Single-particle electron cryomicroscopy (cryo-EM) involves estimating a set of parameters for each particle image and reconstructing a 3D density map; robust algorithms with accurate parameter estimation are essential for high resolution and automation. We introduce a particle-filter algorithm for cryo-EM, which provides high-dimensional parameter estimation through a posterior probability density function (PDF) of the parameters given in the model and the experimental image. The framework uses a set of random support points to represent such a PDF and assigns weighting coefficients not only among the parameters of each particle but also among different particles. We implemented the algorithm in a new program named THUNDER, which features self-adaptive parameter adjustment, tolerance to bad particles, and per-particle defocus refinement. We tested the algorithm by using cryo-EM datasets for the cyclic-nucleotide-gated (CNG) channel, the proteasome, β-galactosidase, and an influenza hemagglutinin (HA) trimer, and observed substantial improvement in resolution.

单粒子电子低温显微镜（cryo-EM）涉及为每个粒子图像估计一组参数并重建3D密度图。具有精确参数估计功能的强大算法对于高分辨率和自动化至关重要。我们介绍了一种用于cryo-EM的粒子滤波算法，该算法通过模型和实验图像中给出的参数的后验概率密度函数（PDF）提供高维参数估计。该框架使用一组随机支持点来表示此类PDF，并且不仅在每个粒子的参数之间而且在不同粒子之间分配加权系数。我们在名为THUNDER的新程序中实现了该算法，该程序具有自适应参数调整，对不良颗粒的耐受性和逐颗粒散焦的改进。