An engineered transforming growth factor β (TGF-β) monomer that functions as a dominant negative to block TGF-β signaling
Bibliographic record
Abstract
The transforming growth factor β isoforms, TGF-β1, -β2, and -β3, are small secreted homodimeric signaling proteins with essential roles in regulating the adaptive immune system and maintaining the extracellular matrix. However, dysregulation of the TGF-β pathway is responsible for promoting the progression of several human diseases, including cancer and fibrosis. Despite the known importance of TGF-βs in promoting disease progression, no inhibitors have been approved for use in humans. Herein, we describe an engineered TGF-β monomer, lacking the heel helix, a structural motif essential for binding the TGF-β type I receptor (TβRI) but dispensable for binding the other receptor required for TGF-β signaling, the TGF-β type II receptor (TβRII), as an alternative therapeutic modality for blocking TGF-β signaling in humans. As shown through binding studies and crystallography, the engineered monomer retained the same overall structure of native TGF-β monomers and bound TβRII in an identical manner. Cell-based luciferase assays showed that the engineered monomer functioned as a dominant negative to inhibit TGF-β signaling with a K i of 20–70 nm. Investigation of the mechanism showed that the high affinity of the engineered monomer for TβRII, coupled with its reduced ability to non-covalently dimerize and its inability to bind and recruit TβRI, enabled it to bind endogenous TβRII but prevented it from binding and recruiting TβRI to form a signaling complex. Such engineered monomers provide a new avenue to probe and manipulate TGF-β signaling and may inform similar modifications of other TGF-β family members.
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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 | 0.001 |
| 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".