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.
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Acknowledgment
This work is supported by grants from the National Institute on Deafness and Other Communication Disorders, National Institute of Health Grants DC014423 and DC016224, National Science Foundation (NSF) Grants 1515801, and 1515930 (to H.M.) and from Agence Nationale de la Recherche (Neurolf project to J.G.) as part of NSF/NIH/ANR Collaborative Research in Computational Neuroscience. CB thanks GIRACT, the GEN and the Roudnitska foundations for supporting her research.
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Bushdid, C., de March, C.A., Matsunami, H., Golebiowski, J. (2018). Numerical Models and In Vitro Assays to Study Odorant Receptors. In: Simoes de Souza, F., Antunes, G. (eds) Olfactory Receptors. Methods in Molecular Biology, vol 1820. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8609-5_7
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DOI: https://doi.org/10.1007/978-1-4939-8609-5_7
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