Antifungal and Antibiofilm Activities and the Mechanism of Action of Repeating Lysine-Tryptophan Peptides against Candida albicans
Bibliographic record
Abstract
The rapid increase in the emergence of antifungal-resistant Candida albicans strains is becoming a serious health concern. Because antimicrobial peptides (AMPs) may provide a potential alternative to conventional antifungal agents, we have synthesized a series of peptides with a varying number of lysine and tryptophan repeats (KWn-NH2). The antifungal activity of these peptides increased with peptide length, but only the longest KW5 peptide displayed cytotoxicity towards a human keratinocyte cell line. The KW4 and KW5 peptides exhibited strong antifungal activity against C. albicans, even under conditions of high-salt and acidic pH, or the addition of fungal cell wall components. Moreover, KW4 inhibited biofilm formation by a fluconazole-resistant C. albicans strain. Circular dichroism and fluorescence spectroscopy indicated that fungal liposomes could interact with the longer peptides but that they did not release the fluorescent dye calcein. Subsequently, fluorescence assays with different dyes revealed that KW4 did not disrupt the membrane integrity of intact fungal cells. Scanning electron microscopy showed no changes in fungal morphology, while laser-scanning confocal microscopy indicated that KW4 can localize into the cytosol of C. albicans. Gel retardation assays revealed that KW4 can bind to fungal RNA as a potential intracellular target. Taken together, our data indicate that KW4 can inhibit cellular functions by binding to RNA and DNA after it has been translocated into the cell, resulting in the eradication of C. albicans.
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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) | 0.000 | 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 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".