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

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Published on: July 25, 2013

Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems.

¹Department of Biochemistry, Institute of Protein Design, University of Washington, Seattle, WA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|June 3, 2017

Summary

This summary is machine-generated.

Molecular replacement (MR) is key for X-ray crystallography, but difficult cases benefit from protein structure prediction. Rosetta offers tools for model building and refinement in challenging MR scenarios.

Keywords:

Molecular replacement Protein structure determination Structure refinement

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Structural Biology
Biophysics
Computational Biology

Background:

Molecular replacement (MR) is a primary method for solving the crystallographic phase problem in X-ray crystallography.
MR's success is contingent on data resolution and the similarity between the search model and target structure.
Low resolution or dissimilar models present significant challenges for conventional MR approaches.

Purpose of the Study:

To describe tools within the Rosetta software suite for addressing difficult molecular replacement (MR) problems.
To provide strategies for model building and refinement in cases where standard MR methods are insufficient.
To leverage recent advancements in protein structure prediction for enhancing MR success rates.

Main Methods:

Utilizing protein structure prediction algorithms to generate accurate models for MR.
Employing Rosetta's computational tools for iterative model building and refinement.
Applying these methods to challenging MR cases with low-resolution data or divergent search models.

Main Results:

Demonstrated utility of protein structure prediction in overcoming MR limitations.
Successful application of Rosetta tools in solving difficult crystallographic structures.
Improved model building and refinement strategies for enhanced MR outcomes.

Conclusions:

Protein structure prediction is a powerful adjunct to molecular replacement for solving challenging crystallographic structures.
Rosetta provides a valuable toolkit for computational model building and refinement in difficult MR scenarios.
These advancements expand the scope and success rate of X-ray crystallography for structure determination.