Sequence-Based Prediction of Olfactory Receptor Responses

Abstract

Computational prediction of how strongly an olfactory receptor (OR) responds to various odors can help in bridging the widening gap between the large number of receptors that have been sequenced and the small number of experiments measuring their responses. Previous efforts in this area have predicted the responses of a receptor to some odors, using the known responses of the same receptor to other odors. Here, we present a method to predict the responses of a receptor without any known responses by using available data about the responses of other conspecific receptors and their sequences. We applied this method to ORs in insects Drosophila melanogaster (both adult and larva) and Anopheles gambiae and to mouse and human ORs. We found the predictions to be in significant agreement with the experimental measurements. The method also provides clues about the response-determining positions within the receptor sequences.

Functional olfactory evolution in Drosophila suzukii and the subgenus Sophophora

“…Using an empirical approach described in ( Chepurwar et al., 2019 ), we identified the top 20 amino acid positions that predict the odorant responses of ORs. These amino acid positions were determined using the response data from the OR22a receptors across 14 Drosophila species for which genomic sequence data were available ( Figures 5 and S5 A).…”

Large-Scale G Protein-Coupled Olfactory Receptor–Ligand Pairing

“…Attempts to predict OR responses to odorants have also achieved encouraging results. 17 − 20 However, data scarcity in the immense odor space is a major bottleneck for good predictivities. To date, less than 50% of human ORs (hORs) and 20% of mouse ORs (mORs) have been deorphanized with less than 250 odorants ( Table S1 ).…”

“…A recent study on insect and mammalian ORs demonstrated that selecting subsets of 20 residues could indeed increase the model predictivity. 20 However, if one assumes that a given function is mostly encoded by 20 residues out of a GPCR sequence of ∼300 residues, the binomial coefficient [300!/20! (300 – 20)!]…”

Mosquito Olfactory Response Ensemble enables pattern discovery by curating a behavioral and electrophysiological response database

“…Having all this data in one place, in a structured format, can enable systematic large-scale analyses to discover trends that cannot be seen with individual studies in a variety of animal models ( Crasto et al., 2002 ; Liu et al., 2004 , 2011 ; Marenco et al., 2016 ; Olender et al., 2013 ). The Database of Odorant Responses (DoOR) catalogs the OR responses of different odors in Drosophila melanogaster ( Galizia et al., 2010 ; Münch and Galizia, 2016 ) and has proved to be very useful in enabling large-scale computational analyses ( Chepurwar et al., 2019 ; Dasgupta et al., 2018 ; Saberi and Seyed-Allaei, 2016 ; Zwicker et al., 2016 ). However, no such curated dataset is available for mosquitoes.…”