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

被引:0
作者
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
机构
[1] Tsinghua University,MOE Key Laboratory of Protein Science, School of Life Sciences
[2] Tsinghua University,Advanced Innovation Center for Structural Biology
[3] National Supercomputing Center in Wuxi,Department of Computer Science and Technology
[4] Tsinghua University,Department of Electronic Engineering
[5] Tsinghua University,undefined
[6] Tsinghua-Peking Joint Center for Life Sciences,undefined
来源
Nature Methods | 2018年 / 15卷
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摘要
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.
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页码:1083 / 1089
页数:6
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