Computationally identified peptides immobilized to hydrogel delivery vehicles enable affinity-controlled release of proteins
Bibliographic record
Abstract
M). Mutational analysis revealed that aromatic residues were essential for binding. When covalently immobilized to a hyaluronan-methylcellulose hydrogel, the peptide enabled prolonged and sequential release of VEGF and PDGF. Computational modeling of the release kinetics using finite element analysis agreed with experimental data, validating the model and guiding the design of the delivery system. Importantly, the released proteins remained bioactive as demonstrated by cell growth and angiogenesis tube formation assays. This study demonstrates a simple and effective approach to achieve controlled and sequential release of therapeutic growth factors. STATEMENT OF SIGNIFICANCE: Proteins are promising therapeutics for multiple diseases; however, their delivery remains challenging due to the use of generic strategies, which often degrade the proteins. We used computational tools to identify specific binders for two growth factors (VEGF and PDGF), and then demonstrated the utility of these binders to control the release of bioactive proteins for a sustained period from a novel hydrogel. We achieved sequential release of VEGF followed by PDGF, which is important in vessel formation. This new system of identifying protein binding partners is broadly applicable and shown herein to be useful to control the release of two specific proteins. This combination of theoretical and experimental approaches is of broad interest to biomaterial researchers.
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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 | 0.000 | 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 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".