Chapter title |
From Three-Dimensional GPCR Structure to Rational Ligand Discovery.
|
---|---|
Chapter number | 7 |
Book title |
G Protein-Coupled Receptors - Modeling and Simulation
|
Published in |
Advances in experimental medicine and biology, January 2014
|
DOI | 10.1007/978-94-007-7423-0_7 |
Pubmed ID | |
Book ISBNs |
978-9-40-077422-3, 978-9-40-077423-0
|
Authors |
Albert J Kooistra, Rob Leurs, Iwan J P de Esch, Chris de Graaf, Kooistra AJ, Leurs R, de Esch IJ, de Graaf C, Albert J. Kooistra, Iwan J. P. de Esch |
Abstract |
This chapter will focus on G protein-coupled receptor structure-based virtual screening and ligand design. A generic virtual screening workflow and its individual elements will be introduced, covering amongst others the use of experimental data to steer the virtual screening process, ligand binding mode prediction, virtual screening for novel ligands, and rational structure-based virtual screening hit optimization. An overview of recent successful structure-based ligand discovery and design studies shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands for GPCRs. Moreover, the recently solved GPCR crystal structures have further increased the opportunities in structure-based ligand discovery for this pharmaceutically important protein family. The current chapter will discuss several challenges in rational ligand discovery based on GPCR structures including: (i) structure-based identification of ligands with specific effects on GPCR mediated signaling pathways, and (ii) virtual screening and structure-based optimization of fragment-like molecules. |
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United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
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Unknown | 33 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 5 | 15% |
Student > Master | 4 | 12% |
Professor | 3 | 9% |
Lecturer | 2 | 6% |
Professor > Associate Professor | 2 | 6% |
Other | 3 | 9% |
Unknown | 14 | 42% |
Readers by discipline | Count | As % |
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Chemistry | 7 | 21% |
Agricultural and Biological Sciences | 5 | 15% |
Biochemistry, Genetics and Molecular Biology | 3 | 9% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 9% |
Computer Science | 1 | 3% |
Other | 0 | 0% |
Unknown | 14 | 42% |