期刊
NATURE METHODS
卷 15, 期 12, 页码 1083-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41592-018-0223-8
关键词
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资金
- National Key Research and Development Program [2016YFA0501102, 2016YFA0501902]
- National Natural Science Foundation of China [31722015, 31570730, 61672312]
- Advanced Innovation Center for Structural Biology
- Tsinghua-Peking Joint Center for Life Sciences
- One-Thousand Talent Program through the State Council of China
- Intel Parallel Computing Center project
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, beta-galactosidase, and an influenza hemagglutinin (HA) trimer, and observed substantial improvement in resolution.
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