Engineering and Therapeutic Application of Single-Chain Bivalent TGF-β Family Traps
Bibliographic record
Abstract
Deregulation of TGF-β superfamily signaling is a causative factor in many diseases. Here we describe a protein engineering strategy for the generation of single-chain bivalent receptor traps for TGF-β superfamily ligands. Traps were assembled using the intrinsically disordered regions flanking the structured binding domain of each receptor as "native linkers" between two binding domains. This yields traps that are approximately threefold smaller than antibodies and consists entirely of native receptor sequences. Two TGF-β type II receptor-based, single-chain traps were designed, termed (TβRII)2 and (TβRIIb)2, that have native linker lengths of 35 and 60 amino acids, respectively. Both single-chain traps exhibit a 100 to 1,000 fold higher in vitro ligand binding and neutralization activity compared with the monovalent ectodomain (TβRII-ED), and a similar or slightly better potency than pan-TGF-β-neutralizing antibody 1D11 or an Fc-fused receptor trap (TβRII-Fc). Despite its short in vivo half-life (<1 hour), which is primarily due to kidney clearance, daily injections of the (TβRII)2 trap reduced the growth of 4T1 tumors in BALB/c mice by 50%, an efficacy that is comparable with 1D11 (dosed thrice weekly). In addition, (TβRII)2 treatment of mice with established 4T1 tumors (100 mm(3)) significantly inhibited further tumor growth, whereas the 1D11 antibody did not. Overall, our results indicate that our rationally designed bivalent, single-chain traps have promising therapeutic potential.
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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".