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Computational Design of Ligand Binding Proteins

Overview of attention for book
Computational Design of Ligand Binding Proteins
Springer New York

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 In silico Identification and Characterization of Protein-Ligand Binding Sites
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    Chapter 2 Computational Modeling of Small Molecule Ligand Binding Interactions and Affinities.
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    Chapter 3 Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers.
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    Chapter 4 Computational Design of Ligand Binding Proteins
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    Chapter 5 PocketOptimizer and the Design of Ligand Binding Sites.
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    Chapter 6 Proteus and the Design of Ligand Binding Sites.
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    Chapter 7 A Structure-Based Design Protocol for Optimizing Combinatorial Protein Libraries.
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    Chapter 8 Computational Design of Ligand Binding Proteins
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    Chapter 9 Computational Design of Ligand Binding Proteins
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    Chapter 10 Computational Design of Multinuclear Metalloproteins Using Unnatural Amino Acids.
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    Chapter 11 De Novo Design of Metalloproteins and Metalloenzymes in a Three-Helix Bundle.
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    Chapter 12 Design of Light-Controlled Protein Conformations and Functions.
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    Chapter 13 Computational Introduction of Catalytic Activity into Proteins.
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    Chapter 14 Computational Design of Ligand Binding Proteins
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    Chapter 15 Design of Specific Peptide-Protein Recognition.
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    Chapter 16 Computational Design of DNA-Binding Proteins.
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    Chapter 17 Motif-Driven Design of Protein-Protein Interfaces.
  19. Altmetric Badge
    Chapter 18 Computational Design of Ligand Binding Proteins
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    Chapter 19 Computational Design of Ligand Binding Proteins
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    Chapter 20 Computational Design of Protein Linkers.
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    Chapter 21 Modeling of Protein-RNA Complex Structures Using Computational Docking Methods.
Attention for Chapter 7: A Structure-Based Design Protocol for Optimizing Combinatorial Protein Libraries.
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Chapter title
A Structure-Based Design Protocol for Optimizing Combinatorial Protein Libraries.
Chapter number 7
Book title
Computational Design of Ligand Binding Proteins
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3569-7_7
Pubmed ID
Book ISBNs
978-1-4939-3567-3, 978-1-4939-3569-7
Authors

Mark W. Lunt, Christopher D. Snow

Editors

Barry L. Stoddard

Abstract

Protein variant libraries created via site-directed mutagenesis are a powerful approach to engineer improved proteins for numerous applications such as altering enzyme substrate specificity. Conventional libraries commonly use a brute force approach: saturation mutagenesis via degenerate codons that encode all 20 natural amino acids. In contrast, this chapter describes a protocol for designing "smarter" degenerate codon libraries via direct combinatorial optimization in "library space."Several case studies illustrate how it is possible to design degenerate codon libraries that are highly enriched for favorable, low-energy sequences as assessed using a standard all-atom scoring function. There is much to gain for experimental protein engineering laboratories willing to think beyond site saturation mutagenesis. In the common case that the exact experimental screening budget is not fixed, it is particularly helpful to perform a Pareto analysis to inspect favorable libraries at a range of possible library sizes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 22%
Researcher 2 22%
Student > Bachelor 1 11%
Student > Ph. D. Student 1 11%
Professor 1 11%
Other 0 0%
Unknown 2 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 22%
Agricultural and Biological Sciences 2 22%
Linguistics 1 11%
Neuroscience 1 11%
Engineering 1 11%
Other 0 0%
Unknown 2 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 December 2017.
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#18,453,763
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Outputs from Methods in molecular biology
#7,923
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Outputs of similar age
#284,496
of 393,648 outputs
Outputs of similar age from Methods in molecular biology
#846
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