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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 8: Computational Design of Ligand Binding Proteins
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Chapter title
Computational Design of Ligand Binding Proteins
Chapter number 8
Book title
Computational Design of Ligand Binding Proteins
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3569-7_8
Pubmed ID
Book ISBNs
978-1-4939-3567-3, 978-1-4939-3569-7
Authors

Wang, Meng, Zhao, Huimin, Meng Wang, Huimin Zhao

Editors

Barry L. Stoddard

Abstract

The advantages of computational design and directed evolution are complementary, and only through combined and iterative use of both approaches, a daunting task such as protein-ligand interaction design, can be achieved efficiently. Here, we describe a systematic strategy to combine structure-guided computational design, iterative site saturation mutagenesis, and yeast two-hybrid system (Y2H)-based phenotypic screening to engineer novel and orthogonal interactions between synthetic ligands and human estrogen receptor α (hERα) for the development of novel gene switches.

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Mendeley readers

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The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Student > Bachelor 1 17%
Researcher 1 17%
Other 1 17%
Unknown 1 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 50%
Neuroscience 1 17%
Engineering 1 17%
Unknown 1 17%
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 21 April 2016.
All research outputs
#20,322,106
of 22,865,319 outputs
Outputs from Methods in molecular biology
#9,916
of 13,127 outputs
Outputs of similar age
#330,679
of 393,645 outputs
Outputs of similar age from Methods in molecular biology
#1,053
of 1,470 outputs
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So far Altmetric has tracked 13,127 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1,470 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.