Position-Dependent Hydrophobicity of the Antimicrobial Magainin Peptide Affects the Mode of Peptide−Lipid Interactions and Selective Toxicity
Bibliographic record
Abstract
Cationic antimicrobial peptides are promising candidates as novel antibiotics of clinical usefulness. Magainin 2, a representative antimicrobial peptide isolated from the skin of the African clawed frog Xenopus leavis, electrostatically recognizes anionic lipids that are abundant in bacterial membranes, forming a peptide-lipid supramolecular complex pore, whereas the peptide does not effectively bind to zwitterionic phospholipids constituting the outer leaflets of mammalian cell membranes because of the low hydrophobicity of the peptide [Matsuzaki, K. (1999) Biochim. Biophys. Acta 1462, 1-10]. In this study, two magainin analogues with enhanced hydrophobicity, MG-H1 (GIKKFLHIIWKFIKAFVGEIMNS) and MG-H2 (IIKKFLHSIWKFGKAFVGEIMNI), with identical amino acid compositions were designed and interactions with lipid bilayers and biological activities were examined in comparison with those of MG (GIGKWLHSAKKFGKAFVGEIMNS = F5W-magainin 2). The apparent hydrophobicities and hydrophobic moments of MG-H1 and MG-H2, conventionally calculated assuming that all residues are involved in helix formation, were almost the same. MG-H2 behaved like MG except for greatly enhanced activity against zwitterionic membranes and erythrocytes. In contrast, despite a very similar calculated hydrophobicity, the observed hydrophobicity of MG-H1 was larger than that of MG-H2 because of a tendency toward helix fraying near the termini. Therefore, the physicochemical parameters of only the helical portion should be considered in characterizing peptide-lipid interactions, although this point was overlooked in most studies. Moreover, MG-H1 induced aggregation and/or fusion of negatively charged membranes. Furthermore, the peptide hydrophobicity was found to affect pore formation rate, pore size, and pore stability. These observations demonstrate that the hydrophobicity of the peptide also controls the mode of action and is dependent on the position of the hydrophobic amino acids in the peptide sequence.
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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".