Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement

• URL: http://arxiv.org/abs/2308.04956v3
• Date: Tue, 23 Apr 2024 02:51:28 GMT
• Title: Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement
• Authors: Weijie Chen, Yuhang Wang, Lin Yao,
• Abstract summary: We propose a self-supervised variational autoencoder architecture called "HetACUMN" based on amortized inference. Results on simulated datasets show that HetACUMN generated more accurate conformational classifications than other amortized or non-amortized methods.
• Score: 14.973360669658561
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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