Steric Mechanism of Auto-Inhibitory Regulation of Specific and Non-Specific DNA Binding by the ETS Transcriptional Repressor ETV6
Bibliographic record
Abstract
DNA binding by the ETS transcriptional repressor ETV6 (or TEL) is auto-inhibited ~50-fold due to an α-helix that sterically blocks its ETS domain binding interface. Using NMR spectroscopy, we demonstrate that this marginally stable helix is unfolded, and not displaced to a non-inhibitory position, when ETV6 is bound to DNA containing a consensus (5')GGAA(3') recognition site. Although significantly lower in affinity, binding to non-specific DNA is auto-inhibited ~5-fold and is also accompanied by helix unfolding. Based on NMR chemical shift perturbations, both specific and non-specific DNA are bound via the same canonical ETS domain interface. However, spectral perturbations are smaller for the non-specific complex, suggesting weaker and less well-defined interactions than in the specific complex. In parallel, the crystal structure of ETV6 bound to a specific DNA duplex was determined. The structure of this complex reveals that a non-conserved histidine residue in the ETS domain recognition helix helps establish the specificity of ETV6 for DNA-binding sites containing (5')GGAA(3')versus(5')GGAT(3'). These studies provide a unified steric mechanism for attenuating ETV6 binding to both specific and non-specific DNA and expand the repertoire of characterized auto-inhibitory strategies utilized to regulate ETS factors.
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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".