Due to the extremely low signal-to-noise ratio (SNR) and unknown poses (projection angles and image shifts) in cryo-electron microscopy (cryo-EM) experiments, reconstructing 3D volumes from 2D images is very challenging. In addition to these challenges, heterogeneous cryo-EM reconstruction requires conformational classification. In popular cryo-EM reconstruction algorithms, poses and conformation classification labels must be predicted for every input cryo-EM image, which can be computationally costly for large datasets. An emerging class of methods adopted the amortized inference approach. In these methods, only a subset of the input dataset is needed to train neural networks for the estimation of poses and conformations. Once trained, these neural networks can make pose/conformation predictions and 3D reconstructions at low cost for the entire dataset during inference. Unfortunately, when facing heterogeneous reconstruction tasks, it is hard for current amortized-inference-based methods to effectively estimate the conformational distribution and poses from entangled latent variables. Here, we propose a self-supervised variational autoencoder architecture called "HetACUMN" based on amortized inference. We employed an auxiliary conditional pose prediction task by inverting the order of encoder-decoder to explicitly enforce the disentanglement of conformation and pose predictions. Results on simulated datasets show that HetACUMN generated more accurate conformational classifications than other amortized or non-amortized methods. Furthermore, we show that HetACUMN is capable of performing heterogeneous 3D reconstructions of a real experimental dataset.

翻译：由于冷冻电镜(cryo-EM)实验中信噪比(SNR)极低且姿态（投影角度与图像偏移）未知，从二维图像重建三维体素极具挑战。除上述难点外，异质性冷冻电镜重建还需进行构象分类。在主流冷冻电镜重建算法中，每个输入图像的姿态与构象分类标签均需预测，这对大规模数据集而言计算成本高昂。一类新兴方法采用摊销推断策略：此类方法仅需输入数据集的子集训练神经网络以估计姿态与构象，训练完成后即可低成本地对全数据集进行姿态/构象预测与三维重建。然而，面对异质性重建任务时，现有基于摊销推断的方法难以从纠缠的隐变量中有效估计构象分布与姿态。本文提出一种基于摊销推断的自监督变分自编码器架构"HetACUMN"，通过逆序编解码器设计引入辅助条件姿态预测任务，显式强制构象与姿态预测的解耦。模拟数据集实验表明，HetACUMN生成的构象分类准确度优于其他摊销或非摊销方法。此外，我们证实HetACUMN能对真实实验数据集进行异质性三维重建。