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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

  1. Altmetric Badge
    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.
  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.
  7. Altmetric Badge
    Chapter 6 Proteus and the Design of Ligand Binding Sites.
  8. Altmetric Badge
    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
  11. Altmetric Badge
    Chapter 10 Computational Design of Multinuclear Metalloproteins Using Unnatural Amino Acids.
  12. Altmetric Badge
    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 4: Computational Design of Ligand Binding Proteins
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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1 patent

Citations

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Chapter title
Computational Design of Ligand Binding Proteins
Chapter number 4
Book title
Computational Design of Ligand Binding Proteins
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3569-7_4
Pubmed ID
27094285
Book ISBNs
978-1-4939-3567-3, 978-1-4939-3569-7
Authors

Moretti, Rocco, Bender, Brian J, Allison, Brittany, Meiler, Jens, Rocco Moretti, Brian J. Bender, Brittany Allison, Jens Meiler, Bender, Brian J.

Editors

Barry L. Stoddard

Abstract

Proteins that bind small molecules (ligands) can be used as biosensors, signal modulators, and sequestering agents. When naturally occurring proteins for a particular target ligand are not available, artificial proteins can be computationally designed. We present a protocol based on RosettaLigand to redesign an existing protein pocket to bind a target ligand. Starting with a protein structure and the structure of the ligand, Rosetta can optimize both the placement of the ligand in the pocket and the identity and conformation of the surrounding sidechains, yielding proteins that bind the target compound.

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X Demographics

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 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 74 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 23%
Researcher 11 15%
Student > Bachelor 7 9%
Student > Doctoral Student 5 7%
Student > Master 3 4%
Other 5 7%
Unknown 26 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 22%
Chemistry 10 14%
Agricultural and Biological Sciences 5 7%
Engineering 4 5%
Computer Science 2 3%
Other 7 9%
Unknown 30 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 15 June 2022.
All research outputs
#6,912,452
of 22,668,244 outputs
Outputs from Methods in molecular biology
#2,072
of 13,037 outputs
Outputs of similar age
#112,008
of 393,064 outputs
Outputs of similar age from Methods in molecular biology
#248
of 1,468 outputs
Altmetric has tracked 22,668,244 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 13,037 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 83% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 393,064 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 1,468 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.

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