Conformational Dynamics and the Binding of Specific and Nonspecific DNA by the Autoinhibited Transcription Factor Ets-1
Bibliographic record
Abstract
The affinity of the Ets-1 transcription factor for DNA is autoinhibited by an intrinsically disordered serine-rich region (SRR) and a helical inhibitory module (IM) appended to its winged helix-turn-helix ETS domain. Using NMR spectroscopy, we investigated how Ets-1 recognizes specific versus nonspecific DNA, with a focus on the roles of protein dynamics and autoinhibition in these processes. Upon binding either DNA, the two marginally stable N-terminal helices of the IM predominantly unfold, but still sample partially ordered conformations. Also, on the basis of amide chemical shift perturbation mapping, Ets-1 associates with both specific and nonspecific DNA through the same canonical ETS domain interface. These interactions are structurally independent of the SRR, and thus autoinhibition does not impart DNA-binding specificity. However, relative to the pronounced NMR spectroscopic changes in Ets-1 resulting from specific DNA binding, the spectra of the nonspecific DNA complexes showed conformational exchange broadening and lacked several diagnostic amide and indole signals attributable to hydrogen bonding interactions seen in reported X-ray crystallographic structures of this transcription factor with its cognate DNA sequences. Such differences are highlighted by the chemical shift and relaxation properties of several interfacial lysine and arginine side chains. Collectively, these data support a general model in which Ets-1 interacts with nonspecific DNA via dynamic electrostatic interactions, whereas hydrogen bonding drives the formation of well-ordered complexes with specific DNA.
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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".