CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images.
CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images.
复制标题
CryoAI：根据真实 Cryo-EM 图像从头开始重建 3D 分子体积的姿势摊销推断。
DOI：
10.1007/978-3-031-19803-8_32
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Wetzstein,Gordon
中科院分区：
文献类型：
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作者：
Levy,Axel;Poitevin,Frédéric;Martel,Julien;Nashed,Youssef;Peck,Ariana;Miolane,Nina;Ratner,Daniel;Dunne,Mike;Wetzstein,Gordon
冷冻电子显微镜 (cryo-EM) 已成为结构生物学中至关重要的工具，帮助我们了解生命的基本组成部分。冷冻电镜的算法挑战是从数百万张极其嘈杂的 2D 图像中联合估计生物分子的未知 3D 姿态和 3D 电子散射势。然而，由于计算和内存成本较高，现有的重建算法无法轻松跟上冷冻电镜数据集规模的快速增长。我们引入了cryoAI，一种用于均匀构象的anab initireconstruction算法，该算法使用直接基于梯度的粒子姿态优化和来自单粒子冷冻电镜数据的电子散射势。 CryoAI 将预测每个粒子图像姿态的学习编码器与基于物理的解码器相结合，将每个粒子图像聚合成散射势体积的隐式表示。该体积存储在傅立叶域中以提高计算效率，并利用现代坐标网络架构来提高内存效率。与对称损失函数相结合，该框架在模拟和实验数据方面获得了与最先进的冷冻电镜求解器相当的质量结果，对于大型数据集来说速度快了一个数量级，并且比现有方法的内存需求显着降低。
Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D electron scattering potential of a biomolecule from millions of extremely noisy 2D images. Existing reconstruction algorithms, however, cannot easily keep pace with the rapidly growing size of cryo-EM datasets due to their high computational and memory cost. We introduce cryoAI, anab initioreconstruction algorithm for homogeneous conformations that uses direct gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data. CryoAI combines a learned encoder that predicts the poses of each particle image with a physics-based decoder to aggregate each particle image into an implicit representation of the scattering potential volume. This volume is stored in the Fourier domain for computational efficiency and leverages a modern coordinate network architecture for memory efficiency. Combined with a symmetric loss function, this framework achieves results of a quality on par with state-of-the-art cryo-EM solvers for both simulated and experimental data, one order of magnitude faster for large datasets and with significantly lower memory requirements than existing methods.