Van Cong T S, Reboul Cyril F, Caesar Joseph J E, Meana-Pañeda Rubén, Lountos George T, Deme Justin C, Bryant Owain J, Johnson Steven, Piczak Claire T, Valkov Eugene, Lea Susan M, Elmlund Hans
National Cancer Institute, National Institutes of Health, Bethesda, MD 21701, USA.
Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA.
Acta Crystallogr D Struct Biol. 2025 Aug 1;81(Pt 8):396-409. doi: 10.1107/S2059798325005686. Epub 2025 Jul 7.
Three-dimensional (3D) structure determination by single-particle analysis of cryo-electron microscopy (cryo-EM) images requires ab initio 3D reconstruction of density volume(s) from 2D images (particles). This large-scale inverse problem requires the determination of many million degrees of freedom from extremely noisy experimental measurements. Here, we introduce a new approach to probabilistic multi-volume ab initio 3D reconstruction for simultaneous estimation of the relative particle 3D orientations and partitioning of the particles into groups with distinct structural states. To account for further structural variability within the discrete state groups, due to for example regional disorder, flexibility or partial occupancy of associating ligands, we introduce a new method for adaptive non-uniform regularization based on iterated conditional modes (ICMs). Our ICM regularization approach can be viewed as a spatially varying real-space prior that optimizes the connectivity of the reconstructed density map(s). Our method is designed to run in real time as the microscope collects the data, which puts significant constraints on algorithm scalability and flexibility with regard to how new particles are incorporated. We describe the probabilistic optimization and non-uniform regularization theory in detail. Finally, we provide numerous benchmarking examples, both on publicly available standard test data sets and on data sets acquired at our cryo-EM facility at the National Cancer Institute, National Institutes of Health. The implementation of our new multi-volume ab initio 3D reconstruction approach is part of the SIMPLE software suite, which is provided open source at https://github.com/hael/SIMPLE.
通过对冷冻电子显微镜（cryo-EM）图像进行单颗粒分析来确定三维（3D）结构，需要从二维图像（颗粒）中对密度体积进行从头开始的三维重建。这个大规模的逆问题需要从极其嘈杂的实验测量中确定数百万个自由度。在这里，我们引入了一种新的概率多体积从头开始三维重建方法，用于同时估计相对颗粒三维取向以及将颗粒划分为具有不同结构状态的组。为了考虑离散状态组内进一步的结构变异性，例如由于区域无序、灵活性或结合配体的部分占据，我们引入了一种基于迭代条件模式（ICM）的自适应非均匀正则化新方法。我们的ICM正则化方法可以看作是一种空间变化的实空间先验，它优化了重建密度图的连通性。我们的方法设计为在显微镜收集数据时实时运行，这对算法的可扩展性以及关于如何纳入新颗粒的灵活性提出了重大限制。我们详细描述了概率优化和非均匀正则化理论。最后，我们在公开可用的标准测试数据集以及我们在美国国立卫生研究院国家癌症研究所的冷冻电子显微镜设施获取的数据集上提供了大量基准测试示例。我们新的多体积从头开始三维重建方法的实现是SIMPLE软件套件的一部分，该套件在https://github.com/hael/SIMPLE上开源提供。
