Biomembrane solubilization mechanism by Triton X-100: a computational study of the three stage model
Bibliographic record
Abstract
The solubilization mechanism of lipid membranes in the presence of Triton X-100 (TX-100) is investigated at molecular resolution using molecular dynamics (MD) simulations. Thanks to the large time and length scales accessible by the hybrid particle-field formulation of the models employed here, the complex process of membrane solubilization has been studied, with the goal of verifying the three stage model reported in the literature. DPPC lipid bilayers and vesicles have been studied at different concentrations of the TX-100 detergent employing coarse grained (CG) models. Systems up to ∼600.000 beads, corresponding to more than 2 millions heavy atoms, have been simulated. Moreover, in order to clarify several experimental pieces of evidence, both slow and fast detergent partition scenarios have been investigated. Flat and curved (vesicles) lipid bilayer surfaces, interacting with TX-100, have been considered to study the curvature effects on the detergent partition rate in the membrane. Shape and conformational changes of mixed DPPC/TX-100 vesicles, as a function of TX-100 content, have also been studied. In particular, high curvature surfaces, corresponding to a higher local TX-100 content, promote a membrane rupture. In flat lipid surfaces, on the time scale simulated the detergent partition is almost absent, following a different pathway of the solubilization membrane mechanism.
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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 | 0.002 | 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".