bioRxivBiophysicsPreprint
A Goldilocks zone of DNA flexibility defines stable yet plastic nucleosomes, tuned by histone chemistry
Jose Ignacio Perez LopeziD, Maria Julia MaristanyiD, Stephen E. FarriD, Jan HuertasiD, Rosana Collepardo‐GuevaraiD
Abstract
Nucleosomes regulate DNA accessibility through partial DNA unwrapping driven by thermal fluctuations or forces exerted by molecular motors. Despite substantial chemical and DNA sequence diversity in vivo, much of our mechanistic understanding of nucleosome unwrapping is derived from canonical constructs. Here, we use a near-atomistic coarse-grained model to quantify the energetics and mechanisms of force-induced nucleosome unwrapping across over 40 nucleosomes spanning genomic and synthetic DNA sequences, histone variants, and post-translational modifications. We find that the force barriers to nucleosome unwrapping are shaped by histone composition and DNA flexibility, and arise from the formation of topologically protected, partially unwrapped intermediates. Strong nucleosome-positioning and genomic sequences consistently fall within a Goldilocks zone of intermediate DNA flexibility, which enables the formation of thermodynamically stable yet mechanically plastic nucleosomes that undergo controlled deformation under applied force. Limited mechanical variation within this zone results in modest DNA sequence-dependent effects on nucleosome deformability. In contrast, histone variants and post-translational modifications modulate nucleosome stability and plasticity in a strongly non-additive manner, with predominant contributions from H3 and H2A core arginines and H3 and H2B tail lysines. Together, our results provide a physical framework linking DNA sequence, histone composition, and nucleosome geometry to nucleosome stability, plasticity and DNA accessibility.
- assay: DNA methylation
- assay: Hi-C
- research focus: chromatin & epigenetic regulation
- research focus: protein folding & dynamics
- research focus: cell type annotation
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 1
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