Quantifying the contribution of DNA conformational flexibility to transcription factor binding on nucleosomal DNA uncovers indirect readout across diverse TF families
Bibliographic record
Abstract
Abstract Background Eukaryotic gene regulation depends on transcription factors (TFs) recognizing short DNA motifs within chromatin. Many of these motifs lie within nucleosomes, where DNA is sharply bent, rotationally phased, and constrained by histone-DNA contacts. Yet only a subset is occupied in any cellular context. Motif identity alone, therefore, cannot fully explain selective TF engagement with nucleosomal DNA. We asked whether sequence-derived DNA conformational flexibility provides an interpretable representation of sequence context relevant to TF recognition on nucleosomes. Results We compiled five DNA flexibility descriptors in the Python package DNAflexpy, representing bendability, torsional deformation, backbone conformational variability, and stiffness. We built quantitative models of TF binding affinity across 226 datasets from a high-throughput in vitro TF-nucleosome binding assay. Flexibility-augmented models improved prediction over mononucleotide baselines in most datasets, with smaller but reproducible gains over trinucleotide baselines. The gains were not uniform: they varied across TF families and were concordant with DNA shape-fluctuation features, suggesting that DNAflexpy descriptors capture a sequence-encoded structural signal. In PIONEAR-seq data, model performance generalized across nucleosomal templates in a TF- and sequence-dependent manner. Beyond prediction, position-resolved flexibility footprints revealed deformation signatures at cognate motifs and flanking regions across diverse TF families. For SOX11, model-derived footprints aligned with DNA shape fluctuations from nanosecond-to-microsecond molecular dynamics trajectories of SOX11-bound nucleosomes, consistent with independently observed DNA conformational dynamics and bound-state stabilization. The in vivo data showed a similar but more context-dependent pattern. OCT4 occupancy tended to correlate with local flexibility, whereas GATA3-pioneered regions showed flexibility coupled with altered rotational positioning of cognate motifs. Flexibility-augmented classifiers further improved discrimination of occupied nucleosomal motifs across ENCODE datasets. Torsional flexibility features, particularly twist dispersion and trx, were most informative for classification. Conclusions Sequence-derived DNA conformational flexibility provides a quantitative and interpretable representation of sequence context in TF recognition on nucleosomes. By augmenting sequence with structural information, these models help quantify and interpret an indirect-readout contribution in which DNA deformation tendencies may complement motif sequence and DNA shape. This framework may help explain why only selected motif instances are engaged in chromatin, without treating flexibility as independent of primary sequence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".