| Chapter title |
Numerical Models and In Vitro Assays to Study Odorant Receptors
|
|---|---|
| Chapter number | 7 |
| Book title |
Olfactory Receptors
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-8609-5_7 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-8608-8, 978-1-4939-8609-5
|
| Authors |
Caroline Bushdid, Claire A. de March, Hiroaki Matsunami, Jérôme Golebiowski, Bushdid, Caroline, de March, Claire A., Matsunami, Hiroaki, Golebiowski, Jérôme |
| Abstract |
Unraveling the sense of smell relies on understanding how odorant receptors recognize odorant molecules. Given the vastness of the odorant chemical space and the complexity of the odorant receptor space, computational methods are in line to propose rules connecting them. We hereby propose an in silico and an in vitro approach, which, when combined are extremely useful for assessing chemogenomic links. In this chapter we mostly focus on the mining of already existing data through machine learning methods. This approach allows establishing predictions that map the chemical space and the receptor space. Then, we describe the method for assessing the activation of odorant receptors and their mutants through luciferase reporter gene functional assays. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 2 | 20% |
| Researcher | 2 | 20% |
| Other | 1 | 10% |
| Student > Ph. D. Student | 1 | 10% |
| Student > Bachelor | 1 | 10% |
| Other | 2 | 20% |
| Unknown | 1 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 2 | 20% |
| Medicine and Dentistry | 2 | 20% |
| Physics and Astronomy | 1 | 10% |
| Computer Science | 1 | 10% |
| Neuroscience | 1 | 10% |
| Other | 1 | 10% |
| Unknown | 2 | 20% |