PEGMA-Based Microgels: A Thermoresponsive Support for Enzyme Reactions
Bibliographic record
Abstract
Thermoresponsive colloidal hydrogels (aqueous microgels) made from poly[oligo(ethylene glycol) methacrylate] (PEGMA) are an interesting class of biomaterials due to their sharp thermal transition and excellent biocompatibility. However, the inherent protein repellency of PEGMA has made the biofunctionalization of these microgels difficult and prevented them from reaching their full potential in applications such as a protein carrier. Here, we report the synthesis of thermoresponsive PEGMA microgels and the covalent attachment of horseradish peroxidase (HRP, as a model protein) to these microgels. We prepared our microgels by the precipitation copolymerization of 70 mol % OEGMA 300 and 30 mol % methacrylic acid in water. The resulting microgels showed a volume phase transition temperature (VPTT) of ca. 60 °C in acidic buffers and in DI water but in neutral and basic buffers did not show a thermal response up to 75 °C. The direct immobilization of HRP to the activated carboxylic groups in the microgel backbone was unsuccessful, but using a diamine spacer with four ethylene glycol units, we were able to covalently attach this enzyme to our PEGMA microgels through bis-aryl hydrazone chemistry. HRP has its maximum activity at ca. 50 °C. At higher temperatures, the activity was reduced, but the microgel-bound enzyme showed less reduction in activity than the native enzyme and no change in activity associated with the VPTT. In addition, PEGMA microgels stabilized the attached enzymes against thermal denaturation. For example, our results showed that the enzyme immobilized on the PEGMA microgel lost its activity 3.4 times slower than the free enzyme in the first 5 h of annealing at 50 °C. The bioconjugation strategy introduced here could serve as a model for the covalent attachment of other biomacromolecules to the protein-repellent PEGMA microgels.
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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 | 0.001 |
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".