Engineering Protein Hydrogels Using SpyCatcher-SpyTag Chemistry
Bibliographic record
Abstract
Constructing hydrogels from engineered proteins has attracted significant attention within the material sciences, owing to their myriad potential applications in biomedical engineering. Developing efficient methods to cross-link tailored protein building blocks into hydrogels with desirable mechanical, physical, and functional properties is of paramount importance. By making use of the recently developed SpyCatcher-SpyTag chemistry, we successfully engineered protein hydrogels on the basis of engineered tandem modular elastomeric proteins. Our resultant protein hydrogels are soft but stable, and show excellent biocompatibility. As the first step, we tested the use of these hydrogels as a drug carrier, as well as in encapsulating human lung fibroblast cells. Our results demonstrate the robustness of the SpyCatcher-SpyTag chemistry, even when the SpyTag (or SpyCatcher) is flanked by folded globular domains. These results demonstrate that SpyCatcher-SpyTag chemistry can be used to engineer protein hydrogels from tandem modular elastomeric proteins that can find applications in tissue engineering, in fundamental mechano-biological studies, and as a controlled drug release vehicle.
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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".