Protein Fragment Reconstitution as a Driving Force for Self‐Assembling Reversible Protein Hydrogels
Bibliographic record
Abstract
Due to their potential biomedical applications, protein‐based hydrogels have attracted considerable interest. Although various methods have been developed to engineer self‐assembling, physically‐crosslinked protein hydrogels, exploring novel driving forces to engineer such hydrogels remains challenging. Protein fragment reconstitution, also known as fragment complementation, is a self‐assembling mechanism by which protein fragments can reconstitute the folded conformation of the native protein when split into two halves. Although it has been used in biophysical studies and bioassays, fragment reconstitution has not been explored for hydrogel construction. Using a small protein GL5 as a model, which is capable of fragment reconstitution to reconstitute the folded GL5 spontaneously when split into two halves, GN and GC, we demonstrate that protein fragment reconstitution is a novel driving force for engineering self‐assembling reversible protein hydrogels. Fragment reconstitution between GN and GC crosslinks GN and GC‐containing proteins into self‐assembling reversible protein hydrogels. These novel hydrogels show temperature‐dependent reversible sol‐gel transition, and excellent property against erosion in water. Since many proteins can undergo fragment reconstitution, we anticipate that such fragment reconstitution may offer a general driving force for engineering protein hydrogels from a variety of proteins, and thus significantly expanding the ‘toolbox’ currently available in the field of biomaterials.
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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 | 0.001 | 0.001 |
| 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".