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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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Updated: May 2, 2026

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Finding antibodies in cryo-EM maps with CrAI.

Vincent Mallet¹, Chiara Rapisarda², Hervé Minoux²

¹LIX, Ecole Polytechnique, IPP Paris, Palaiseau, 91120, France.

Bioinformatics (Oxford, England)

|April 9, 2025

Summary

CrAI is a new machine learning tool that automatically identifies therapeutic antibodies in cryo-electron microscopy (cryo-EM) maps. This efficient method significantly speeds up structural analysis of antibodies, improving drug discovery pipelines.

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Last Updated: May 2, 2026

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Area of Science:

Structural biology
Computational biology
Drug discovery

Background:

Therapeutic antibodies are crucial drugs, refined using structural information.
Cryo-electron microscopy (cryo-EM) generates 3D maps but can be noisy and artifact-prone.
Accurate identification of antibodies within cryo-EM maps is essential for interpretation but challenging for existing automated methods.

Purpose of the Study:

To develop a fully automatic and efficient method for identifying antibodies in cryo-EM maps.
To overcome the limitations of existing automated methods in terms of accuracy, input requirements, and processing time.

Main Methods:

Developed CrAI, a novel machine learning approach.
Leveraged conserved antibody structures and a custom-built database.
Designed for seamless integration into automated analysis pipelines.

Main Results:

CrAI is the first fully automatic and efficient method for finding antibodies in cryo-EM maps.
Predictions are completed in seconds, a significant improvement over hours.
The method accurately identifies both Fabs and VHHs at resolutions up to 10 Å.
CrAI demonstrates significantly higher reliability compared to existing approaches.

Conclusions:

CrAI offers a fast, accurate, and reliable solution for antibody identification in cryo-EM data.
The method simplifies and accelerates the structural analysis of antibodies.
CrAI is available as open-source software and a ChimeraX bundle.