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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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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

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Related Experiment Video

Updated: Jan 9, 2026

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

EMProt improves structure determination from cryo-EM maps.

Tao Li¹, Ji Chen¹, Hao Li¹

¹School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, China.

Nature Structural & Molecular Biology

|December 8, 2025

Summary

This summary is machine-generated.

We developed EMProt, an automated method for protein structure determination from cryo-electron microscopy (cryo-EM) maps. EMProt accurately models protein structures, outperforming existing methods in map-to-model accuracy and completeness.

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Last Updated: Jan 9, 2026

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Published on: May 29, 2021

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Published on: March 16, 2022

Area of Science:

Structural biology
Biophysics
Computational biology

Background:

Cryo-electron microscopy (cryo-EM) is a leading technique for determining macromolecular structures.
Accurate all-atom protein structure modeling from cryo-EM maps is challenging due to resolution heterogeneity.

Purpose of the Study:

To present EMProt, a fully automated method for precise protein structure determination from cryo-EM data.
To address the limitations of existing methods in handling resolution variations in cryo-EM maps.

Main Methods:

EMProt integrates cryo-EM map information with structure prediction using a three-track attention network.
The method was evaluated on 177 experimental cryo-EM maps with varying resolutions (<4 Å) and complexities (up to 54 chains).

Main Results:

EMProt significantly outperforms state-of-the-art methods like AlphaFold3, DeepMainmast, and ModelAngelo in structure recovery and completeness.
Models generated by EMProt demonstrate high accuracy in fitting cryo-EM maps and pass rigorous structure validation checks.

Conclusions:

EMProt offers a robust and automated solution for accurate protein structure determination from cryo-EM maps.
This advancement is crucial for structural biology and drug discovery, enabling more reliable structural models from experimental data.