Effects of Hydrophobicity on the Antifungal Activity of α‐Helical Antimicrobial Peptides
Bibliographic record
Abstract
We utilized a series of analogs of D-V13K (a 26-residue amphipathic alpha-helical antimicrobial peptide, denoted D1) to compare and contrast the role of hydrophobicity on antifungal and antibacterial activity to the results obtained previously with Pseudomonas aeruginosa strains. Antifungal activity for zygomycota fungi decreased with increasing hydrophobicity (D-V13K/A12L/A20L/A23L, denoted D4, the most hydrophobic analog was sixfold less active than D1, the least hydrophobic analog). In contrast, antifungal activity for ascomycota fungi increased with increasing hydrophobicity (D4, the most hydrophobic analog was fivefold more active than D1). Hemolytic activity is dramatically affected by increasing hydrophobicity with peptide D4 being 286-fold more hemolytic than peptide D1. The therapeutic index for peptide D1 is 1569-fold and 62-fold better for zygomycota fungi and ascomycota fungi, respectively, compared with peptide D4. To reduce the hemolytic activity of peptide D4 and improve/maintain the antifungal activity of D4, we substituted another lysine residue in the center of the non-polar face (V16K) to generate D5 (D-V13K/V16K/A12L/A20L/A23L). This analog D5 decreased hemolytic activity by 13-fold, enhanced antifungal activity to zygomycota fungi by 16-fold and improved the therapeutic index by 201-fold compared with D4 and represents a unique approach to control specificity while maintaining high hydrophobicity in the two hydrophobic segments on the non-polar face of D5.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".