Binding of regulatory proteins to nucleosomes is modulated by dynamic histone tails
Bibliographic record
Abstract
Abstract Despite histone tails’ critical roles in epigenetic regulation, little is known about mechanisms of how histone tails modulate the nucleosomal DNA solvent accessibility and recognition of nucleosomes by other macromolecules. Here we generate extensive atomic level conformational ensembles of histone tails in the context of the full human nucleosome, totaling 26 microseconds of molecular dynamics simulations. We explore the histone tail binding with the nucleosomal and linker DNA and observe rapid conformational transitions between bound and unbound states allowing us to estimate kinetic and thermodynamic properties of the histone tail-DNA interactions. Different histone types exhibit distinct, although conformationally heterogeneous, binding modes and each histone type occludes specific DNA regions from the solvent. Using a comprehensive set of experimental data on nucleosome structural complexes, we find that majority of the studied nucleosome-binding proteins and histone tails target mutually exclusive regions on nucleosomal or linker DNA around the super-helical locations ±1, ±2, and ±7. This finding is explained within the generalized competitive binding and tail displacement models of partners recruitment to nucleosomes. Finally, we demonstrate the crosstalk between different histone post-translational modifications, where charge-altering modifications and mutations typically suppress tail-DNA interactions and enhance histone tail dynamics.
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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".