CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM Via Deep Adversarial Learning
CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM Via Deep Adversarial Learning
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DOI：
10.1109/tci.2021.3096491
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发表时间：
2021-01-01
影响因子：
5.4
通讯作者：
Unser, Michael
中科院分区：
文献类型：
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作者：
Gupta, Harshit;McCann, Michael T.;Unser, Michael
我们提出了CryoGAN，这是一种基于无监督深度对抗学习的单粒子冷冻电子显微镜（cryo-EM）重建的新范例。在单粒子冷冻EM中，生物分子的结构需要从大量具有未知方向的有噪层析投影中重建。当前的重建技术是基于边缘化的最大似然公式，其需要对每个投影图像的所有可能姿态的集合进行计算，这是一个计算要求很高的过程。我们的方法是寻求一个三维结构，具有模拟投影，匹配的真实的数据在分布意义上，从而回避姿态估计或边缘化。我们证明，在一个理想化的数学模型的冷冻EM，这种方法的结果在恢复正确的结构。受分布匹配的启发，我们提出了CryoGAN，这是一种专门的GAN，由3D结构，cryo-EM物理模拟器和神经网络组成。在重建期间，3D结构被优化，使得通过模拟器获得的其投影类似于真实的数据（对于投影仪）。同时，训练鉴别器以区分真实的投影与模拟投影。CryoGAN仅将真实的投影图像和cryo-EM成像参数的分布作为输入。它既不涉及先前的训练，也不涉及对3D结构的初始估计。CryoGAN目前在现实的合成数据集上实现了10.8埃的分辨率。实验β-半乳糖苷酶和80 S核糖体数据的初步结果证明了CryoGAN在标准实验成像条件下利用数据统计的能力。我们相信，这种模式打开了一个家庭的新的可能性无算法冷冻EM重建的大门。
We present CryoGAN, a new paradigm for single-particle cryo-electron microscopy (cryo-EM) reconstruction based on unsupervised deep adversarial learning. In single-particle cryo-EM, the structure of a biomolecule needs to be reconstructed from a large set of noisy tomographic projections with unknown orientations. Current reconstruction techniques are based on a marginalized maximum-likelihood formulation that requires calculations over the set of all possible poses for each projection image, a computationally demanding procedure. Our approach is to seek a 3D structure that has simulated projections that match the real data in a distributional sense, thereby sidestepping pose estimation or marginalization. We prove that, in an idealized mathematical model of cryo-EM, this approach results in recovery of the correct structure. Motivated by distribution matching, we propose CryoGAN, a specialized GAN that consists of a 3D structure, a cryo-EM physics simulator, and a discriminator neural network. During reconstruction, the 3D structure is optimized so that its projections obtained through the simulator resemble real data (to the discriminator). Simultaneously, the discriminator is trained to distinguish real projections from simulated projections. CryoGAN takes as input only real projection images and the distribution of the cryo-EM imaging parameters. It involves neither prior training nor an initial estimation of the 3D structure. CryoGAN currently achieves a 10.8 angstrom resolution on a realistic synthetic dataset. Preliminary results on experimental beta-galactosidase and 80S ribosome data demonstrate the ability of CryoGAN to exploit data statistics under standard experimental imaging conditions. We believe that this paradigm opens the door to a family of novel likelihood-free algorithms for cryo-EM reconstruction.