Mechanistic Insights into Bioengineered Antibiofilm Enamel Pellicles
Bibliographic record
Abstract
Dental caries remains the most widespread chronic disease worldwide. Basically, caries originates within biofilms accumulated on dental enamel. Despite the nonrenewable nature of the enamel tissue, targeted preventive strategies are still very limited. We previously introduced customized multifunctional proteinaceous pellicles (coatings) for controlling bacterial attachment and subsequent biofilm succession. Stemmed from our whole proteome/peptidome analysis of the in vivo acquired enamel pellicle, we designed these pellicles using hybrid mixtures of the most abundant and complementary-acting antimicrobial and antifouling proteins/peptides for synergetic suppression of early biofilms. In conjugating these domains synthetically, their bioinhibitory efficacy was remarkably boosted. Herein, we sought to explore the key structure-function relationship of these potent de novo hybridized conjugates in comparison with their individual domains, solely or in physical mixtures. Specifically, we interrelated the following facets: physicochemical and 3-dimensional folding characteristics via molecular dynamics simulations, adopted secondary structure by circular dichroism, immobilization capacity on enamel through high–spatial resolution multiphoton microscopy, and biofilm suppression potency. Our data showed consistent associations among the increased preference for protein folding structures, α-helix content, and enamel-immobilization capacity; all were inversely correlated with the attached bioburden. The expressed phenotypes could be explained by the adopted strongly amphipathic helical conformation upon conjugation, mediated by the highly anionic and acidic N-terminal pentapeptide shared region/motif for enhanced immobilization on enamel. In conclusion, conjugating bioactive proteins/peptides is a novel translational approach to engineer robust antibiofilm pellicles for caries prevention. The adopted α-helical conformation is key to enhance the antibiofilm efficacy and immobilization capacity on enamel that are promoted by certain physicochemical properties of the constituent domains. These data are valuable for bioengineering versatile therapeutics to prevent/arrest dental caries, a condition that otherwise requires invasive treatments with substantial health care expenditures.
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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) | 0.001 | 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".