(For a full list of source code and simulation results from our publications, go to the group’s GitHub Page)
DRAGON is a software package to enable De novo, and RAtional prediction of Genome organizatiON. It provides an implementation of the model proposed in the manuscript to simulate chromatin structure and dynamics. With DRAGON, one can predict cell type specific chromosome structures using genome-wide profiles of histone modifications and CTCF molecules.
The package is mainly written in Python, and it streamlines all the necessary steps to process epigenomics data, to perform molecular dynamics simulations and to analyze predicted conformational ensemble for the chromatin.
OpenABC stands for OpenMM GPU-Accelerated simulations of Biomolecular Condensates. It is flexible and implements multiple popular coarse-grained force fields for simulations, including the hydropathy scale (HPS) model, MOFF Ca model, and the molecular renormalization group (MRG)-CG DNA model. The package dramatically simplifies the simulation setup: users only need a few lines of python code to carry out condensate simulations starting from initial configurations of a single protein or DNA. The package is integrated with OpenMM, a GPU-accelerated MD simulation engine, enabling efficient simulations with advanced sampling techniques. We include tools for converting coarse-grained configurations to atomistic structures for further simulations with all-atom force fields. We provide tutorials in Jupyter notebooks to demonstrate the various capabilities. We anticipate OpenABC to significantly facilitate the application of existing computer models for simulating biomolecular condensates and the continued development of new force fields.
OpenNucleome is an open-source software designed for conducting molecular dynamics (MD) simulations of the human nucleus. This software streamlines the process of setting up whole nucleus simulations through just a few lines of Python scripting. OpenNucleome can unveil intricate, high-resolution structural and dynamic chromosome arrangements at a 100 KB resolution. It empowers researchers to track the kinetics of condensate formation and fusion while also exploring the influence of chemical modifications on condensate stability. Furthermore, it facilitates the examination of nuclear envelope deformation’s impact on genome organization. The software’s modular architecture enhances its adaptability and extensibility. Leveraging the power of OpenMM, a GPU-accelerated MD engine, OpenNucleome ensures efficient simulations.
Potential contrasting is an efficient method for learning a potential energy function that can reproduce an ensemble of molecular conformations (Ding and Zhang, JCTC, 2022). It can be easily applied to can learn coarse-grained force fields based on all-atom simulations. It generalizes the noise contrastive estimation method to use complex unnormalized noise distributions constructed using molecular dynamics techniques such as umbrella sampling.