Synthetic antibodies targeting <scp>EphA2</scp> induce diverse signaling‐competent clusters with differential activation
Bibliographic record
Abstract
The receptor tyrosine kinase EphA2 interacts with ephrin (Efn) ligands to mediate bi-directional signals that drive cellular sorting processes during tissue development. In the context of various cancers, EphA2 can also drive invasive metastatic disease and represents an important target for cancer therapeutics. Natural Efn ligands sterically seed intertwined EphA2 clusters capable of recruiting intracellular kinases to mediate trans-phosphorylation. Synthetic proteins, such as antibodies (Abs), can mimic Efn ligands to trigger EphA2 signaling, leading to receptor internalization and degradation, and enabling intracellular delivery of conjugated drugs. Furthermore, Abs are capable of recruiting EphA2 into clusters distinct from those seeded by Efn. We developed three synthetic Abs targeting distinct EphA2 domains and determined the paratope valency necessary for agonist or antagonist properties of each of the three epitopes. Structural modeling of monovalent Fabs in complex with EphA2 elucidated competitive and non-competitive mechanisms of inhibition of EphA2 canonical signaling. Likewise, modeling of clusters induced by bivalent IgGs elucidated multiple signaling-competent EphA2 clusters capable of triggering a continuum of signaling strengths and provided insights into the requirement for multimerization of EphA2 to trigger phosphorylation. Our study shows how different agonist clusters lead to distinct kinase recruitment efficiencies to modify phosphotyrosine signal strength, and provides a panel of anti-EphA2 Abs as reagents for the development of therapeutics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".