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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
  3. Altmetric Badge
    Chapter 2 Computational Modeling of Small Molecule Ligand Binding Interactions and Affinities.
  4. Altmetric Badge
    Chapter 3 Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers.
  5. Altmetric Badge
    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.
  9. Altmetric Badge
    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.
  15. Altmetric Badge
    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
  20. Altmetric Badge
    Chapter 19 Computational Design of Ligand Binding Proteins
  21. Altmetric Badge
    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 1: In silico Identification and Characterization of Protein-Ligand Binding Sites
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Chapter title
In silico Identification and Characterization of Protein-Ligand Binding Sites
Chapter number 1
Book title
Computational Design of Ligand Binding Proteins
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3569-7_1
Pubmed ID
Book ISBNs
978-1-4939-3567-3, 978-1-4939-3569-7
Authors

Roche, Daniel Barry, McGuffin, Liam James, Daniel Barry Roche, Liam James McGuffin

Editors

Barry L. Stoddard

Abstract

Protein-ligand binding site prediction methods aim to predict, from amino acid sequence, protein-ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein-ligand interactions has become extremely important to help determine a protein's functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein-ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein-ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein-ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.

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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 %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 22%
Student > Ph. D. Student 6 19%
Student > Doctoral Student 4 13%
Student > Master 3 9%
Student > Bachelor 2 6%
Other 6 19%
Unknown 4 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 44%
Pharmacology, Toxicology and Pharmaceutical Science 3 9%
Engineering 3 9%
Agricultural and Biological Sciences 2 6%
Nursing and Health Professions 1 3%
Other 5 16%
Unknown 4 13%
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 29 April 2016.
All research outputs
#18,453,763
of 22,865,319 outputs
Outputs from Methods in molecular biology
#7,923
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#284,496
of 393,648 outputs
Outputs of similar age from Methods in molecular biology
#846
of 1,470 outputs
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