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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: Aug 2, 2025

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

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Published on: January 9, 2015

CryoFold 2.0: Cryo-EM Structure Determination with MELD.

Liwei Chang¹, Arup Mondal¹, Justin L MacCallum²

¹Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States.

The Journal of Physical Chemistry. A

|April 21, 2023

Summary

New CryoFold 2.0 software integrates Bayesian inference and molecular dynamics flexible fitting for accurate macromolecular structure determination from cryo-electron microscopy data, improving efficiency and reducing computational needs.

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Last Updated: Aug 2, 2025

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

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Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Cryo-electron microscopy (cryo-EM) is increasingly vital for determining macromolecular structures.
Existing computational tools are often limited to high-resolution cryo-EM maps and struggle with map heterogeneity.
Novel methods are required to interpret lower-resolution and heterogeneous cryo-EM data.

Purpose of the Study:

To introduce CryoFold 2.0, an advanced computational approach for atomic-level structure determination using cryo-EM data.
To enhance the efficiency and accuracy of macromolecular structure modeling by integrating multiple data sources.
To provide a user-friendly and computationally economical solution for cryo-EM structure analysis.

Main Methods:

CryoFold 2.0 employs an integrative, physics-based strategy combining Bayesian inference with molecular dynamics flexible fitting (MDFF).
The approach is integrated into the MELD (modeling employing limited data) plugin for streamlined workflow.
It leverages multiple data sources and handles map heterogeneity effectively.

Main Results:

CryoFold 2.0 demonstrates superior computational efficiency and accuracy compared to standalone MELD or MDFF.
The method requires fewer computational resources and reduced simulation times compared to the original CryoFold.
Effectiveness validated across eight diverse biological systems, showcasing broad applicability.

Conclusions:

CryoFold 2.0 offers a significant advancement in analyzing cryo-EM data for macromolecular structure determination.
The integrated MELD pipeline provides a more efficient, accurate, and resource-conscious solution.
This tool facilitates broader accessibility and deeper insights into biological structures from cryo-EM data.