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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The entry codes of the datasets from EMPIAR are EMPIAR-10025, EMPIAR-10061, and EMPIAR-10097. The entry codes of the density maps from the EMDB are EMD-6656, EMD-2984, EMD-6287, and EMD-8731. The entry codes of the structure models from the Protein Data Bank are PDB 1PMA, PDB 3WHE, PDB 5H3O, and PDB 5A1A. The calculated density maps that support the findings of this study are available as Supplementary Data and from the corresponding author upon reasonable request.
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This work was supported by funds from The National Key Research and Development Program (2016YFA0501102 and 2016YFA0501902 to X.L.), National Natural Science Foundation of China (31722015 and 31570730 to X.L., and 61672312 to G.Y.), Advanced Innovation Center for Structural Biology (to X.L., Y.S., and G.Y.), Tsinghua-Peking Joint Center for Life Sciences (to X.L.), One-Thousand Talent Program through the State Council of China (to X.L. and Y.S.), and Intel Parallel Computing Center project (to X.L.). We thank Y. Cheng (University of California San Francisco) for providing the T20S proteasome sample, and X. Zhou and C. Lin (Tsinghua University) for collecting the T20S proteasome data on CCD camera. We thank X. Lin (Intel) for help in optimizing the C++ code of THUNDER. We acknowledge the National Supercomputing Center in Wuxi and the Tsinghua University Branch of the China National Center for Protein Sciences Beijing for providing facility support in computation.