Hydroxyapatite Growth Inhibition Effect of Pellicle Statherin Peptides
Bibliographic record
Abstract
In our recent studies, we have shown that in vivo-acquired enamel pellicle is a sophisticated biological structure containing a significant portion of naturally occurring salivary peptides. From a functional aspect, the identification of peptides in the acquired enamel pellicle is of interest because many salivary proteins exhibit functional domains that maintain the activities of the native protein. Among the in vivo-acquired enamel pellicle peptides that have been newly identified, 5 peptides are derived from statherin. Here, we assessed the ability of these statherin pellicle peptides to inhibit hydroxyapatite crystal growth. In addition, atomistic molecular dynamics (MD) simulations were performed to better understand the underlying physical mechanisms of hydroxyapatite growth inhibition. A microplate colorimetric assay was used to quantify hydroxyapatite growth. Statherin protein, 5 statherin-derived peptides, and a peptide lacking phosphate at residues 2 and 3 were analyzed. Statherin peptide phosphorylated on residues 2 and 3 indicated a significant inhibitory effect when compared with the 5 other peptides (P < 0.05). MD simulations showed a strong affinity and fast adsorption to hydroxyapatite for phosphopeptides, whereas unphosphorylated peptides interacted weakly with the hydroxyapatite. Our data suggest that the presence of a covalently linked phosphate group (at residues 2 and 3) in statherin peptides modulates the effect of hydroxyapatite growth inhibition. This study provides a mechanism to account for the composition and function of acquired enamel pellicle statherin peptides that will contribute as a base for the development of biologically stable and functional synthetic peptides for therapeutic use against dental caries and/or periodontal disease.
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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".