Lipid Bilayer Structure Refinement with SAXS/SANS Based Restrained Ensemble Molecular Dynamics
Bibliographic record
Abstract
Small-angle scattering is a powerful technique that can probe the structure of lipid bilayers on the nanometer scale. Retrieving the real space structure of lipid bilayers from the scattering intensity can be a challenging task, as their fluid nature results in a liquid-like scattering pattern which is hard to interpret. The standard approach to this problem is to describe the bilayer structure as a sum of density distributions of separate components of the lipid molecule and then to fit the parameters of the distributions against experimental data. The accuracy of the density-based analysis is partially limited by the choice of the functions used to describe component distributions, especially in the case of multi-component bilayers. The number of parameters in the model is balanced by the need for an accurate description of the underlying bilayer structure and the risk of overfitting the data. Here, we present an alternative method for the interpretation of small-angle scattering intensity data for lipid bilayers. The method is based on restrained ensemble molecular dynamics simulations that allow direct incorporation of the scattering data into the simulations in the form of a restraining potential. This approach combines the information implicitly contained in the simulation force field with structural data from the scattering intensity and is free from prior assumptions regarding the bilayer structure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".