Hydrogen-bonded supramolecular biohybrid frameworks for protein biomineralization constructed from natural phenolic building blocks
Bibliographic record
Abstract
Hydrogen bond-mediated supramolecular crystalline materials, such as hydrogen-bonded organic frameworks, offer a promising strategy for protein biomineralization, yet the intricate design and multi-step synthesis of specific orthogonal units in molecular building blocks pose a significant synthetic challenge. Identifying new classes of natural building blocks capable of facilitating supramolecular framework construction while enabling stable protein binding has remained an elusive goal. Here, we introduce a versatile assembly strategy enabling the organization of diverse proteins and phenolic building blocks into highly crystalline hydrogen-bonded supramolecular phenolic frameworks (ProteinX@SPF). The natural ellagic acid (EA) exhibits a centrosymmetric structure with catechol groups on each molecular side, facilitating hydrogen bonding with protein amino acid residues for primary nucleation. Subsequently, EA self-assembles into ProteinX@SPF through hydrogen bonding and π-π interactions. The multiple hydrogen-bonding interactions impart structural rigidity and directional integrity, conferring ProteinX@SPF biohybrids with remarkable resistance to harsh conditions while preserving protein bioactivity. Additionally, the supramolecular stacking induced by π-π interactions endows ProteinX@SPF with long-range ordered nanochannels, which can serve as the gating to sieve the catalytic substrate and thus enhance the biocatalytic specificity. This work sheds light on biomineralization with natural building blocks for functional biohybrids, showing enormous potential in biocatalysis, sensing, and nanomedicine.
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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".