A particle-filter framework for robust cryo-EM 3D reconstruction. (December 2018)

• Record Type: Journal Article
• Authors: Hu, Mingxu; Yu, Hongkun; Gu, Kai; Wang, Zhao; Ruan, Huabin; Wang, Kunpeng; Ren, Siyuan; Li, Bing; Gan, Lin; Xu, Shizhen; Yang, Guangwen; Shen, Yuan; Li, Xueming
• Is Part Of: Nature methods. Volume 15:Number 12(2018)
• Page Start: 1083
• Page End: 1089
• Publication Date: 2018-12
• Subjects: Life sciences -- Methodology -- Periodicals; Life sciences -- Research -- Periodicals; Biology -- Methodology -- Periodicals; Biology -- Research -- Periodicals; 570.72
• DOI: 10.1038/s41592-018-0223-8
• Languages: English
• ISSNs: 1548-7091

Abstract

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.