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 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".