| Chapter title |
A Structural Framework for GPCR Chemogenomics: What’s In a Residue Number?
|
|---|---|
| Chapter number | 4 |
| Book title |
Computational Methods for GPCR Drug Discovery
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7465-8_4 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7464-1, 978-1-4939-7465-8
|
| Authors |
Márton Vass, Albert J. Kooistra, Stefan Verhoeven, David Gloriam, Iwan J. P. de Esch, Chris de Graaf |
| Abstract |
The recent surge of crystal structures of G protein-coupled receptors (GPCRs), as well as comprehensive collections of sequence, structural, ligand bioactivity, and mutation data, has enabled the development of integrated chemogenomics workflows for this important target family. This chapter will focus on cross-family and cross-class studies of GPCRs that have pinpointed the need for, and the implementation of, a generic numbering scheme for referring to specific structural elements of GPCRs. Sequence- and structure-based numbering schemes for different receptor classes will be introduced and the remaining caveats will be discussed. The use of these numbering schemes has facilitated many chemogenomics studies such as consensus binding site definition, binding site comparison, ligand repurposing (e.g. for orphan receptors), sequence-based pharmacophore generation for homology modeling or virtual screening, and class-wide chemogenomics studies of GPCRs. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 25% |
| United States | 3 | 19% |
| India | 1 | 6% |
| Japan | 1 | 6% |
| Czechia | 1 | 6% |
| France | 1 | 6% |
| Australia | 1 | 6% |
| Unknown | 4 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 56% |
| Scientists | 7 | 44% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 24 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 29% |
| Student > Master | 5 | 21% |
| Researcher | 4 | 17% |
| Professor > Associate Professor | 2 | 8% |
| Student > Doctoral Student | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 2 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 7 | 29% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 17% |
| Agricultural and Biological Sciences | 4 | 17% |
| Veterinary Science and Veterinary Medicine | 2 | 8% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 3 | 13% |