ArticleProceedings of the National Academy of Sciences of the United States of America2014
Training-free atomistic prediction of nucleosome occupancy.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 28 citations in OpenAlex.
- HDGS-Net: nucleosome occupancy prediction based on a hybrid dilated gated separable convolutional neural network.BMC genomics · 2026Article
- Hierarchical natural move Monte Carlo refines flexible RNA structures into cryo-EM densities.RNA (New York, N.Y.) · 2020Article
- Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences.Nucleic acids research · 2019Article
- HLA-DM Stabilizes the Empty MHCII Binding Groove: A Model Using Customized Natural Move Monte Carlo.Journal of chemical information and modeling · 2019Article
- LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks.Bioinformatics (Oxford, England) · 2018Article
- Contributions of Sequence to the Higher-Order Structures of DNA.Biophysical journal · 2017Review
- A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2016Review
- Modeling Functional Motions of Biological Systems by Customized Natural Moves.Biophysical journal · 2016Article
- Categorical spectral analysis of periodicity in nucleosomal DNA.Nucleic acids research · 2016Article
- The ATP binding site of the chromatin remodeling homolog Lsh is required for nucleosome density and de novo DNA methylation at repeat sequences.Nucleic acids research · 2015Article
- Evolving insights on how cytosine methylation affects protein-DNA binding.Briefings in functional genomics · 2015Review
- Prediction of nucleosome rotational positioning in yeast and human genomes based on sequence-dependent DNA anisotropy.BMC bioinformatics · 2014Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Nucleosomes alter gene expression by preventing transcription factors from occupying binding sites along DNA. DNA methylation can affect nucleosome positioning and so alter gene expression epigenetically (without changing DNA sequence). Conventional methods to predict nucleosome occupancy are trained on observed DNA sequence patterns or known DNA oligonucleotide structures. They are statistical and lack the physics needed to predict subtle epigenetic changes due to DNA methylation. The training-free method presented here uses physical principles and state-of-the-art all-atom force fields to predict both nucleosome occupancy along genomic sequences as well as binding to known positioning sequences. Our method calculates the energy of both nucleosomal and linear DNA of the given sequence. Based on the DNA deformation energy, we accurately predict the in vitro occupancy profile observed experimentally for a 20,000-bp genomic region as well as the experimental locations of nucleosomes along 13 well-established positioning sequence elements. DNA with all C bases methylated at the 5 position shows less variation of nucleosome binding: Strong binding is weakened and weak binding is strengthened compared with normal DNA. Methylation also alters the preference of nucleosomes for some positioning sequences but not others.
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