CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images.

Axel Levy, Frédéric Poitevin, Julien Martel, Youssef Nashed, Ariana Peck, Nina Miolane, Daniel Ratner, Mike Dunne, Gordon Wetzstein
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Abstract

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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CryoAI:从真实低温电子显微镜图像初始重建三维分子卷的摊销推断姿势。
低温电子显微镜(cryo-EM)已成为结构生物学领域的重要工具,帮助我们了解生命的基本组成。冷冻电子显微镜在算法上面临的挑战是如何从数百万张噪声极高的二维图像中联合估算出生物分子的未知三维姿态和三维电子散射势。然而,由于计算和内存成本高昂,现有的重建算法难以跟上低温电子显微镜数据集快速增长的步伐。我们介绍的 CryoAI 是一种针对同质构象的自证重建算法,它采用基于梯度的直接优化方法,从单粒子低温电子显微镜数据中优化粒子位置和电子散射势。CryoAI 将预测每个粒子图像位置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提高内存效率。该框架与对称损失函数相结合,在模拟和实验数据的质量上与最先进的低温电磁求解器不相上下,在大型数据集上比现有方法快一个数量级,对内存的要求也大大降低。
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