Biomineralization through a Symmetry-Controlled Oligomeric Peptide
Bibliographic record
Abstract
Biomineralization peptides are versatile tools for generating nanostructures since they can make specific interactions with various inorganic metals, which can lead to the formation of intricate nanostructures. Previously, we examined the influence that multivalency has on inorganic structures formed by p53 tetramer-based biomineralization peptides and noted a connection between the geometry of the peptide and its ability to regulate nanostructure formation. To investigate the role of multivalency in nanostructure formation by biomineralization peptides more thoroughly, silver biomineralization peptides were engineered by linking them to additional self-assembling molecules based on coiled-coil peptides and multistranded DNA oligomers. Under mild reducing conditions at room temperature, these engineered biomineralization peptides self-assembled and formed silver nanostructures. The trimeric forms of the biomineralization peptides were the most efficient in forming a hexagonal disk nanostructure, with both the coiled-coil peptide and DNA-based multimeric forms. Together, the results suggest that the spatial arrangement of biomineralization peptides plays a more important role in regulating nanostructure formation than their valency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".