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

TL;DR

This paper introduces HetACUMN, a self-supervised variational autoencoder that improves cryo-EM pose estimation and 3D classification by disentangling conformational and pose information, enabling more accurate reconstructions.

Contribution

It presents a novel disentanglement approach using a self-supervised VAE with auxiliary pose prediction for cryo-EM data.

Findings

HetACUMN outperforms existing methods in conformational classification accuracy.

It successfully performs heterogeneous 3D reconstructions on real cryo-EM datasets.

The method reduces computational costs by effective latent space disentanglement.

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.