Self-Assembly of Antimicrobial Peptoids Impacts Their Biological Effects on <i>ESKAPE</i> Bacterial Pathogens
Bibliographic record
Abstract
Antimicrobial peptides (AMPs) are promising pharmaceutical candidates for the prevention and treatment of infections caused by multidrug-resistant ESKAPE pathogens, which are responsible for the majority of hospital-acquired infections.Clinical translation of AMPs has been limited, in part by apparent toxicity on systemic dosing and by instability arising from susceptibility to proteolysis.Peptoids (sequence-specific oligo-Nsubstituted glycines) resist proteolytic digestion and thus are of value as AMP mimics.Only a few natural AMPs such as LL-37 and polymyxin self-assemble in solution; whether antimicrobial peptoids mimic these properties has been unknown.Here, we examine the antibacterial efficacy and dynamic self-assembly in aqueous media of eight peptoid mimics of cationic AMPs designed to self-assemble and two nonassembling controls.These amphipathic peptoids self-assembled in different ways, as determined by small-angle X-ray scattering; some adopt helical bundles, while others form core-shell ellipsoidal or worm-like micelles.Interestingly, many of these peptoid assemblies show promising antibacterial, antibiofilm activity in vitro in media, under hostmimicking conditions and antiabscess activity in vivo.While self-assembly correlated overall with antibacterial efficacy, this correlation was imperfect.Certain self-assembled morphologies seem better-suited for antibacterial activity.In particular, a peptoid exhibiting a high fraction of long, worm-like micelles showed reduced antibacterial, antibiofilm, and antiabscess activity against ESKAPE pathogens compared with peptoids that form ellipsoidal or bundled assemblies.This is the first report of self-assembling peptoid antibacterials with activity against in vivo biofilm-like infections relevant to clinical medicine.
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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) | 0.002 | 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".