CryoAI：从冷冻电镜图像从头计算重建三维分子结构的位姿推断
CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images
摘要：
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。cryo-EM的算法挑战是从数百万个噪声极大的2D图像中联合估计未知的3D姿态和生物分子的3D电子散射势。然而，现有的重建算法，不能很容易地跟上快速增长的冷冻EM数据集的大小，由于其高的计算和内存成本。我们介绍cryoAI，一种从头计算的均匀构象重建算法，使用直接基于梯度的优化粒子姿态和电子散射势从单粒子cryo-EM数据。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为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以提高内存效率。结合对称化的损失函数，该框架实现了与模拟和实验数据的最先进的冷冻EM求解器同等质量的结果，对于大型数据集快一个数量级，并且比现有方法的内存需求显著降低。
英文摘要：
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, an ab initio reconstruction 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 symmetrized 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.
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