Inhibition of hydroxyapatite formation by osteopontin phosphopeptides
Bibliographic record
Abstract
Osteopontin (OPN) is an acidic phosphoglycoprotein that is believed to function in the prevention of soft tissue calcification. In vitro studies have shown that OPN can inhibit the formation of hydroxyapatite (HA) and other biologically relevant crystal phases, and that this inhibitory activity requires phosphorylation of the protein; however, it is not known which phosphorylated residues are involved. We have synthesized peptides corresponding to four phosphoserine-containing sequences in rat OPN: OPN7-17, containing phosphoserines 10 and 11; OPN41-52, containing phosphoserines 46 and 47; OPN248-264, containing phosphoserines 250, 257 and 262; and OPN290-301, containing phosphoserines 295-297. The abilities of these peptides to inhibit de novo HA formation were determined using a constant-composition autotitration assay. All four OPN phosphopeptides caused a dose-dependent increase in nucleation lag time, but did not significantly affect subsequent formation of the crystals. However, OPN41-52 (inhibitory constant 73.5 min/microM) and OPN290-301 (72.2 min/microM) were approx. 4 times more potent inhibitors than OPN7-17 (19.7 min/microM) and OPN247-264 (16.3 min/microM). 'Scrambling' the amino acid sequence of OPN290-301 resulted in decreased potency (45.6 min/microM), whereas omission of the phosphate groups from this peptide caused a greater decrease (5.20 min/microM). These findings have identified phosphorylated sequences that are important for the ability of rat bone OPN to inhibit HA crystal formation, and suggest that negative-charge density is an important factor in this activity.
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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".