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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...

Electron Microscope Tomography and Single-particle Reconstruction

Electron Microscope Tomography and Single-particle Reconstruction

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: May 30, 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

AI-based methods for biomolecular structure modeling for Cryo-EM.

Farhanaz Farheen¹, Genki Terashi², Han Zhu¹

¹Department of Computer Science, Purdue University, West Lafayette, IN, USA.

Current Opinion in Structural Biology

|January 26, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) enhances cryo-electron microscopy (Cryo-EM) data processing for determining complex macromolecular structures. This review highlights AI

Keywords:

AI Artificial intelligence Cryo-EM Deep learning Structural heterogeneity Structure modeling Structure validation

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

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

Published on: January 9, 2015

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Last Updated: May 30, 2025

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

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

Published on: January 9, 2015

Area of Science:

Structural biology
Biophysics
Computational biology

Background:

Cryo-electron microscopy (Cryo-EM) is a powerful technique for determining high-resolution structures of biological macromolecules.
Traditional Cryo-EM data processing involves complex computational steps, often limiting throughput and resolution.
Advancements in artificial intelligence (AI), particularly deep learning, offer new possibilities for optimizing Cryo-EM workflows.

Purpose of the Study:

To review state-of-the-art AI-based techniques applied to key Cryo-EM data processing steps.
To highlight the impact of AI on improving macromolecular structure modeling and heterogeneity analysis.
To provide insights into the current landscape and future directions of AI in Cryo-EM.

Main Methods:

Review of recent literature on AI applications in Cryo-EM data processing.
Focus on deep learning algorithms for image processing, particle picking, and 3D reconstruction.
Discussion of AI-driven approaches for analyzing structural heterogeneity within datasets.

Main Results:

AI significantly enhances the accuracy and efficiency of various Cryo-EM data processing steps.
Deep learning models show particular promise in improving particle identification and 3D model quality.
AI facilitates more robust analysis of conformational heterogeneity in macromolecular complexes.

Conclusions:

AI is transforming Cryo-EM by accelerating structure determination and enabling analysis of previously intractable biological systems.
Continued development and integration of AI tools will be crucial for advancing structural biology.
AI-powered Cryo-EM is poised to unlock new biological insights through high-resolution structural determination.