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

作者：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^*

来源：Nature Methods, 2018, 15(12): 1083-+.

DOI：10.1038/s41592-018-0223-8

• BEAM-INDUCED MOTION
• SPECIMEN
• REFINEMENT
• BOUNDS
• TILT

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 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, beta-galactosidase, and an influenza hemagglutinin (HA) trimer, and observed substantial improvement in resolution.

• 出版日期2018-12
• 单位清华大学