Skeletal development in the sea urchin relies upon protein families that contain intrinsic disorder, aggregation-prone, and conserved globular interactive domains
Bibliographic record
Abstract
The formation of the sea urchin spicule skeleton requires the participation of hydrogel-forming protein families that regulate mineral nucleation and nanoparticle assembly processes that give rise to the spicule. However, the structure and molecular behavior of these proteins is not well established, and thus our ability to understand this process is hampered. We embarked on a study of sea urchin spicule proteins using a combination of biophysical and bioinformatics techniques. Our biophysical findings indicate that recombinant variants of the two most studied spicule matrix proteins, SpSM50 and SpSM30B/C (S. purpuratus) have a conformational landscape that include a C-terminal random coil/intrinsically disordered MAPQG sequence coupled to a conserved, folded N-terminal C-type lectin-like (CTLL) domain, with SpSM50 > SpSM30B/C with regard to intrinsic disorder. Both proteins possess solvent-accessible unfolded MAQPG sequence regions where Asn, Gln, and Arg residues may be accessible for protein hydrogel interactions with water molecules. Our bioinformatics study included seven other spicule matrix proteins where we note similarities between these proteins and rare, unusual proteins that possess folded and unfolded traits. Moreover, spicule matrix proteins possess three types of sequences: intrinsically disordered, amyloid-like, and folded protein-protein interactive. Collectively these reactive domains would be capable of driving protein assembly and hydrogel formation. Interestingly, three types of global conformations are predicted for the nine member protein set, wherein we note variations in the arrangement of intrinsically disordered and interactive globular domains. These variations may reflect species-specific requirements for spiculogenesis. We conclude that the molecular landscape of spicule matrix protein families enables them to function as hydrogelators, nucleators, and assemblers of mineral nanoparticles.
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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".