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Cryo-electron Microscopy

Cryo-electron Microscopy

Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...

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Related Experiment Video

Updated: Jan 11, 2026

Cryo-EM and Single-Particle Analysis with Scipion

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

CryoAtom improves model building for cryo-EM.

Baoquan Su¹, Kun Huang², Zhenling Peng³

¹MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China.

Nature Structural & Molecular Biology

|November 14, 2025

Summary

This summary is machine-generated.

CryoAtom enhances atomic model building for cryo-electron microscopy (cryo-EM) maps by adapting AlphaFold2. This novel method improves model completeness and accuracy, even at lower resolutions, accelerating protein structure determination.

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Last Updated: Jan 11, 2026

Cryo-EM and Single-Particle Analysis with Scipion

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Published on: May 29, 2021

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Published on: April 11, 2025

Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction

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Published on: January 9, 2015

Area of Science:

Structural Biology
Biophysics
Computational Biology

Background:

Atomic model construction from cryo-electron microscopy (cryo-EM) density maps is crucial for understanding molecular mechanisms.
Existing methods for de novo model building in cryo-EM face limitations in completeness and resolution requirements.

Purpose of the Study:

To introduce CryoAtom, a novel computational approach for de novo model building in cryo-EM maps.
To leverage advancements in AlphaFold2, adapting it for enhanced performance with cryo-EM data.

Main Methods:

CryoAtom modifies AlphaFold2 by replacing global attention with local attention mechanisms.
A novel three-dimensional rotary position embedding is incorporated to better utilize cryo-EM map information.
The approach was tested on three large cryo-EM maps.

Main Results:

CryoAtom generates more complete atomic models and reduces the need for high-resolution maps.
The method successfully identified previously uncharacterized proteins and modeled conformational changes.
It demonstrated efficient modeling of large complexes (e.g., 104 proteins) and isolation of non-protein components.

Conclusions:

CryoAtom represents a significant advancement in accurate model building for cryo-EM structure determination.
The method accelerates the process and improves the quality of atomic models derived from cryo-EM data.
CryoAtom is publicly available, facilitating its adoption in structural biology research.