| Chapter title |
Challenges and Opportunities in Drug Discovery of Biased Ligands
|
|---|---|
| Chapter number | 14 |
| Book title |
Computational Methods for GPCR Drug Discovery
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7465-8_14 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7464-1, 978-1-4939-7465-8
|
| Authors |
Ismael Rodríguez-Espigares, Agnieszka A. Kaczor, Tomasz Maciej Stepniewski, Jana Selent |
| Abstract |
The observation of biased agonism in G protein-coupled receptors (GPCRs) has provided new approaches for the development of more efficacious and safer drugs. However, in order to rationally design biased drugs, one must understand the molecular basis of this phenomenon. Computational approaches can help in exploring the conformational universe of GPCRs and detecting conformational states with relevance for distinct functional outcomes. This information is extremely valuable for the development of new therapeutic agents that promote desired conformational receptor states and responses while avoiding the ones leading to undesired side-effects.This book chapter intends to introduce the reader to powerful computational approaches for sampling the conformational space of these receptors, focusing first on molecular dynamics and the analysis of the produced data through methods such as dimensionality reduction, Markov State Models and adaptive sampling. Then, we show how to seek for compounds that target distinct conformational states via docking and virtual screening. In addition, we describe how to detect receptor-ligand interactions that drive signaling bias and comment current challenges and opportunities of presented methods. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 50% |
| Turkey | 1 | 17% |
| Japan | 1 | 17% |
| Unknown | 1 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 83% |
| Scientists | 1 | 17% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 27 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 22% |
| Researcher | 5 | 19% |
| Student > Doctoral Student | 2 | 7% |
| Student > Master | 2 | 7% |
| Lecturer | 1 | 4% |
| Other | 4 | 15% |
| Unknown | 7 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 22% |
| Chemistry | 4 | 15% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 15% |
| Agricultural and Biological Sciences | 3 | 11% |
| Decision Sciences | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 8 | 30% |