Moderate‐Affinity Affibodies Modulate the Delivery and Bioactivity of Bone Morphogenetic Protein‐2
Bibliographic record
Abstract
Uncontrolled bone morphogenetic protein-2 (BMP-2) release can lead to off-target bone growth and other adverse events. To tackle this challenge, yeast surface display is used to identify unique BMP-2-specific protein binders known as affibodies that bind to BMP-2 with different affinities. Biolayer interferometry reveals an equilibrium dissociation constant of 10.7 nm for the interaction between BMP-2 and high-affinity affibody and 34.8 nm for the interaction between BMP-2 and the low-affinity affibody. The low-affinity affibody-BMP-2 interaction also exhibits an off-rate constant that is an order of magnitude higher. Computational modeling of affibody-BMP-2 binding predicts that the high- and low-affinity affibodies bind to two distinct sites on BMP-2 that function as different cell-receptor binding sites. BMP-2 binding to affibodies reduces expression of the osteogenic marker alkaline phosphatase (ALP) in C2C12 myoblasts. Affibody-conjugated polyethylene glycol-maleimide hydrogels increase uptake of BMP-2 compared to affibody-free hydrogels, and high-affinity hydrogels exhibit lower BMP-2 release into serum compared to low-affinity hydrogels and affibody-free hydrogels over four weeks. Loading BMP-2 into affibody-conjugated hydrogels prolongs ALP activity of C2C12 myoblasts compared to soluble BMP-2. This work demonstrates that affibodies with different affinities can modulate BMP-2 delivery and activity, creating a promising approach for controlling BMP-2 delivery in clinical applications.
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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".