Hydrophobic Interactions in Complexes of Antimicrobial Peptides with Bacterial Polysaccharides
Bibliographic record
Abstract
Biofilms of Pseudomonas aeruginosa are responsible for chronic lung infections in cystic fibrosis patients, where they are characterized by overproduction of the exopolysaccharide alginate and are recalcitrant to treatment with conventional antibiotics. Cationic antimicrobial peptides (CAPs) are potential alternatives for the treatment of multi-drug-resistant P. aeruginosa. However, alginate in P. aeruginosa biofilms has been proposed to bind these peptides through hydrophobic interactions, consequently reducing their activity [Chan et al., J Biol Chem 2004; 279: 38749-38754]. Here we perform biophysical analyses of the interactions of alginate with a series of novel peptide antibiotics (alpha-CAPs) of prototypic sequence KK-AAAXAAAAAXAAWAAXAAA-KKKK (where X = Phe, Trp or Leu). The hydrophobic interaction interface in alginate was investigated by examining (i) the effects of polysaccharide composition with respect to D-mannuronate and L-guluronate content; (ii) glycan chain length; (iii) alpha-CAP Trp fluorescence; and (iv) 1-anilinonaphthalene-8-sulfonate fluorescence. The results show that, while M and G residues produce equivalent effects, hydrophobic interactions between alginate and alpha-CAPs require a minimal glycan chain length. Peptide interactions with alginate are deduced to be mediated by hydrophobic microdomains comprised of pyranosyl C-H groups that are inducible upon formation of alpha-CAP-alginate complexes due to charge neutralization between the two species.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".