2026-04-19T11:38:56Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/2218412025-12-04T21:15:49Zcom_2072_1057col_2072_478781col_2072_478908col_2072_478917

00925njm 22002777a 4500 dc Farré Gil, David author Arcon, Juan Pablo author Laughton, Charles A. author Orozco López, Modesto author 2025-06-27T13:15:45Z 2025-06-27T13:15:45Z 2024-07-08 2025-06-27T13:15:45Z 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. Biotecnologia Sistemes hamiltonians Dinàmica molecular Cromatina Biotechnology Hamiltonian systems Molecular dynamics Chromatin CGeNArate: a sequence-dependent coarse-grained model of DNA for accurate atomistic MD simulations of kb-long duplexes