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.

中文翻译：

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CGeNArate：一种依赖于序列的 DNA 粗粒度模型，用于对 kb 长双链体进行精确的原子 MD 模拟


我们推出了 CGeNArate，这是一种在生物技术或染色质研究背景下对非常长的 B-DNA 片段进行分子动力学模拟的新模型。所开发的方法使用粗粒度哈密顿量，其轨迹通过机器学习方法以极高的精度反向映射到原子分辨率级别。该方法是序列依赖性的，不仅可以很好地再现 DNA 的局部物理特性，而且可以很好地再现 DNA 的整体物理特性。该方法的效率使我们能够以减少的计算量恢复包含数千碱基 DNA 的高质量原子分辨率片段集合，进入基因范围甚至某些细胞器的整个 DNA。