Tandem Modular Protein-Based Hydrogels Constructed Using a Novel Two-Component Approach
Bibliographic record
Abstract
Leucine zipper sequences have been widely used to engineer protein-based hydrogels for biomedical applications. Previously, we have used this method to engineer tandem modular protein-based hydrogels as a step toward developing extracellular matrix-mimetic hydrogels. However, the spontaneous self-association of leucine zipper sequences in solution has made it challenging to express and purify tandem modular proteins carrying leucine zipper under native conditions. To obviate this problem, here we report a novel two-component approach to engineer tandem modular protein-based hydrogels. This methodology makes use of two complementary leucine zipper sequences (CCE and CCK), which do not self-associate but self-assemble into heterodimeric coiled-coils at neutral pH, as functional groups to drive the self-assembly of protein hydrogels. The two protein components are bifunctional and trifunctional tandem modular proteins carrying the leucine zipper functional groups. We found that the two proteins carrying CCE or CCK can be expressed and purified under native conditions with high yield. Upon mixing, the aqueous solution of the two proteins readily forms a transparent hydrogel. The resultant hydrogel can undergo reversible sol-gel transitions as a function of temperature, and shows much improved erosion properties. This method provides a new approach to tune the topology and physical properties of the protein hydrogels via genetic engineering, and opens the possibility to systematically explore the use of large native extracellular proteins to engineer extracellular matrix-mimetic hydrogels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".