# Computational Methods for Single-Particle Cryo-EM

arXiv: https://arxiv.org/abs/2003.13828 · HTML: https://mlsanity.com/paper/2003.13828/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction](https://mlsanity.com/paper/2101.03549/) (2021)
- [Exploring the Adjugate Matrix Approach to Quaternion Pose Extraction](https://mlsanity.com/paper/2205.09116/) (2022)

## Abstract (arXiv)

Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional structure of proteins and other biologically significant complexes at near-atomic resolution. It is an imaging method that does not require crystallization and can capture molecules in their native states. In single-particle cryo-EM, the three-dimensional molecular structure needs to be determined from many noisy two-dimensional tomographic projections of individual molecules, whose orientations and positions are unknown. The high level of noise and the unknown pose parameters are two key elements that make reconstruction a challenging computational problem. Even more challenging is the inference of structural variability and flexible motions when the individual molecules being imaged are in different conformational states. This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing that also play a significant role in many other data science applications.