Rapid Assembly of Infection-Resistant Coatings: Screening and Identification of Antimicrobial Peptides Works in Cooperation with an Antifouling Background
Bibliographic record
Abstract
Bacterial adhesion and the succeeding biofilm formation onto surfaces are responsible for implant- and device-associated infections. Bifunctional coatings integrating both nonfouling components and antimicrobial peptides (AMPs) are a promising approach to develop potent antibiofilm coatings. However, the current approaches and chemistry for such coatings are time-consuming and dependent on substrates and involve a multistep process. Also, the information is limited on the influence of the coating structure or its components on the antibiofilm activity of such AMP-based coatings. Here, we report a new strategy to rapidly assemble a stable, potent, and substrate-independent AMP-based antibiofilm coating in a nonfouling background. The coating structure allowed for the screening of AMPs in a relevant nonfouling background to identify optimal peptide combinations that work in cooperation to generate potent antibiofilm activity. The structure of the coating was changed by altering the organization of the hydrophilic polymer chains within the coatings. The coatings were thoroughly characterized using various surface analytical techniques and correlated with the efficiency to prevent biofilm formation against diverse bacteria. The coating method that allowed the conjugation of AMPs without altering the steric protection ability of hydrophilic polymer structure results in a bifunctional surface coating with excellent antibiofilm activity. In contrast, the conjugation of AMPs directly to the hydrophilic polymer chains resulted in a surface with poor antibiofilm activity and increased adhesion of bacteria. Using this coating approach, we further established a new screening method and identified a set of potent surface-tethered AMPs with high activity. The success of this new peptide screening and coating method is demonstrated using a clinically relevant mouse infection model to prevent catheter-associated urinary tract infection (CAUTI).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".