The importance of amino acid interactions in the crystallization of hydroxyapatite
Bibliographic record
Abstract
Non-collagenous proteins (NCPs) inhibit hydroxyapatite (HA; Ca(5)(PO(4))(3)OH) formation in living organisms by binding to nascent nuclei of HA and preventing their further growth. Polar and charged amino acids (AAs) are highly expressed in NCPs, and the negatively charged ones, such as glutamic acid (Glu) and phosphoserine (P-Ser) seem to be mainly responsible for the inhibitory effect of NCPs. Despite the recognized importance of these AAs on the behaviour of NCPs, their specific effect on HA crystallization is still unclear, and controversial results have been reported concerning the efficacy of HA inhibition of positively versus negatively charged AAs. We focused on a positively charged (arginine, Arg) and a negatively charged (Glu) AA, and their combination in the same solution. We studied their inhibitory effect on HA nucleation and growth at physiological temperature and pH and we determined the mechanism by which they can affect HA crystallization. Our results showed a strong inhibitory effect of Arg on HA nucleation; however, Glu was more effective in inhibiting HA crystal growth during the growth stage. The combination of Glu and Arg was less effective in controlling HA nucleation, but it inhibited HA crystal growth. We attributed these differences to the stability of complexes formed between AAs and calcium and phosphate ions at the nucleation stage, and in bonding strength of AAs to HA crystal faces during the growth stage. The AAs also influenced the morphology of synthesized HA. Presence of either Arg or Glu resulted in the formation of spherulites consisting of preferentially oriented nanoplatelets orientation. This was attributed to kinetic factors favoring growth front nucleation (GFN) mechanism.
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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".