The Addition of a Synthetic LPS-Targeting Domain Improves Serum Stability While Maintaining Antimicrobial, Antibiofilm, and Cell Stimulating Properties of an Antimicrobial Peptide
Bibliographic record
Abstract
Multi-drug resistant (MDR) bacteria and their biofilms are a concern in veterinary and human medicine. Protegrin-1 (PG-1), a potent antimicrobial peptide (AMP) with antimicrobial and immunomodulatory properties, is considered a potential alternative for conventional antibiotics. AMPs are less stable and lose activity in the presence of physiological fluids, such as serum. To improve stability of PG-1, a hybrid peptide, SynPG-1, was designed. The antimicrobial and antibiofilm properties of PG-1 and the PG-1 hybrid against MDR pathogens was analyzed, and activity after incubation with physiological fluids was compared. The effects of these peptides on the IPEC-J2 cell line was also investigated. While PG-1 maintained some activity in 25% serum for 2 h, SynPG-1 was able to retain activity in the same condition for up to 24 h, representing a 12-fold increase in stability. Both peptides had some antibiofilm activity against Escherichia coli and Salmonella typhimurium. While both peptides prevented biofilm formation of methicillin-resistant Staphylococcus aureus (MRSA), neither could destroy MRSA’s pre-formed biofilms. Both peptides maintained activity after incubation with trypsin and porcine gastric fluid, but not intestinal fluid, and stimulated IPEC-J2 cell migration. These findings suggest that SynPG-1 has much better serum stability while maintaining the same antimicrobial potency as PG-1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".