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Updated: May 31, 2026

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

Published on: July 19, 2024

Modeling symmetric macromolecular structures in Rosetta3.

Frank DiMaio¹, Andrew Leaver-Fay, Phil Bradley

¹Department of Biochemistry, University of Washington, Seattle, Washington, United States of America. dimaio@u.washington.edu

|July 7, 2011

Summary

This summary is machine-generated.

This study introduces a new framework in Rosetta3 for modeling large, symmetric protein structures, overcoming limitations of traditional methods. The efficient approach enables accurate structure prediction and design for complex biological assemblies.

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Last Updated: May 31, 2026

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

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Published on: July 19, 2024

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Biochemistry
Structural Biology
Computational Biology

Background:

Symmetric protein assemblies are crucial for numerous biochemical functions.
Modeling large symmetric protein systems presents significant computational challenges for conventional methods.

Purpose of the Study:

To present a general and efficient framework for modeling arbitrary symmetric protein systems within Rosetta3.
To detail the implementation and applications of this symmetric modeling capability.

Main Methods:

Developed a general framework in Rosetta3 to handle various types of protein symmetries.
Implemented an efficient computational strategy that restricts search space by sampling only symmetric degrees of freedom.
Simulated only a subset of interacting monomers to reduce computational load.

Main Results:

Successfully implemented a versatile framework for modeling diverse symmetric protein assemblies.
Demonstrated efficient conformational searching by focusing on symmetric degrees of freedom.
Showcased applications in protein structure prediction and design using the new capabilities.

Conclusions:

The Rosetta3 symmetric framework provides an efficient solution for modeling large, complex symmetric protein structures.
This advancement facilitates enhanced structure prediction and design of symmetric biological assemblies.
The paper serves as a guide for utilizing these new capabilities in simulations.