| Chapter title |
GPCR Homology Model Generation for Lead Optimization
|
|---|---|
| Chapter number | 5 |
| Book title |
Computational Methods for GPCR Drug Discovery
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7465-8_5 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7464-1, 978-1-4939-7465-8
|
| Authors |
Christofer S. Tautermann |
| Abstract |
The vast increase of recently solved GPCR X-ray structures forms the basis for GPCR homology modeling to atomistic accuracy. Nowadays, homology models can be employed for GPCR-ligand optimization and have been reported as invaluable tools for drug design in the last few years. Elucidation of the complex GPCR pharmacology and the associated GPCR conformations made clear that different homology models have to be constructed for different activation states of the GPCRs. Therefore, templates have to be chosen accordingly to their sequence homology as well as to their activation state. The subsequent ligand placement is nontrivial, as some recent X-ray structures show very unusual ligand binding sites and solvent involvement, expanding the space of the putative ligand binding site from the generic retinal binding pocket to the whole receptor. In the present study, a workflow is presented starting from the selection of the target sequence, guiding through the GPCR modeling process, and finishing with ligand placement and pose validation. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 25% |
| Germany | 1 | 13% |
| Australia | 1 | 13% |
| France | 1 | 13% |
| Mexico | 1 | 13% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 50% |
| Members of the public | 3 | 38% |
| Practitioners (doctors, other healthcare professionals) | 1 | 13% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 30% |
| Student > Bachelor | 4 | 20% |
| Student > Ph. D. Student | 3 | 15% |
| Lecturer | 1 | 5% |
| Student > Doctoral Student | 1 | 5% |
| Other | 3 | 15% |
| Unknown | 2 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 15% |
| Chemistry | 3 | 15% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 10% |
| Biochemistry, Genetics and Molecular Biology | 2 | 10% |
| Computer Science | 1 | 5% |
| Other | 3 | 15% |
| Unknown | 6 | 30% |