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Related Concept Videos

Cryo-electron Microscopy01:28

Cryo-electron Microscopy

3.4K
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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Exploratory Studies Detecting Secondary Structures in Medium Resolution 3D Cryo-EM Images Using Deep Convolutional Neural Networks.

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CHALLENGES IN MATCHING SECONDARY STRUCTURES IN CRYO-EM: AN EXPLORATION.

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Related Experiment Video

Updated: Aug 21, 2025

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency
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A Pattern Recognition Tool for Medium-resolution Cryo-EM Density Maps and Low-resolution Cryo-ET Density maps.

Devin Haslam1, Salim Sazzed1, Willy Wriggers2

  • 1Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.

Bioinformatics Research and Applications : 14Th International Symposium, ISBRA 2018, Beijing, China, June 8-11, 2018, Proceedings. ISBRA (Conference) (14Th : 2018 : Beijing, China)
|November 16, 2022
PubMed
Summary

This study introduces a new tool for detecting protein secondary structures in cryo-electron microscopy (cryo-EM) density maps. The method enhances the analysis of biological molecules at medium resolutions.

Keywords:
Beta-strandsCryo-electron MicroscopyDensity MapFilamentHelixPattern RecognitionStereocilia

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Single Particle Cryo-Electron Microscopy: From Sample to Structure
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Single Particle Cryo-Electron Microscopy: From Sample to Structure
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Area of Science:

  • Structural Biology
  • Biophysics
  • Computational Biology

Background:

  • Cryo-electron microscopy (Cryo-EM) and cryo-electron tomography (cryo-ET) generate 3-D density maps of biological molecules.
  • Accurate identification of protein secondary structures (helices and β-strands) in medium-resolution (5-10 Å) cryo-EM maps remains a challenge.
  • Pattern recognition tools are crucial for interpreting these volumetric maps.

Purpose of the Study:

  • To develop and integrate a computational tool for detecting and evaluating protein secondary structures in medium-resolution 3-D cryo-EM density maps.
  • To combine existing computational methods for improved accuracy in secondary structure detection.
  • To enhance the analysis capabilities within popular cryo-EM visualization software.

Main Methods:

  • Development of a novel tool integrating SSETracer, StrandTwister, and AxisComparison algorithms.
  • Implementation of the tool within UCSF Chimera, a widely used cryo-EM visualization platform.
  • Related development of BundleTrac for tracing filaments in lower-resolution cryo-ET maps.

Main Results:

  • The developed tool enables the detection and evaluation of protein secondary structures in medium-resolution cryo-EM density maps.
  • Integration into UCSF Chimera provides a user-friendly solution for the cryo-EM community.
  • BundleTrac demonstrated accurate actin filament tracing in stereocilia, showing potential for broader application.

Conclusions:

  • The new tool improves the analysis of protein secondary structures from medium-resolution cryo-EM data.
  • The integration with UCSF Chimera facilitates broader adoption and application in structural biology.
  • Further development, including BundleTrac, promises to enhance cryo-EM/cryo-ET data interpretation.