Cutinase Immobilization on a Supramolecular Cage Protein Scaffold
Bibliographic record
Abstract
Background: Self-assembly of preformed nanoparticles into larger and more complex materials, termed nanoarchitectonics, is an area of great interest as the resulting higher-order archi-tectures can exhibit advanced supramolecular properties important in sensor design, catalysis, and ferromagnetic properties. Objective: The aim of the current investigation is to explore the application of self-assembling pro-tein networks to serve as molecular scaffolds for immobilization of enzyme catalysts. The use of 12 nm ferritin cage proteins to serve as components of these scaffolds would expand the application of these types of multifunctional proteins to the fabrication of advanced biomaterials. Method: Humicola insolens cutinase was immobilized on a supramolecular protein scaffold using bioconjugation to biotinylate the enzyme of interest. The protein-based scaffold consisted of a fer-ritin-biotin-avidin system, and the interaction of biotin and avidin was used to suspend the enzyme molecules onto this network. Matrix-assisted laser desorption mass spectrometry, scanning electron microscopy, and energy dispersive X-ray spectroscopy were employed to analyze the supramolec-ular cage protein scaffold at various stages of fabrication. Results: The activities of these scaffold-bound enzymes towards chromogenic esters and polyeth-ylene terephthalate (PET) were analyzed and found to remain active towards both substrates follow-ing biotinylation and immobilization. Conclusion: Biotinylated Humicola insolens cutinase enzymes can be immobilized on nanodimen-sional protein networks composed of avidin and biotinylated horse spleen ferritin and exhibit cata-lytic activity toward a small substrate, p-nitrophenylbutyrate, as well as an industrial plastic. Self-assembling protein networks may provide new approaches for biomolecular immobilization.
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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".