Agonists of G-Protein-Coupled Odorant Receptors Are Predicted from Chemical Features

Abstract

Predicting the activity of chemicals for a given odorant receptor is a longstanding challenge. Here the activity of 258 chemicals on the human G-protein-coupled odorant receptor (OR)51E1, also known as prostate-specific G-protein-coupled receptor 2 (PSGR2), was virtually screened by machine learning using 4884 chemical descriptors as input. A systematic control by functional in vitro assays revealed that a support vector machine algorithm accurately predicted the activity of a screened library. It allowed us to identify two novel agonists in vitro for OR51E1. The transferability of the protocol was assessed on OR1A1, OR2W1, and MOR256-3 odorant receptors, and, in each case, novel agonists were identified with a hit rate of 39-50%. We further show how ligands' efficacy is encoded into residues within OR51E1 cavity using a molecular modeling protocol. Our approach allows widening the chemical spaces associated with odorant receptors. This machine-learning protocol based on chemical features thus represents an efficient tool for screening ligands for G-protein-coupled odorant receptors that modulate non-olfactory functions or, upon combinatorial activation, give rise to our sense of smell.

Computational exploration of molecular receptive fields in the olfactory bulb reveals a glomerulus-centric chemical map

“…These observations indicate that the ground truth for ligand activity needs to be sampled from a variety of assays in order to obtain a coherent picture, which is important e.g. for in-silico structural modeling of olfactory receptor binding mechanisms, that have been carried out for a related receptor 52 .…”

Citronellal perception and transmission by Anopheles gambiae s.s. (Diptera: Culicidae) females

“…Therefore, the homologous proteins, 3B6X and OR83b, found in A. gambiae s.s., AgamOBP4, AgamOBP5, AgamOBP6, AgamOBP19, AgamOBP20, AgamOBP83 and AgamORC7 were clustered in phylogenetic trees. Classical OBPs have been found to have six conserved cysteine residues and hydrophobic binding sites 75 – 77 , while ORs which are similar to G-protein coupled receptors have seven transmembrane domains 48 , 78 – 80 . Each of these typical traits were found in the amino acid sequences of predicted OBPs and OR, indicating that all of the predicted proteins were eligible.…”

“…In the reverse molecular docking, the 3D structures of citronellal were predicted using Chem3D 17.0 (Thermo Fisher Scientific, Waltham, MA, USA), and submitted to the PharmMapper web service. Reference literature was used to access the citronellal-binding OBPs or ORs 26 , 48 . Secondly, the amino acid sequences of the OBPs and ORs in A. gambiae s.s. (AgamOBPs and AgamORs) was accessed from the protein databases: NCBI, PDB and UniProt.…”

Machine learning decodes chemical features to identify novel agonists of a moth odorant receptor

“…Several recent studies revealed that the application of machine learning in the context of virtual screening opens up the possibility to enlarge animal odor spaces. Machine learning based on odorant chemical descriptors allowed predicting receptor-odorant interactions in both insects [22][23][24][25] and mammals 26 , although their ORs do not belong to the same protein families. Notably, quantitative structure-activity relationship (QSAR) is an in silico ligand-based method used to predict biological activity of untested chemicals, based on chemical features shared by active molecules 27 .…”

“…More recently, antagonists for the insect coreceptor Orco have been identified by screening a library of 1280 odorant molecules 28 . In mammals, a more modest virtual screening of 258 chemicals anyhow identified new agonists of four human ORs 26 . Although efficient, this approach requires prior knowledge on the response spectrum of a given OR and its application has thus been restricted to model species with cumulative odorant-receptor functional data.…”