Electrostatic Bending of Lipid Membranes: How Are Lipid and Electrostatic Properties Interrelated?
Bibliographic record
Abstract
Electrostatic modification of lipid headgroups and its effect on membrane curvature are not only relevant in a variety of contexts such as cell shape transformation and membrane tubulation but also are intriguingly implicated in membrane functions. For instance, the gating (open vs closed) properties of mechanosensitive channels can be influenced by membrane curvature and ion valence. However, a full theoretical description of membrane electrostatics is still lacking; in the past, membrane bending has often been considered under a few assumptions about how bending modifies lipid arrangements and surface charges. Here, we present a unified theoretical approach to spontaneous membrane curvature, C(0), in which lipid properties (e.g., packing shape) and electrostatic effects are self-consistently integrated. For the description of electrostatic interactions, especially between a lipid charge and a divalent counterion, we implement the Poisson-Boltzmann (PB) approach by incorporation of finite ionic sizes, so as to capture both lateral and transverse charge correlations on the membrane surface. Our results show that C(0) is sensitive to the way lipid rearrangements and divalent counterions are modeled. Interestingly, it can change its sign in the presence of divalent counterions, thus stabilizing reverse hexagonal (H(II)) phases. Our results show how electrostatic modification of headgroups influences the preferred structure of lipid aggregates (inverted micelles vs 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".