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Protein Crystallography

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Cover of 'Protein Crystallography'

Table of Contents

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    Book Overview
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    Chapter 1 Expression and Purification of Recombinant Proteins in Escherichia coli with a His6 or Dual His6-MBP Tag
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    Chapter 2 Protein Crystallization
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    Chapter 3 Advanced Methods of Protein Crystallization
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    Chapter 4 The “Sticky Patch” Model of Crystallization and Modification of Proteins for Enhanced Crystallizability
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    Chapter 5 Crystallization of Membrane Proteins: An Overview
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    Chapter 6 Locating and Visualizing Crystals for X-Ray Diffraction Experiments
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    Chapter 7 Collection of X-Ray Diffraction Data from Macromolecular Crystals
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    Chapter 8 Identifying and Overcoming Crystal Pathologies: Disorder and Twinning
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    Chapter 9 Applications of X-Ray Micro-Beam for Data Collection
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    Chapter 10 Serial Synchrotron X-Ray Crystallography (SSX)
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    Chapter 11 Time-Resolved Macromolecular Crystallography at Modern X-Ray Sources
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    Chapter 12 Structure Determination Using X-Ray Free-Electron Laser Pulses
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    Chapter 13 Processing of XFEL Data
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    Chapter 14 Many Ways to Derivatize Macromolecules and Their Crystals for Phasing
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    Chapter 15 Experimental Phasing: Substructure Solution and Density Modification as Implemented in SHELX
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    Chapter 16 Contemporary Use of Anomalous Diffraction in Biomolecular Structure Analysis
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    Chapter 17 Long-Wavelength X-Ray Diffraction and Its Applications in Macromolecular Crystallography
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    Chapter 18 Acknowledging Errors: Advanced Molecular Replacement with Phaser
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    Chapter 19 Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems
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    Chapter 20 Radiation Damage in Macromolecular Crystallography
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    Chapter 21 Boxes of Model Building and Visualization
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    Chapter 22 Structure Refinement at Atomic Resolution
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    Chapter 23 Low Resolution Refinement of Atomic Models Against Crystallographic Data
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    Chapter 24 Stereochemistry and Validation of Macromolecular Structures
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    Chapter 25 Validation of Protein–Ligand Crystal Structure Models: Small Molecule and Peptide Ligands
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    Chapter 26 Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive
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    Chapter 27 Databases, Repositories, and Other Data Resources in Structural Biology
Attention for Chapter 19: Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems
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Chapter title
Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems
Chapter number 19
Book title
Protein Crystallography
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-7000-1_19
Pubmed ID
Book ISBNs
978-1-4939-6998-2, 978-1-4939-7000-1
Authors

Frank DiMaio

Editors

Alexander Wlodawer, Zbigniew Dauter, Mariusz Jaskolski

Abstract

Molecular replacement (MR), a method for solving the crystallographic phase problem using phases derived from a model of the target structure, has proven extremely valuable, accounting for the vast majority of structures solved by X-ray crystallography. However, when the resolution of data is low, or the starting model is very dissimilar to the target protein, solving structures via molecular replacement may be very challenging. In recent years, protein structure prediction methodology has emerged as a powerful tool in model building and model refinement for difficult molecular replacement problems. This chapter describes some of the tools available in Rosetta for model building and model refinement specifically geared toward difficult molecular replacement cases.

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 29%
Student > Bachelor 1 14%
Student > Doctoral Student 1 14%
Other 1 14%
Researcher 1 14%
Other 0 0%
Unknown 1 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 29%
Computer Science 1 14%
Chemistry 1 14%
Medicine and Dentistry 1 14%
Unknown 2 29%