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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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Related Experiment Video

Updated: Jan 6, 2026

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion

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Published on: January 31, 2022

CryoLike: a Python package for cryo-electron microscopy image-to-structure likelihood calculations.

Wai Shing Tang¹, Jeff Soules¹, Aaditya Rangan¹

¹Center for Computational Mathematics, Flatiron Institute, New York, NY 10010, USA.

Acta Crystallographica. Section D, Structural Biology

|November 19, 2025

Summary

This summary is machine-generated.

CryoLike software efficiently assesses image-to-structure likelihoods for flexible biomolecules using Bayesian methods. This computational tool aids in analyzing conformational heterogeneity from cryo-electron microscopy (cryo-EM) data.

Keywords:

Bayesian inference cryo-EM molecular dynamics molecular modeling

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Area of Science:

Structural biology
Biophysics
Computational imaging

Background:

Conformational heterogeneity in flexible biomolecules presents a significant challenge for traditional cryo-electron microscopy (cryo-EM) 3D classification.
Bayesian approaches offer physically interpretable insights into cryo-EM heterogeneity but can be computationally intensive.
Fourier-Bessel image-representation methods have been developed to address computational costs.

Purpose of the Study:

To develop computationally efficient software for evaluating image-to-structure likelihoods in cryo-EM.
To reduce the computational burden associated with Bayesian methods for analyzing biomolecular heterogeneity.
To provide a user-friendly Python workflow for processing large cryo-EM datasets.

Main Methods:

Development of CryoLike software, a computationally efficient tool.
Leveraging Fourier-Bessel image-representation techniques.
Implementation within a user-friendly Python workflow for large-scale data analysis.

Main Results:

CryoLike significantly reduces computational cost for Bayesian heterogeneity analysis.
The software enables efficient evaluation of image-to-structure likelihoods.
Facilitates the analysis of conformational heterogeneity in flexible biomolecules.

Conclusions:

CryoLike offers an efficient and accessible solution for tackling conformational heterogeneity in cryo-EM.
The software empowers researchers to gain deeper insights into the structural dynamics of flexible biomolecules.
Advancements in computational tools like CryoLike are crucial for pushing the boundaries of cryo-EM analysis.