Controlling Nanonet Morphology via Residue‐Specific Modulation of β‐Hairpin Peptide for Enhanced Bacterial Trapping
Bibliographic record
Abstract
Abstract Precise control over peptide nanonet architecture is instrumental in advancing the development of antibacterial nanonets. Here, a novel design strategy is presented to control bacteria nanonet morphology through rational modification of the β‐hairpin side strands, leveraging the unique chemical properties of amino acid side chains. By fine‐tuning both the termini and aromaticity of the hydrophobic residue, the W‐W 13 peptide is engineered to form increased nanofibers interweaving on bacterial surfaces, forming a tightly interwoven nanonet that effectively traps and kills both E. coli and S. aureus . In contrast, asymmetric glutamic acid substitutions on the cationic residues of the E‐E 13 ASYM peptide redirect the nanofibers to self‐interweave, forming extensive nanonets with minimal bacterial coverage and no antibacterial activity. Using these nanonets with distinct morphologies and function, it is demonstrated that the formation of tightly interwoven nanonets on bacterial surfaces significantly reduces the spread of motile E. coli and P. aeruginosa , outperforming both loosely trapping nanonets and conventional potent antibiotics. The findings pave the way for the development of novel peptide‐based nanonets, offering a promising strategy to target bacterial motility and prevent spreading of bacteria.
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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".