A particle-filter framework for robust cryo-EM 3D reconstruction
A particle-filter framework for robust cryo-EM 3D reconstruction
复制标题
用于稳健冷冻电镜 3D 重建的粒子滤波器框架
DOI：
10.1038/s41592-018-0223-8
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发表时间：
2018-12-01
期刊：
影响因子：
48
通讯作者：
Li, Xueming
中科院分区：
文献类型：
--
作者：
Hu, Mingxu;Yu, Hongkun;Li, Xueming
单粒子电子低温显微镜（cryo-EM）涉及估计每个粒子图像的一组参数并重建3D密度图;具有准确参数估计的鲁棒算法对于高分辨率和自动化至关重要。我们介绍了一种粒子滤波算法的冷冻EM，它提供了高维参数估计通过后验概率密度函数（PDF）的模型和实验图像中的参数。该框架使用一组随机的支持点来表示这样的PDF，并分配权重系数，不仅在每个粒子的参数，而且在不同的粒子。我们在一个新的程序THUNDER中实现了该算法，该程序具有自适应参数调整、对坏粒子的容忍以及对每个粒子的散焦细化等功能。我们通过使用环核苷酸门控（CNG）通道、蛋白酶体、β-半乳糖苷酶和流感血凝素（HA）三聚体的冷冻EM数据集测试了该算法，并观察到分辨率的大幅提高。
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.