The PhoQ-Activating Potential of Antimicrobial Peptides Contributes to Antimicrobial Efficacy and Is Predictive of the Induction of Bacterial Resistance
Bibliographic record
Abstract
Antimicrobial peptides (AMPs) are among the leading candidates to replace antibiotics which have been rendered ineffective by the evolution of resistant bacterial strains. Concerns do exist, however, that the therapeutic administration of AMPs may also select for resistant strains but with much more dire consequences, as these peptides represent an endogenous and essential component of host immune defense. The recent demonstration that AMPs function as ligands for the bacterial sensory kinase PhoQ for the initiation of virulence and adaptive responses lends credence to these concerns. While the ability to serve as PhoQ ligands suggests that the therapeutic administration of AMPs could (i) exacerbate infections by promoting bacterial virulence and (ii) select resistant mutants by encouraging adaptive behaviors, it also provides a rational basis for AMP selection and optimization. Here, we demonstrate that derivatives of a representative AMP have differential abilities to serve as PhoQ ligands and that this correlates with the ability to induce bacterial adaptive responses. We propose that PhoQ-activating potential is a logical parameter for AMP optimization and introduce a novel strategy for the treatment of minimal bactericidal concentration data that permits the discrimination and quantification of the contributions of PhoQ-activating potential and direct antimicrobial activity to net antimicrobial efficiency.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".