Enhancement of the Antagonistic Potency of Transforming Growth Factor-β Receptor Extracellular Domains by Coiled Coil-induced Homo- and Heterodimerization
Bibliographic record
Abstract
Transforming growth factor-beta (TGF-beta) plays a causal role in several human pathologies including fibrotic diseases and metastasis. TGF-beta signaling is mediated through its interaction with three types of cell surface receptors, RI, RII, and RIII. The soluble ectodomains of RII and RIII bind to TGF-beta, making them attractive candidates to sequester TGF-beta and inhibit its activity. To optimize the activity of the ectodomains, we studied the effect of artificially dimerizing them upon their kinetics of binding to TGF-beta using an optical biosensor and studied their antagonistic potencies using an in vitro signaling assay. We fused the RII ectodomain and the membrane-proximal ligand-binding domain of the RIII ectodomain to de novo designed heterodimerizing coil strands and demonstrated that the coil strands within the fusion proteins were capable of promoting the dimerization of the coil-tagged ectodomains. Our results indicate that coiled coil-induced dimerization of the ectodomains stabilized their interaction with TGF-beta as compared with the monomeric ectodomains. Also, in contrast to the monomeric ectodomains, which did not block signaling, the coiled coil-induced dimers were characterized by antagonistic potencies in the low nanomolar range.
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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".