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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 8: Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method
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Chapter title
Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method
Chapter number 8
Book title
Computational Methods for GPCR Drug Discovery
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7465-8_8
Pubmed ID
Book ISBNs
978-1-4939-7464-1, 978-1-4939-7465-8
Authors

Ewa I. Chudyk, Laurie Sarrat, Matteo Aldeghi, Dmitri G. Fedorov, Mike J. Bodkin, Tim James, Michelle Southey, Roger Robinson, Inaki Morao, Alexander Heifetz

Abstract

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity. It is essential for an efficient structure-based drug design (SBDD) process. FMO enables ab initio approaches to be applied to systems that conventional quantum-mechanical (QM) methods would find challenging. The key advantage of the Fragment Molecular Orbital Method (FMO) is that it can reveal atomistic details about the individual contributions and chemical nature of each residue and water molecule toward ligand binding which would otherwise be difficult to detect without using QM methods. In this chapter, we demonstrate the typical use of FMO to analyze 19 crystal structures of β1 and β2 adrenergic receptors with their corresponding agonists and antagonists.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 44%
Student > Doctoral Student 2 11%
Student > Master 2 11%
Student > Ph. D. Student 1 6%
Other 1 6%
Other 2 11%
Unknown 2 11%
Readers by discipline Count As %
Chemistry 6 33%
Biochemistry, Genetics and Molecular Biology 3 17%
Agricultural and Biological Sciences 2 11%
Chemical Engineering 1 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Other 2 11%
Unknown 3 17%