Electrostatic modification of the lipopolysaccharide layer: competing effects of divalent cations and polycationic or polyanionic molecules
Bibliographic record
Abstract
The outer membrane (OM) of Gram-negative bacteria is asymmetrical with its outer layer mainly populated with polyanionic lipopolysaccharide (LPS). Much empirical evidence shows how OM permeability can be altered electrostatically: if Mg(2+) or divalent cations are required for the integrity of the OM, antimicrobial peptides (AMPs) or ethylene-diaminetetraacetic acid (EDTA) can permeabilize it. Using a coarse-grained model of the outer LPS layer, in which the layer is viewed as forming discrete binding sites for opposite charges, we study how the LPS layer can be modified electrostatically. In particular, we capture systematically ion-pairing and lateral-charge correlations on the LPS layer. Our results offer a clear picture of (competitive) ion binding onto the LPS layer and its impact on the lateral packing of LPS molecules, similarly to what has been seen in experiments: divalent cations such as Mg(2+) not only neutralize the LPS layer but also make its planar charge distribution heterogeneous, thus tightening the LPS layer; on the other hand, polycationic AMPs or polyanionic EDTA can displace Mg(2+) ions from the LPS layer and counteract the favorable effect of Mg(2+). Our result will be useful for clarifying to what extent OM permeability can be modified electrostatically.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".