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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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EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps.

Jiahua He¹, Sheng-You Huang¹

¹School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China.

Briefings in Bioinformatics

|May 6, 2021

Summary

A new framework, EMNUSS, accurately identifies protein secondary structures in cryo-electron microscopy (cryo-EM) maps. This tool aids in building precise structural models from cryo-EM data across various resolutions.

Keywords:

EM maps cryo-electron microscopy (cryo-EM)deep learning nested U-net secondary structure

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Last Updated: Nov 6, 2025

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Area of Science:

Structural Biology
Biophysics
Computational Biology

Background:

Cryo-electron microscopy (cryo-EM) is crucial for molecular structure determination.
Advances in instrumentation and algorithms have increased cryo-EM map deposition.
Accurate structure model building from cryo-EM maps remains a significant challenge.

Purpose of the Study:

To develop a novel framework for secondary structure annotation in cryo-EM maps.
To improve the accuracy and robustness of cryo-EM structure modeling.
To provide a tool applicable to both intermediate and high-resolution cryo-EM maps.

Main Methods:

Development of EMNUSS, a secondary structure annotation framework.
Implementation of a three-dimensional (3D) nested U-net architecture.
Validation using simulated, middle-resolution, and high-resolution experimental cryo-EM datasets.

Main Results:

EMNUSS demonstrated high accuracy in identifying secondary structures from cryo-EM maps.
The framework proved robust across diverse datasets and resolutions.
Successful annotation of protein secondary structures was achieved.

Conclusions:

EMNUSS is an effective tool for secondary structure assignment in cryo-EM maps.
The framework facilitates more accurate cryo-EM structure modeling.
EMNUSS is freely available to the scientific community.