Numerical Models and In Vitro Assays to Study Odorant Receptors.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Bushdid, C; de March, CA; Matsunami, H; Golebiowski, J

Published Date

  • 2018

Published In

Volume / Issue

  • 1820 /

Start / End Page

  • 77 - 93

PubMed ID

  • 29884939

Pubmed Central ID

  • 29884939

Electronic International Standard Serial Number (EISSN)

  • 1940-6029

Digital Object Identifier (DOI)

  • 10.1007/978-1-4939-8609-5_7

Language

  • eng

Conference Location

  • United States