Toward Infection-Resistant Surfaces: Achieving High Antimicrobial Peptide Potency by Modulating the Functionality of Polymer Brush and Peptide
Bibliographic record
Abstract
Bacterial infection associated with indwelling medical devices and implants is a major clinical issue, and the prevention or treatment of such infections is challenging. Antimicrobial coatings offer a significant step toward addressing this important clinical problem. Antimicrobial coatings based on tethered antimicrobial peptides (AMPs) on hydrophilic polymer brushes have been shown to be one of the most promising strategies to avoid bacterial colonization and have demonstrated broad spectrum activity. Optimal combinations of the functionality of the polymer-brush-tethered AMPs are essential to maintaining long-term AMP activity on the surface. However, there is limited knowledge currently available on this topic. Here we report the development of potent antimicrobial coatings on implant surfaces by elucidating the roles of polymer brush chemistry and peptide structure on the overall antimicrobial activity of the coatings. We screened several combinations of polymer brush coatings and AMPs constructed on nanoparticles, titanium surfaces, and quartz slides on their antimicrobial activity and bacterial adhesion against Gram-positive and Gram-negative bacteria. Highly efficient killing of planktonic bacteria by the antimicrobial coatings on nanoparticle surfaces, as well as potent killing of adhered bacteria in the case of coatings on titanium surfaces, was observed. Remarkably, the antimicrobial activity of AMP-conjugated brush coatings demonstrated a clear dependence on the polymer brush chemistry and peptide structure, and optimization of these parameters is critical to achieving infection-resistant surfaces. By analyzing the interaction of polymer-brush-tethered AMPs with model lipid membranes using circular dichroism spectroscopy, we determined that the polymer brush chemistry has an influence on the extent of secondary structure change of tethered peptides before and after interaction with biomembranes. The peptide structure also has an influence on the density of conjugated peptides on polymer brush coatings and the resultant wettability of the coatings, and both of these factors contributed to the antimicrobial activity and bacterial adhesion of the coatings. Overall, this work highlights the importance of optimizing the functionality of the polymer brush to achieve infection-resistant surfaces and presents important insight into the design criteria for the selection of polymers and AMPs toward the development of potent antimicrobial coating on implants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".