Oligomerization enhances the binding affinity of a silver biomineralization peptide and catalyzes nanostructure formation
Bibliographic record
Abstract
Binding affinity and specificity are crucial factors that influence nanostructure control by biomineralization peptides. In this paper, we analysed the role that the oligomeric state of a silver biomineralization peptide plays in regulating the morphology of silver nanostructure formation. Oligomerization was achieved by conjugating the silver specific TBP biomineralization peptide to the p53 tetramerization domain peptide (p53Tet). Interestingly, the TBP-p53Tet tetrameric peptide acted as a growth catalyst, controlling silver crystal growth, which resulted in the formation of hexagonal silver nanoplates without consuming the peptide. The TBP-p53Tet peptide caps the surface of the silver crystals, which enhances crystal growth on specific faces and thereby regulates silver nanostructure formation in a catalytic fashion. The present findings not only provide an efficient strategy for controlling silver nanostructure formation by biomineralization peptides, but they also demonstrate that in this case the oligomeric peptides play a unique catalytic role.
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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".