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Computational Design of Ligand Binding Proteins

Overview of attention for book
Cover of 'Computational Design of Ligand Binding Proteins'

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.
  22. Altmetric Badge
    Chapter 21 Modeling of Protein-RNA Complex Structures Using Computational Docking Methods.
Attention for Chapter 14: Computational Design of Ligand Binding Proteins
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Chapter title
Computational Design of Ligand Binding Proteins
Chapter number 14
Book title
Computational Design of Ligand Binding Proteins
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3569-7_14
Pubmed ID
27094295
Book ISBNs
978-1-4939-3567-3, 978-1-4939-3569-7
Authors

Reich, Lothar Luther, Dutta, Sanjib, Keating, Amy E, Lothar “Luther” Reich, Sanjib Dutta, Amy E. Keating

Editors

Barry L. Stoddard

Abstract

Library methods are widely used to study protein-protein interactions, and high-throughput screening or selection followed by sequencing can identify a large number of peptide ligands for a protein target. In this chapter, we describe a procedure called "SORTCERY" that can rank the affinities of library members for a target with high accuracy. SORTCERY follows a three-step protocol. First, fluorescence-activated cell sorting (FACS) is used to sort a library of yeast-displayed peptide ligands according to their affinities for a target. Second, all sorted pools are deep sequenced. Third, the resulting data are analyzed to create a ranking. We demonstrate an application of SORTCERY to the problem of ranking peptide ligands for the anti-apoptotic regulator Bcl-xL.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 31 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 47%
Researcher 6 19%
Other 2 6%
Student > Master 2 6%
Professor 1 3%
Other 2 6%
Unknown 4 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 41%
Agricultural and Biological Sciences 5 16%
Chemistry 4 13%
Computer Science 1 3%
Chemical Engineering 1 3%
Other 2 6%
Unknown 6 19%
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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