Microgel Interactions with Peptides and Proteins : Consequence of Peptide and Microgel Properties
Bibliographic record
Abstract
Microgels are lightly cross-linked hydrogel particles in the sub-micrometer to micrometer size range with a capacity to drastically change their volume in response to changes in the external environment. Microgels have an ability to bind and store substances such as biomacromolecular drugs, notably proteins and peptides, and release them upon stimuli, making them potential candidates as drug delivery vehicles and functional biomaterials. This thesis aims at clarifying important factors affecting peptide-microgel interactions. These interactions were studied by micromanipulator-assisted light and fluorescence microscopy focusing on microgel deswelling in response to peptide binding, as well as re-swelling in response to peptide release or enzymatic degradation. To evaluate peptide uptake in microgels, solution depletion measurements were used whereas the peptide secondary structure was investigated by circular dichroism. In addition, the peptide and enzyme distribution within microgels was analyzed with confocal microscopy. Results presented in this thesis demonstrate that peptide incorporation into microgels, as well as peptide-induced microgel deswelling, increases with peptide length and charge density. In addition, results demonstrate that the peptide charge (length) rather than peptide charge density determines microgels deswelling. End-to-end cyclization is shown to not noticeably influence peptide-microgel interactions, suggesting that peptide cyclization can be used in combination with oppositely charged microgel carriers to improve the proteolytic and chemical stability of the peptide compared to the corresponding linear variant. Peptide secondary structure is found to drastically affect peptide incorporation into, and release from, oppositely charged microgels. Furthermore, it is shown that microgel charge density, peptide molecular weight, and enzyme concentration all greatly influence microgel bound peptide degradation. Of importance for applications, protective effects of microgels against proteolytic peptide degradation are observed only at sufficiently high microgel charge densities. Enzyme-mediated microgel degradation is shown to increase with increasing enzyme concentration, while an increased peptide loading in microgels causes a concentration-dependent decrease in microgel degradation. Taken together, results obtained in this work have provided some insight into factors of importance for rational use of microgels as delivery systems for protein or peptide drugs, but also in a host of other biomedical applications using weakly cross-linked polymer systems.
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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 | 0.000 |
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".