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Computational Methods for GPCR Drug Discovery

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Cover of 'Computational Methods for GPCR Drug Discovery'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Current and Future Challenges in GPCR Drug Discovery
  3. Altmetric Badge
    Chapter 2 Characterization of Ligand Binding to GPCRs Through Computational Methods
  4. Altmetric Badge
    Chapter 3 Breakthrough in GPCR Crystallography and Its Impact on Computer-Aided Drug Design
  5. Altmetric Badge
    Chapter 4 A Structural Framework for GPCR Chemogenomics: What’s In a Residue Number?
  6. Altmetric Badge
    Chapter 5 GPCR Homology Model Generation for Lead Optimization
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    Chapter 6 GPCRs: What Can We Learn from Molecular Dynamics Simulations?
  8. Altmetric Badge
    Chapter 7 Methods of Exploring Protein–Ligand Interactions to Guide Medicinal Chemistry Efforts
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    Chapter 8 Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method
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    Chapter 9 Molecular Basis of Ligand Dissociation from G Protein-Coupled Receptors and Predicting Residence Time
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    Chapter 10 Methodologies for the Examination of Water in GPCRs
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    Chapter 11 Methods for Virtual Screening of GPCR Targets: Approaches and Challenges
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    Chapter 12 Approaches for Differentiation and Interconverting GPCR Agonists and Antagonists
  14. Altmetric Badge
    Chapter 13 Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs
  15. Altmetric Badge
    Chapter 14 Challenges and Opportunities in Drug Discovery of Biased Ligands
  16. Altmetric Badge
    Chapter 15 Synergistic Use of GPCR Modeling and SDM Experiments to Understand Ligand Binding
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    Chapter 16 Computational Support of Medicinal Chemistry in Industrial Settings
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    Chapter 17 Investigating Small-Molecule Ligand Binding to G Protein-Coupled Receptors with Biased or Unbiased Molecular Dynamics Simulations
  19. Altmetric Badge
    Chapter 18 Ligand-Based Methods in GPCR Computer-Aided Drug Design
  20. Altmetric Badge
    Chapter 19 Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery
  21. Altmetric Badge
    Chapter 20 Cheminformatics in the Service of GPCR Drug Discovery
  22. Altmetric Badge
    Chapter 21 Modeling and Deorphanization of Orphan GPCRs
Attention for Chapter 15: Synergistic Use of GPCR Modeling and SDM Experiments to Understand Ligand Binding
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Chapter title
Synergistic Use of GPCR Modeling and SDM Experiments to Understand Ligand Binding
Chapter number 15
Book title
Computational Methods for GPCR Drug Discovery
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7465-8_15
Pubmed ID
Book ISBNs
978-1-4939-7464-1, 978-1-4939-7465-8
Authors

Andrew Potterton, Alexander Heifetz, Andrea Townsend-Nicholson, Potterton, A, Heifetz, A, Townsend-Nicholson, A

Abstract

There is a substantial amount of historical ligand binding data available from site-directed mutagenesis (SDM) studies of many different GPCR subtypes. This information was generated prior to the wave of GPCR crystal structure, in an effort to understand ligand binding with a view to drug discovery. Concerted efforts to determine the atomic structure of GPCRs have proven extremely successful and there are now more than 80 GPCR crystal structure in the PDB database, many of which have been obtained in the presence of receptor ligands and associated G proteins. These structural data enable the generation of computational model structures for all GPCRs, including those for which crystal structures do not yet exist. The power of these models in designing novel ligands, especially those with improved residence times, and for better understanding receptor function can be enhanced tremendously by combining them synergistically with historic SDM ligand binding data. Here, we describe a protocol by which historic SDM binding data and receptor models may be used together to identify novel key residues for mutagenesis studies.

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

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 23%
Student > Bachelor 2 15%
Student > Ph. D. Student 2 15%
Professor 1 8%
Other 1 8%
Other 2 15%
Unknown 2 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 23%
Chemistry 3 23%
Agricultural and Biological Sciences 3 23%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Unknown 3 23%
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 04 December 2018.
All research outputs
#20,453,782
of 23,009,818 outputs
Outputs from Methods in molecular biology
#9,941
of 13,157 outputs
Outputs of similar age
#378,156
of 442,310 outputs
Outputs of similar age from Methods in molecular biology
#1,193
of 1,498 outputs
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So far Altmetric has tracked 13,157 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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