A particle-filter framework for robust cryo-EM 3D reconstruction

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Mingxu Hu, Hongkun Yu, Kai Gu, Zhao Wang, Huabin Ruan, Kunpeng Wang, Siyuan Ren, Bing Li, Lin Gan, Shizhen Xu, Guangwen Yang, Yuan Shen, Xueming Li

Published: 01 Dec 2018, Last Modified: 27 Jan 2026Nature MethodsEveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: 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. A particle-filter algorithm for single-particle cryo-electron microscopy, implemented in a tool called THUNDER, provides high-dimensional parameter estimation, improving the obtainable resolution for several protein structures.