Water Defect and Pore Formation in Atomistic and Coarse-Grained Lipid Membranes: Pushing the Limits of Coarse Graining
Bibliographic record
Abstract
Defects in lipid bilayers are important in a range of biological processes, including interactions between antimicrobial peptides and membranes, transport of polar molecules (including drugs) across membranes, and lipid flip-flop from one monolayer to the other. Passive lipid flip-flop and the translocation of polar molecules across lipid membranes occur on a slow time scale because of high-energy intermediates involving water defects and pores in the membrane. Such defects are an interesting test case for coarse-grained models because of their relatively small characteristic size at the level of water molecules and the complex environment of water and polar head groups in a low-dielectric membrane interior. Here we compare coarse-grained simulations with the MARTINI model with the standard MARTINI water and two recently developed coarse-grained polarizable water models to atomistic simulations. Although in several cases the MARTINI model reproduces the correct free energies, there are structural differences between the atomistic and coarse-grained models. The polarizable water model improves the free energies but only moderately improves the structures. Atomistic test simulations in which water molecules are artificially tethered to each other in groups of four, the resolution of MARTINI, suggest that the limiting factor is not the size of the coarse-grained particles but rather the simple interaction potential and/or the entropy lost in coarse graining the system. By increasing the attractive interaction between the lipids' headgroup and water, we did observe pore formation but at the expense of the correct equilibrium properties of the bilayers.
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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) | 0.000 | 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 | 0.001 |
| 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".