TβR-II Discriminates the High- and Low-Affinity TGF-β Isoforms via Two Hydrogen-Bonded Ion Pairs
Bibliographic record
Abstract
The TGF-beta isoforms, TGF-beta1, -beta2, and -beta3, share greater than 70% sequence identity and are almost structurally identical. TGF-beta2 differs from the others, however, in that it binds the TGF-beta type II receptor (TbetaR-II) with much lower affinity than either TGF-beta1 or -beta3. It has been previously shown that three conserved interfacial residues, Arg25, Val92, Arg94, in TGF-beta1 and -beta3 are responsible for their high-affinity interaction with TbetaR-II. In this study, the role of each of these residues was examined by creating single, double, and triple substitutions resulting in both TGF-beta3 loss-of-function and TGF-beta2 gain-of-function variants. One-dimensional 1H NMR spectra of the variants confirmed a lack of large structural perturbations. Affinities, kinetics, and thermodynamics for TbetaR-II binding were determined by surface plasmon resonance biosensor analysis. Double substitutions revealed that nearly all of the high-affinity binding is contributed by Arg25 and Arg94. Single site substitutions showed that Arg94 makes the greatest contribution. Substitution of Arg25 and Arg94 with alanine verified the requirement of the arginine guanidinium functional groups for the highly specific hydrogen-bonded ion pairs formed between Arg25 and Arg94 of TGF-beta1 and -beta3, and Glu119 and Asp32 of TbetaR-II. Further kinetic and thermodynamic analyses confirmed that Arg25 and Arg94 are primarily responsible for high-affinity binding and also revealed that noninterfacial longer range effects emanating from the TGF-beta structural framework contribute slightly to TbetaR-II binding. Growth inhibition assays showed that binding changes generally correlate directly with changes in function; however, a role Val92 in this cellular context was uncovered.
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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".