and Dioleoyloxytrimethylammonium Propane Lipid Bilayers: Atomistic Insight for Structure and Dynamics
Bibliographic record
Abstract
We performed atomistic molecular dynamics simulations of lipid bilayers consisting of a mixture of cationic dioleoyloxytrimethylammonium propane (DOTAP) and zwitterionic dimyristoylphosphatidylcholine (DMPC) lipids at different DOTAP fractions. Our primary focus was the specific effects of unsaturated lipid chains on structural and dynamic properties of mixed cationic bilayers. The bilayer area, as well as the ordering of lipid tails, shows a pronounced nonmonotonic behavior when TAP lipid fraction increases. The minimum in area (maximum in ordering) was observed for a bilayer with TAP fraction of 0.4, that is, at lower TAP fractions compared with saturated PC/TAP bilayers. Adding unsaturated DOTAP lipids into DMPC bilayers was found to promote lipid chain interdigitation and to fluidize lipid bilayers, as seen through enhanced lateral lipid diffusion. The speed-up in lateral diffusion at large DOTAP fractions results from increasing area per lipid, whereas at smaller DOTAP concentrations, the competing effect due to lipid-lipid complex formation results in a constant value for diffusion. We also characterize the lipid headgroup orientation and the interactions between DMPC and DOTAP lipids, which were found to form PC-PC and PC-TAP pairs, and the formation of lipid clusters.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".