Matrix Gla Protein Inhibits Ectopic Calcification by a Direct Interaction with Hydroxyapatite Crystals
Bibliographic record
Abstract
Mice lacking the gene encoding matrix gla protein (MGP) exhibit massive mineral deposition in blood vessels and die soon after birth. We hypothesize that MGP prevents arterial calcification by adsorbing to growing hydroxyapatite (HA) crystals. To test this, we have used a combined experimental-computational approach. We synthesized peptides covering the entire sequence of human MGP, which contains three sites of serine phosphorylation and five sites of γ-carboxylation, and studied their effects on HA crystal growth using a constant-composition autotitration assay. In parallel studies, the interactions of these sequences with the {100} and {001} faces of HA were analyzed using atomistic molecular dynamics (MD) simulations. YGlapS (amino acids 1-14 of human MGP) and SK-Gla (MGP43-56) adsorbed rapidly to the {100} and {001} faces and strongly inhibited HA growth (IC(50) = 2.96 μg/mL and 4.96 μg/mL, respectively). QR-Gla (MGP29-42) adsorbed more slowly and was a moderate growth inhibitor, while the remaining three (nonpost-translationally modified) peptides had little or no effect in either analysis. Substitution of gla with glutamic acid reduced the adsorption and inhibition activities of SK-Gla and (to a lesser extent) QR-Gla but not YGlapS; substitution of phosphoserine with serine reduced the inhibitory potency of YGlapS. These studies suggest that MGP prevents arterial calcification by a direct interaction with HA crystals that involves both phosphate groups and gla residues of the protein. The strong correlation between simulated adsorption and measured growth inhibition indicates that MD provides a powerful tool to predict the effects of proteins and peptides on crystal formation.
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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".