Synergistic Biomineralization Phenomena Created by a Combinatorial Nacre Protein Model System
Bibliographic record
Abstract
In the nacre or aragonite layer of the mollusk shell, proteomes that regulate both the early stages of nucleation and nano-to-mesoscale assembly of nacre tablets from mineral nanoparticle precursors exist. Several approaches have been developed to understand protein-associated mechanisms of nacre formation, yet we still lack insight into how protein ensembles or proteomes manage nucleation and crystal growth. To provide additional insights, we have created a proportionally defined combinatorial model consisting of two nacre-associated proteins, C-RING AP7 (shell nacre, Haliotis rufescens) and pseudo-EF hand PFMG1 (oyster pearl nacre, Pinctada fucata), whose individual in vitro mineralization functionalities are well-documented and distinct from one another. Using scanning electron microscopy, flow cell scanning transmission electron microscopy, atomic force microscopy, Ca(II) potentiometric titrations, and quartz crystal microbalance with dissipation monitoring quantitative analyses, we find that both nacre proteins are functionally active within the same mineralization environments and, at 1:1 molar ratios, synergistically create calcium carbonate mesoscale structures with ordered intracrystalline nanoporosities, extensively prolong nucleation times, and introduce an additional nucleation event. Further, these two proteins jointly create nanoscale protein aggregates or phases that under mineralization conditions further assemble into protein-mineral polymer-induced liquid precursor-like phases with enhanced ACC stabilization capabilities, and there is evidence of intermolecular interactions between AP7 and PFMG1 under these conditions. Thus, a combinatorial model system consisting of more than one defined biomineralization protein dramatically changes the outcome of the in vitro biomineralization process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".