# Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs

arXiv: https://arxiv.org/abs/2106.14108 · HTML: https://mlsanity.com/paper/2106.14108/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2021.

## Built on by (most cited first, 17 of 17)

- [RUST: Latent Neural Scene Representations from Unposed Imagery](https://mlsanity.com/paper/2211.14306/) (2022)
- [CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images](https://mlsanity.com/paper/2203.08138/) (2022)
- [Amortized Inference for Heterogeneous Reconstruction in Cryo-EM](https://mlsanity.com/paper/2210.07387/) (2022)
- [Theoretical Perspectives on Deep Learning Methods in Inverse Problems](https://mlsanity.com/paper/2206.14373/) (2022)
- [Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy](https://mlsanity.com/paper/2201.02867/) (2022)
- [Latent Space Diffusion Models of Cryo-EM Structures](https://mlsanity.com/paper/2211.14169/) (2022)
- [CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference](https://mlsanity.com/paper/2406.10455/) (2024)
- [End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data](https://mlsanity.com/paper/2107.02958/) (2021)
- [Mixture of neural fields for heterogeneous reconstruction in cryo-EM](https://mlsanity.com/paper/2412.09420/) (2024)
- [Heterogeneous reconstruction of deformable atomic models in Cryo-EM](https://mlsanity.com/paper/2209.15121/) (2022)
- [Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM](https://mlsanity.com/paper/2302.06091/) (2023)
- [Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters](https://mlsanity.com/paper/2302.13711/) (2023)
- [Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement](https://mlsanity.com/paper/2308.04956/) (2023)
- [Equivariant amortized inference of poses for cryo-EM](https://mlsanity.com/paper/2406.01630/) (2024)
- [cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM](https://mlsanity.com/paper/2407.01574/) (2024)
- [CryoChains: Heterogeneous Reconstruction of Molecular Assembly of Semi-flexible Chains from Cryo-EM Images](https://mlsanity.com/paper/2306.07274/) (2023)
- [Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstruction](https://mlsanity.com/paper/2602.21915/) (2026)

## Abstract (arXiv)

Cryo-electron microscopy (cryo-EM) has revolutionized experimental protein structure determination. Despite advances in high resolution reconstruction, a majority of cryo-EM experiments provide either a single state of the studied macromolecule, or a relatively small number of its conformations. This reduces the effectiveness of the technique for proteins with flexible regions, which are known to play a key role in protein function. Recent methods for capturing conformational heterogeneity in cryo-EM data model it in volume space, making recovery of continuous atomic structures challenging. Here we present a fully deep-learning-based approach using variational auto-encoders (VAEs) to recover a continuous distribution of atomic protein structures and poses directly from picked particle images and demonstrate its efficacy on realistic simulated data. We hope that methods built on this work will allow incorporation of stronger prior information about protein structure and enable better understanding of non-rigid protein structures.