An engineered arginine-rich α-helical antimicrobial peptide exhibits broad-spectrum bactericidal activity against pathogenic bacteria and reduces bacterial infections in mice
Bibliographic record
Abstract
The increase in the prevalence of antibiotic-resistant bacteria has become a major public health concern. Antimicrobial peptides (AMPs) are emerging as promising candidates addressing this issue. In this study, we designed several AMPs by increasing α-helical contents and positive charges and optimizing hydrophobicity and amphipathicity in the Sushi 1 peptide from horseshoe crabs. A neural network-based bioinformatic prediction tool was used for the first stage evaluations of peptide properties. Among the peptides designed, Sushi-replacement peptide (SRP)-2, an arginine-rich and highly α-helical peptide, showed broad-spectrum bactericidal activity against both Gram-positive and Gram-negative bacteria, including methicillin-resistant Staphylococcus aureus and multidrug-resistant Acinetobacter baumannii; nevertheless, it showed little hemolytic and cytotoxic activity against mammalian cells. Atomic force microscopy results indicated that SRP-2 should interact directly with cell membrane components, resulting in bacterial cell death. SRP-2 also neutralized LPS-induced macrophage activation. Moreover, in an intraperitoneal multidrug-resistant A. baumannii infection mouse model, SRP-2 successfully reduced the bacterial number in ascitic fluid and tumor necrosis factor-α production. Our study findings demonstrate that bioinformatic calculations can be powerful tools to help design potent AMPs and that arginine is superior to lysine for providing positive charges for AMPs to exhibit better bactericidal activity and selectivity against bacterial cells.
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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".