MOE Key Laboratory of Protein Science, School of Life Sciences, Tsinghua University, Beijing, China.
Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, China.
Nat Methods. 2018 Dec;15(12):1083-1089. doi: 10.1038/s41592-018-0223-8. Epub 2018 Nov 30.
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.
单颗粒电子冷冻显微镜（cryo-EM）涉及估计每个粒子图像的一组参数并重建一个 3D 密度图；具有准确参数估计的强大算法对于高分辨率和自动化至关重要。我们介绍了一种用于 cryo-EM 的粒子滤波算法，该算法通过模型和实验图像中给出的参数的后验概率密度函数（PDF）提供高维参数估计。该框架使用一组随机支撑点来表示这样的 PDF，并不仅为每个粒子的参数，而且为不同的粒子分配加权系数。我们在一个名为 THUNDER 的新程序中实现了该算法，该程序具有自适应参数调整、对不良粒子的容忍度以及每个粒子的离焦细化功能。我们使用环核苷酸门控（CNG）通道、蛋白酶体、β-半乳糖苷酶和流感血凝素（HA）三聚体的 cryo-EM 数据集测试了该算法，并观察到分辨率有了显著提高。
