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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

  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.
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    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.
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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.
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

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9 X users
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59 Mendeley
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Title
Computational Design of Ligand Binding Proteins
Published by
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3569-7
Pubmed ID
ISBNs
978-1-4939-3567-3, 978-1-4939-3569-7
Authors

Roche, Daniel Barry, McGuffin, Liam James

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

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 12%
Student > Ph. D. Student 7 12%
Student > Bachelor 4 7%
Student > Doctoral Student 4 7%
Student > Master 4 7%
Other 7 12%
Unknown 26 44%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 24%
Pharmacology, Toxicology and Pharmaceutical Science 5 8%
Engineering 3 5%
Agricultural and Biological Sciences 2 3%
Chemistry 2 3%
Other 6 10%
Unknown 27 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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
#3,544,662
of 22,668,244 outputs
Outputs from Methods in molecular biology
#856
of 13,037 outputs
Outputs of similar age
#61,938
of 393,064 outputs
Outputs of similar age from Methods in molecular biology
#140
of 1,468 outputs
Altmetric has tracked 22,668,244 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
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 particularly well, scoring higher than 93% 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 done well, scoring higher than 84% 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 particularly well, scoring higher than 90% of its contemporaries.