ArticleNucleic acids research2024
CGeNArate: a sequence-dependent coarse-grained model of DNA for accurate atomistic MD simulations of kb-long duplexes.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Near-atomistic simulations reveal the molecular principles that control chromatin structure and phase separation.Nature communications · 2026Article
- cgNA+min: computation of sequence-dependent dsDNA energy-minimizing minicircles.Nucleic acids research · 2026Article
- NEAT-DNA: A Chemically Accurate, Sequence-Dependent Coarse-Grained Model for Large-Scale DNA Simulations.Journal of chemical theory and computation · 2026Article
- Investigating Phase Separation in Genome Folding via Multiscale Computational Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Multiscale structure of chromatin condensates explains phase separation and material properties.Science (New York, N.Y.) · 2025Article
- Near-atomistic simulations reveal the molecular principles that control chromatin structure and phase separation.bioRxiv : the preprint server for biology · 2025Article
- NEAT-DNA: A Chemically Accurate, Sequence-Dependent Coarse-Grained Model for Large-Scale DNA Simulations.bioRxiv : the preprint server for biology · 2025Article
- Predicting rare DNA conformations via dynamical graphical models: a case study of the B→A transition.Nucleic acids research · 2025Article
- CGeNArateWeb: a web server for the atomistic study of the structure and dynamics of chromatin fibers.Nucleic acids research · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present CGeNArate, a new model for molecular dynamics simulations of very long segments of B-DNA in the context of biotechnological or chromatin studies. The developed method uses a coarse-grained Hamiltonian with trajectories that are back-mapped to the atomistic resolution level with extreme accuracy by means of Machine Learning Approaches. The method is sequence-dependent and reproduces very well not only local, but also global physical properties of DNA. The efficiency of the method allows us to recover with a reduced computational effort high-quality atomic-resolution ensembles of segments containing many kilobases of DNA, entering into the gene range or even the entire DNA of certain cellular organelles.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.