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Modeling Ligands into Maps Derived from Electron Cryomicroscopy

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Published on: July 19, 2024

Computational methods for constructing protein structure models from 3D electron microscopy maps.

Juan Esquivel-Rodríguez¹, Daisuke Kihara

¹Department of Computer Science, College of Science, Purdue University, West Lafayette, IN 47907, USA.

Journal of Structural Biology

|June 26, 2013

Summary

This summary is machine-generated.

Cryo-electron microscopy (EM) now achieves high-resolution protein structure determination. This review covers computational methods for modeling 3D protein structures from EM density maps, including de novo and fitting approaches.

Keywords:

3D Zernike descriptor 3DZD CATH Class, Architecture, Topology, Homologous superfamily. Acronym for the CATH protein structure database Computational algorithm DEN Deformable Elastic Network EM EMDB ENM Elastic Network Model Electron Microscopy Data Bank Electron density map Electron microscopy MC MD MDFF Macromolecular structure modeling Monte Carlo NMA NMFF NMR Normal Mode Flexible Fitting Nuclear Magnetic Resonance PDB Protein Data Bank RMSD Root Mean Square Deviation SVM Structure fitting electron microscopy molecular dynamics molecular dynamics flexible fitting normal mode analysis support vector machine

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Single Particle Cryo-Electron Microscopy: From Sample to Structure

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Published on: May 29, 2021

Area of Science:

Structural Biology
Biophysics
Computational Biology

Background:

Cryo-electron microscopy (EM) has advanced significantly, enabling high-resolution protein structure determination.
Recent cryo-EM structures are solved at resolutions approaching 3Å, showcasing technological progress.
Interpreting cryo-EM data relies heavily on computational methods.

Purpose of the Study:

To review computational methods for modeling protein 3D structures from 3D cryo-EM density maps.
To categorize these methods into de novo and structure fitting approaches.
To provide a list of available computational tools for structure modeling.

Main Methods:

Focus on computational procedures for interpreting 3D EM density maps.
Discussion includes de novo methods for identifying structural elements within EM maps.
Coverage extends to structure fitting methods for incorporating known structures into low-resolution EM maps.

Main Results:

A comprehensive overview of computational strategies for protein structure modeling from cryo-EM data.
Categorization of methods based on their approach to interpreting EM density maps.
Identification and listing of relevant computational tools.

Conclusions:

Computational methods are crucial for advancing protein structure determination using cryo-EM.
A range of de novo and fitting methods are available for modeling structures.
The review provides valuable resources for researchers in the field.