Metal Chelation Dynamically Regulates the Mechanical Properties of Engineered Protein Hydrogels
Bibliographic record
Abstract
Engineering protein hydrogels with dynamically tunable mechanical and physical properties is of great interest due to their potential applications in biomedical engineering and mechanobiology. In our recent work, we engineered a novel dynamic protein hydrogel using a redox responsive, mutually exclusive protein (MEP)-based folding switch as the building block. By modulating the redox potential, the MEP-based folding switch can switch its conformation between two distinct states, leading to a significant change of the proteins’ effective contour length of the polypeptide chain and an effective change of the cross-linking density of the hydrogel network ( Kong, N. et al. Adv. Funct. Mater. 2014, 24, 7310). Building upon this work, here we report an engineered metal-chelation based method to dynamically regulate mechanical and physical properties of MEP-based protein hydrogels. We engineered a bihistidine metal binding motif in the host domain of the MEP. The binding of bivalent ions (such as Ni 2+ ) enhances the thermodynamic stability of the host domain and results in the shift of the conformational equilibrium between the two mutually exclusive conformations of the MEP. Thus, the bihistidine mutant can serve as a metal ion responsive-folding switch to regulate the conformational equilibrium of the MEP. Using this bihistidine mutant of MEP as building blocks, we engineered chemically cross-linked protein hydrogels. We found that the mechanical and physical properties (including Young’s modulus, resilience, and swelling degree) of this hydrogel can be regulated by metal chelation in a continuous and reversible fashion. This dynamic change is due to the metal chelation-induced shift of the conformational equilibrium of the MEP and consequently the effective cross-linking density of the hydrogel. Our results demonstrate a general strategy to engineer MEP-based dynamic protein hydrogels that may find applications in mechanobiology and tissue 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) | 0.000 | 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 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".