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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: Jun 28, 2025

Expression and Purification of the Human Lipid-sensitive Cation Channel TRPC3 for Structural Determination by Single-particle Cryo-electron Microscopy

Expression and Purification of the Human Lipid-sensitive Cation Channel TRPC3 for Structural Determination by Single-particle Cryo-electron Microscopy

Published on: January 7, 2019

Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration.

Jason Chen¹, Ayisha Zia², Albert Luo¹

¹Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA.

Briefings in Bioinformatics

|April 12, 2024

Summary

This summary is machine-generated.

DeepTracer-Refine improves protein structure prediction by refining AlphaFold models using DeepTracer's modeled structures. This automated method enhances residue coverage and accuracy, outperforming existing refinement techniques.

Keywords:

AlphaFold DeepTracer cryo-EM protein docking protein structure refinement

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Last Updated: Jun 28, 2025

Expression and Purification of the Human Lipid-sensitive Cation Channel TRPC3 for Structural Determination by Single-particle Cryo-electron Microscopy

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Published on: January 31, 2022

Area of Science:

Structural biology
Computational biology
Biomedical applications

Background:

Protein structure determination is crucial for biomedical applications like vaccine development.
Automating protein structure model building from electron density maps is challenging due to limited atomic resolution in most experimental maps.
Current protein structure prediction tools like AlphaFold2 achieve high accuracy but often require time-consuming manual refinement.

Purpose of the Study:

To develop an automated method for refining protein structures predicted by AlphaFold2.
To improve the accuracy and efficiency of protein structure modeling.

Main Methods:

Developed DeepTracer-Refine, an automated method that refines AlphaFold predicted structures.
Aligned AlphaFold predicted structures with DeepTracer's modeled structures for refinement.
Evaluated the method on 39 multi-domain proteins.

Main Results:

Improved average residue coverage from 78.2% to 90.0%.
Enhanced average local Distance Difference Test (lDDT) score from 0.67 to 0.71.
Demonstrated superior performance and faster run-time compared to Phenix's AlphaFold refinement, especially for less precise initial AlphaFold models.

Conclusions:

DeepTracer-Refine offers an effective automated solution for refining AlphaFold protein structures.
The method significantly improves model accuracy and coverage, addressing limitations of current prediction and refinement techniques.
DeepTracer-Refine presents a faster and more accurate alternative for protein structure refinement in computational biology.