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Cryo-electron Microscopy

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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: Jun 7, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

Secondary Structure Detection and Structure Modeling for Cryo-EM.

Pranav Punuru¹, Anika Jain¹, Daisuke Kihara^2,3

¹Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.

Methods in Molecular Biology (Clifton, N.J.)

|November 14, 2024

Summary

This summary is machine-generated.

Deep learning tools aid structural biology by analyzing cryogenic electron microscopy (cryo-EM) maps. These AI models help model protein structures from low-resolution cryo-EM data, improving accuracy.

Keywords:

CryoREAD DAQ Deep learning DeepMainmast Emap2sec Structural biology Structure detection Structure modeling cryo-EM

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Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

Area of Science:

Structural Biology
Biophysics
Computational Biology

Background:

Cryogenic electron microscopy (cryo-EM) enables high-resolution determination of macromolecular structures.
Manual atomic modeling of cryo-EM density maps is challenging at resolutions below 3 Å.
Lower resolution cryo-EM maps require advanced methods for accurate structure interpretation.

Purpose of the Study:

To present a suite of deep learning tools for analyzing cryo-EM density maps across various resolutions.
To assist structural biologists in modeling protein structures from challenging, lower-resolution cryo-EM data.
To provide accessible computational tools for cryo-EM data interpretation.

Main Methods:

Development of deep learning algorithms for identifying structural features in cryo-EM maps.
DeepMainmast: an automated tool for all-atom structure modeling from near-atomic resolution (≤5 Å) cryo-EM maps.
Emap2sec and Emap2sec+: tools for detecting protein secondary structures and nucleic acids in intermediate resolution (5-10 Å) maps.
DAQ score for quantifying map-model fit and identifying potential modeling errors.

Main Results:

Deep learning successfully identifies local map features for amino acids and atoms.
DeepMainmast automatically models protein main chains from high-resolution cryo-EM maps.
Emap2sec and Emap2sec+ accurately detect secondary structures and nucleic acids in intermediate-resolution maps.
The DAQ score effectively evaluates the quality of cryo-EM map-model correlations.

Conclusions:

Deep learning-based tools significantly enhance the analysis of cryo-EM density maps, especially at lower resolutions.
The developed suite of tools, including DeepMainmast and Emap2sec, provides valuable assistance for structural biologists.
These computational resources are accessible via a web server, promoting wider adoption in structural biology research.