Using Sugar and Amino Acid Additives to Stabilize Enzymes within Sol−Gel Derived Silica
Bibliographic record
Abstract
The inclusion of additives during the immobilization of proteins into sol−gel processed materials has been widely explored as a route to stabilize proteins against the denaturing stresses encountered upon entrapment. In this report, we explore the effects of sorbitol and N -methylglycine (collectively referred to as osmolytes) on both the conformational stability and biological activity of the enzymes α-chymotrypsin and ribonuclease T1 in solution and when entrapped into sol−gel derived silica. In each case, the encapsulation of the enzymes into sol−gel derived silica in the absence of additives led to a moderate decrease in the thermodynamic stability of the proteins. However, entrapment in the presence of the osmolytes produced significant increases in the thermal stability and biological activity of the encapsulated proteins. We show that the observed enhancements in enzyme stability are likely based on a combination of increases in the pore size of the silica material (which improves substrate delivery and thus activity) and changes in the thermal stability of the entrapped enzymes in the presence of osmolytes. Our results suggest that these additives stabilize the two proteins by altering the hydration of the entrapped protein, hence this stabilization method may prove to be applicable to a wide variety of proteins.
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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 | 0.000 | 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".