Wriggers Willy, Alshammari Maytha, Stember Joseph N, Stolzenberg Sebastian, Metwally Essam, Auer Manfred, He Jing, Weinstein Harel
Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, Virginia 23529, United States.
Department of Computer Science, Old Dominion University, Norfolk, Virginia 23529, United States.
J Phys Chem B. 2025 Jan 23;129(3):825-834. doi: 10.1021/acs.jpcb.4c05077. Epub 2025 Jan 10.
ModeHunter is a modular Python software package for the simulation of 3D biophysical motion across spatial resolution scales using modal analysis of elastic networks. It has been curated from our in-house Python scripts over the last 15 years, with a focus on detecting similarities of elastic motion between atomic structures, coarse-grained graphs, and volumetric data obtained from biophysical or biomedical imaging origins, such as electron microscopy or tomography. With ModeHunter, normal modes of biophysical motion can be analyzed with various static visualization techniques or brought to life by dynamics animation in terms of single or multimode trajectories or decoy ensembles. Atomic structures can also be refined against volumetric densities with flexible fitting strategies. The software consists of multiple stand-alone programs for the preparation, analysis, visualization, animation, and refinement of normal modes and 3D data sets. At its core, two spatially reductionist elastic motion engines are currently supported: elastic network models (typically for a Cα level of detail and rectangular meshes) and bend-twist stretch (for trigonal or tetrahedral meshes or trees resulting from spatial clustering). The programs have recently been modernized to Python 3, requiring only the common numpy and scipy external libraries for numerical support. The main advantage of our modular design is that the tools can be combined by the end users for specific modeling applications, either standalone or with complementary tools from our C/C++-based Situs modeling package. The modular design and consistent look and feel facilitate the maintenance of individual programs and the development of novel application workflows. Here, we provide the first complete overview of the ModeHunter package as it exists today, with an emphasis on functionality and workflows supported by version 1.4.
ModeHunter是一个模块化的Python软件包,用于使用弹性网络的模态分析来模拟跨越空间分辨率尺度的3D生物物理运动。它是在过去15年中从我们内部的Python脚本整理而来的,重点是检测原子结构、粗粒度图以及从生物物理或生物医学成像源(如电子显微镜或断层扫描)获得的体积数据之间的弹性运动相似性。使用ModeHunter,可以通过各种静态可视化技术分析生物物理运动的正常模式,或者通过动力学动画将其呈现为单模式或多模式轨迹或诱饵集合。原子结构也可以通过灵活的拟合策略根据体积密度进行优化。该软件由多个独立程序组成,用于正常模式和3D数据集的准备、分析、可视化、动画制作和优化。其核心目前支持两种空间简化的弹性运动引擎:弹性网络模型(通常用于Cα细节级别和矩形网格)和弯曲-扭转-拉伸(用于三角或四面体网格或空间聚类产生的树)。这些程序最近已更新为Python 3,仅需要常见的numpy和scipy外部库提供数值支持。我们模块化设计的主要优点是,最终用户可以将这些工具组合起来用于特定的建模应用,既可以单独使用,也可以与我们基于C/C++的Situs建模包中的补充工具结合使用。模块化设计以及一致的外观和感觉便于单个程序的维护和新应用工作流程的开发。在这里,我们首次全面概述了当前存在的ModeHunter软件包,重点介绍了1.4版本支持的功能和工作流程。