Influence of <i>cis</i> double-bond parametrization on lipid membrane properties: How seemingly insignificant details in force-field change even qualitative trends
Bibliographic record
Abstract
We have employed atomistic molecular dynamics simulations to investigate the effect of double-bond parametrization on lipid membrane properties. As models, we use one-component membranes composed of glycerol-based phosphatidylcholines (PCs) with monounsaturated acyl chains, and we complement these studies by additional PC/cholesterol simulations. We compare differences between double-bond parametrizations by varying the position of the double bond systematically along the lipid hydrocarbon chains. The results give rise for concern: They indicate that the double-bond description may change not only the quantitative but also the qualitative nature of membrane behavior. In particular, we find that the double-bond description which accounts for skew states in the vicinity of a double bond predicts a maximum in membrane disorder, when the double bond resides at the middle of an acyl chain, in agreement with experiments. The more commonly used description which does not accommodate skew states, however, predicts membrane disorder to decrease monotonically as the double bond is shifted from the glycerol backbone to the end of an acyl chain. The results highlight the importance of properly describing double bonds especially in many-component membranes, where the interplay of different molecule types is difficult to predict on intuitive grounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".