Structural insights into DNA recognition by the BEN domain of the transcription factor BANP
Bibliographic record
Abstract
The BEN domain-containing transcription factors regulate transcription by recruiting chromatin-modifying factors to specific chromatin regions via their DNA-binding BEN domains. The BEN domain of BANP has been shown to bind to a CGCG DNA sequence or an AAA-containing matrix attachment regions DNA sequence. Consistent with these in vivo observations, we identified an optimal DNA-binding sequence of AAATCTCG by protein binding microarray, which was also confirmed by our isothermal titration calorimetry and mutagenesis results. We then determined crystal structures of the BANP BEN domain in apo form and in complex with a CGCG-containing DNA, respectively, which revealed that the BANP BEN domain mainly used the electrostatic interactions to bind DNA with some base-specific interactions with the TC motifs. Our isothermal titration calorimetry results also showed that BANP bound to unmethylated and methylated DNAs with comparable binding affinities. Our complex structure of BANP-mCGCG revealed that the BANP BEN domain bound to the unmethylated and methylated DNAs in a similar mode and cytosine methylation did not get involved in binding, which is also consistent with our observations from the complex structures of the BEND6 BEN domain with the CGCG or CGmCG DNAs. Taken together, our results further elucidate the elements important for DNA recognition and transcriptional regulation by the BANP BEN domain-containing transcription factor.
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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".