Decorating a Blank Slate Protein Hydrogel: A General and Robust Approach for Functionalizing Protein Hydrogels
Bibliographic record
Abstract
Protein hydrogels constructed from recombinant proteins have attracted increasing interests for fundamental biological studies as well as applications in biomedical engineering field. In such protein hydrogels, biochemical and physical properties of protein hydrogels are often coupled to each other, making it challenging to investigate the individual effect of chemical and physical cues on cells. Moreover, laborious engineering is often required to incorporate different protein ligands into such hydrogels. To address these challenges, functionalizing a blank slate protein hydrogel is an attractive approach. However, conjugating ligands to such a blank slate protein hydrogel is challenging, as existing bioconjugation methods developed in synthetic polymer hydrogels cannot be readily adapted for protein hydrogels, significantly impeding the use of this approach in the field. Here we report a facile, general, and robust method, which is based on the SpyCatcher-SpyTag chemistry, to covalently functionalize the "blank slate" of protein hydrogels using genetically encoded interacting partners. We demonstrate that this novel method enables covalent conjugation of a wide variety of ligands, including full-length intact folded proteins, to a blank slate protein hydrogel, and allows for the decoupling of biochemical and physical properties of hydrogels from each other and investigating the individual effect of biochemical and mechanical cues on cell behaviors. To our best knowledge, this is the first general approach enabling functionalization of protein hydrogels, and we anticipate that this novel approach will find a broad range of uses in protein-based biomaterials for applications in biomedical engineering.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".