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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

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    Book Overview
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    Chapter 1 Current and Future Challenges in GPCR Drug Discovery
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    Chapter 2 Characterization of Ligand Binding to GPCRs Through Computational Methods
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    Chapter 3 Breakthrough in GPCR Crystallography and Its Impact on Computer-Aided Drug Design
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    Chapter 4 A Structural Framework for GPCR Chemogenomics: What’s In a Residue Number?
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    Chapter 5 GPCR Homology Model Generation for Lead Optimization
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    Chapter 6 GPCRs: What Can We Learn from Molecular Dynamics Simulations?
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    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
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    Chapter 13 Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs
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    Chapter 14 Challenges and Opportunities in Drug Discovery of Biased Ligands
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    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
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    Chapter 18 Ligand-Based Methods in GPCR Computer-Aided Drug Design
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    Chapter 19 Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery
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    Chapter 20 Cheminformatics in the Service of GPCR Drug Discovery
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    Chapter 21 Modeling and Deorphanization of Orphan GPCRs
Attention for Chapter 6: GPCRs: What Can We Learn from Molecular Dynamics Simulations?
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Chapter title
GPCRs: What Can We Learn from Molecular Dynamics Simulations?
Chapter number 6
Book title
Computational Methods for GPCR Drug Discovery
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7465-8_6
Pubmed ID
Book ISBNs
978-1-4939-7464-1, 978-1-4939-7465-8
Authors

Naushad Velgy, George Hedger, Philip C. Biggin

Abstract

Advances in the structural biology of G-protein Coupled Receptors have resulted in a significant step forward in our understanding of how this important class of drug targets function at the molecular level. However, it has also become apparent that they are very dynamic molecules, and moreover, that the underlying dynamics is crucial in shaping the response to different ligands. Molecular dynamics simulations can provide unique insight into the dynamic properties of GPCRs in a way that is complementary to many experimental approaches. In this chapter, we describe progress in three distinct areas that are particularly difficult to study with other techniques: atomic level investigation of the conformational changes that occur when moving between the various states that GPCRs can exist in, the pathways that ligands adopt during binding/unbinding events and finally, the influence of lipids on the conformational dynamics of GPCRs.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 17%
Researcher 9 17%
Student > Bachelor 8 15%
Professor 4 8%
Student > Doctoral Student 3 6%
Other 9 17%
Unknown 10 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 25%
Chemistry 10 19%
Agricultural and Biological Sciences 8 15%
Pharmacology, Toxicology and Pharmaceutical Science 3 6%
Medicine and Dentistry 2 4%
Other 2 4%
Unknown 14 27%