DNA sequence-dependent variation in nucleosome structure, stability, and dynamics detected by a FRET-based analysisThis paper is one of a selection of papers published in this Special Issue, entitled 29th Annual International Asilomar Chromatin and Chromosomes Conference, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Förster resonance energy transfer (FRET) techniques provide powerful and sensitive methods for the study of conformational features in biomolecules. Here, we review FRET-based studies of nucleosomes, focusing particularly on our work comparing the widely used nucleosome standard, 5S rDNA, and 2 promoter-derived regulatory element-containing nucleosomes, mouse mammary tumor virus (MMTV)-B and GAL10. Using several FRET approaches, we detected significant DNA sequence-dependent structure, stability, and dynamics differences among the three. In particular, 5S nucleosomes and 5S H2A/H2B-depleted nucleosomal particles have enhanced stability and diminished DNA dynamics, compared with MMTV-B and GAL10 nucleosomes and particles. H2A/H2B-depleted nucleosomes are of interest because they are produced by the activities of many transcription-associated complexes. Significant location-dependent (intranucleosomal) stability and dynamics variations were also observed. These also vary among nucleosome types. Nucleosomes restrict regulatory factor access to DNA, thereby impeding genetic processes. Eukaryotic cells possess mechanisms to alter nucleosome structure, to generate DNA access, but alterations often must be targeted to specific nucleosomes on critical regulatory DNA elements. By endowing specific nucleosomes with intrinsically higher DNA accessibility and (or) enhanced facility for conformational transitions, DNA sequence-dependent nucleosome dynamics and stability variations have the potential to facilitate nucleosome recognition and, thus, aid in the crucial targeting process. This and other nucleosome structure and function conclusions from FRET analyses are discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".