Divalent Calcium Ions Inhibit the Penetration of Protamine through the Polysaccharide Brush of the Outer Membrane of Gram-Negative Bacteria
Bibliographic record
Abstract
Protamine is a cationic antimicrobial peptide, which inhibits or kills a number of Gram-negative bacteria, including Pseudomonas aeruginosa and Escherichia coli . Electrostatic interactions between the outer leaflet of the membrane and protamine are thought to be important for the antimicrobial effect. We hypothesized that divalent ions would compete with protamine for binding to the charged O-sidechain of the liposaccharide and expel protamine from the O-sidechains. Experimentally it was shown that increasing concentrations of divalent cations (Ca 2+ and Mg 2+ ) reduced the antimicrobial effect of protamine on P. aeruginosa PA01 and E. coli . We also modeled the electrostatic interactions between five protamine Y1 molecules from Atlantic herring and the surface of a Gram-negative bacterium possessing charged O-sidechains of the B-band lipopolysaccharides of P. aeruginosa PA01 in the presence/absence of calcium ions in an aqueous solution described by linearized Poisson−Boltzmann theory with Debye screening lengths κ - 1 of 1.0 nm (∼100 mM) and 3.33 nm (∼10 mM). Our conclusions are as follows. [1] A high concentration of calcium ions brought about a slight polysaccharide chain collapse. The calcium ions formed dynamic bridges between the negatively charged O-sidechains on time scales comparable to that of polymer motion. [2] Without the presence of added calcium, all five protamine molecules were trapped in the charged polysaccharide O-sidechain. The probability of finding segments of protamine molecules closer than ∼0.5 nm to the membrane plane (the x − y plane at z = 0) was effectively zero. [3] Both the calcium distribution and the protamine distribution, when present separately, were essentially independent of monovalent ionic concentration for both values of κ. [4] When calcium and protamine were present simultaneously, the effects depended strongly upon the monovalent ion concentration. Added calcium effectively prevented protamine from entering the O-sidechain brush. Conclusions 3 and 4 show that the minimum inhibitory concentration should depend on monovalent ion concentration since complex growth media contain multivalent ions. [5] Our experiments confirmed our theoretical prediction that the addition of Ca 2+ significantly reduced the inhibitory effect of protamine. This also confirmed the importance of electrostatic interactions during the first step in protamine's antibacterial mode of action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".