The Flexible Polyelectrolyte Hypothesis of Protein−Biomineral Interaction
Bibliographic record
Abstract
Biomineralization is characterized by a high degree of control over the location, nature, size, shape, and orientation of the crystals formed. For many years, it has been widely believed that the exquisitely precise nature of crystal formation in biological tissues is the result of stereochemically specific interactions between growing crystals and extracellular matrix proteins. That is, the ability of many mineralized tissue proteins to adsorb to particular faces of biominerals has been attributed to a steric and electrical complementarity between periodic regions of the polypeptide chain and arrays of ions on the crystal face. In recent years, however, evidence has accumulated that many mineral-associated proteins lack periodic structure even when adsorbed to crystals. It also appears that protein-crystal interactions involve a general electrostatic attraction rather than arrays of complementary charges. In the present work, we review these studies and present some relevant new findings involving the mineral-modulating phosphoprotein osteopontin. Using molecular dynamics simulations, we show that the adsorption of osteopontin peptides to hydroxyapatite crystals does not involve a unique conformation of the peptide molecule, and that the adsorbed peptides are not aligned with rows of Ca(2+) ions on the crystal face. Further, we show that the interface between osteopontin peptides and calcium oxalate monohydrate crystals consists of peptide regions of high electronegativity and crystal faces of high electropositivity. Collectively, the above-mentioned studies suggest that interactions between mineral-modulating proteins and biologically relevant crystals are primarily electrostatic in nature, and that molecular disorder assists these proteins in forming multiple bonds with cations of the crystal face.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".