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Computational Protein Design

Overview of attention for book
Cover of 'Computational Protein Design'

Table of Contents

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    Book Overview
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    Chapter 1 The Framework of Computational Protein Design.
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    Chapter 2 Achievements and Challenges in Computational Protein Design.
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    Chapter 3 Production of Computationally Designed Small Soluble- and Membrane-Proteins: Cloning, Expression, and Purification.
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    Chapter 4 Deterministic Search Methods for Computational Protein Design.
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    Chapter 5 Geometric Potentials for Computational Protein Sequence Design.
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    Chapter 6 Modeling Binding Affinity of Pathological Mutations for Computational Protein Design.
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    Chapter 7 Multistate Computational Protein Design with Backbone Ensembles.
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    Chapter 8 Integration of Molecular Dynamics Based Predictions into the Optimization of De Novo Protein Designs: Limitations and Benefits.
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    Chapter 9 Applications of Normal Mode Analysis Methods in Computational Protein Design.
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    Chapter 10 Computational Protein Design Under a Given Backbone Structure with the ABACUS Statistical Energy Function.
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    Chapter 11 Computational Protein Design Through Grafting and Stabilization.
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    Chapter 12 An Evolution-Based Approach to De Novo Protein Design.
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    Chapter 13 Parallel Computational Protein Design.
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    Chapter 14 Computational Protein Design
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    Chapter 15 OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design.
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    Chapter 16 Evolution-Inspired Computational Design of Symmetric Proteins.
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    Chapter 17 A Protocol for the Design of Protein and Peptide Nanostructure Self-Assemblies Exploiting Synthetic Amino Acids.
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    Chapter 18 Probing Oligomerized Conformations of Defensin in the Membrane.
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    Chapter 19 Computational Design of Ligand Binding Proteins.
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    Chapter 20 EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function.
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    Chapter 21 Computational Tools for Aiding Rational Antibody Design.
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    Chapter 22 Computational Design of Membrane Curvature-Sensing Peptides.
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    Chapter 23 Computational Tools for Allosteric Drug Discovery: Site Identification and Focus Library Design.
Attention for Chapter 16: Evolution-Inspired Computational Design of Symmetric Proteins.
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Chapter title
Evolution-Inspired Computational Design of Symmetric Proteins.
Chapter number 16
Book title
Computational Protein Design
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6637-0_16
Pubmed ID
27914059
Book ISBNs
978-1-4939-6635-6, 978-1-4939-6637-0
Authors

Arnout R. D. Voet, David Simoncini, Jeremy R. H. Tame, Kam Y. J. Zhang

Editors

Ilan Samish

Abstract

Monomeric proteins with a number of identical repeats creating symmetrical structures are potentially very valuable building blocks with a variety of bionanotechnological applications. As such proteins do not occur naturally, the emerging field of computational protein design serves as an excellent tool to create them from nonsymmetrical templates. Existing pseudo-symmetrical proteins are believed to have evolved from oligomeric precursors by duplication and fusion of identical repeats. Here we describe a computational workflow to reverse-engineer this evolutionary process in order to create stable proteins consisting of identical sequence repeats.

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Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 16%
Student > Ph. D. Student 3 16%
Professor 2 11%
Student > Bachelor 2 11%
Student > Master 2 11%
Other 2 11%
Unknown 5 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 37%
Agricultural and Biological Sciences 2 11%
Computer Science 1 5%
Physics and Astronomy 1 5%
Social Sciences 1 5%
Other 0 0%
Unknown 7 37%

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