Applying Computational Protein Design to Engineer Affibodies for Affinity-controlled Delivery of Vascular Endothelial Growth Factor and Platelet-Derived Growth Factor
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) play coordinated roles in angiogenesis. However, current biomaterial delivery vehicles for these proteins have a limited ability to precisely control the kinetics of protein release, preventing systematic exploration of their temporal effects. Here, we combined yeast surface display and computational protein design to engineer eight VEGF-specific and PDGF-specific protein binders called affibodies with a broad range of affinities for controlled protein release. Soluble affibodies modulated protein bioactivity as evidenced by changes in VEGF-induced endothelial cell proliferation and luminescent output of a PDGF-responsive cell line. Affibody-conjugated hydrogels enabled tunable protein release over 7 days. VEGF and PDGF released from affibody-conjugated hydrogels exhibited higher bioactivity than proteins released from hydrogels without affibodies, suggesting that these engineered affinity interactions could prolong protein bioactivity. This work underscores the power of computational protein design to enhance biomaterial functionality, creating a platform for tunable protein delivery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".