Machine learning assisted approach for finding novel high activity agonists of human ectopic olfactory receptors

Amara Jabeen, Claire A. de March, Hiroaki Matsunami*, Shoba Ranganathan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Olfactory receptors (ORs) constitute the largest superfamily of G protein-coupled receptors (GPCRs). ORs are involved in sensing odorants as well as in other ectopic roles in non-nasal tissues. Matching of an enormous number of the olfactory stimulation repertoire to its counterpart OR through machine learning (ML) will enable understanding of olfactory system, receptor characterization, and exploitation of their therapeutic potential. In the current study, we have selected two broadly tuned ectopic human OR proteins, OR1A1 and OR2W1, for expanding their known chemical space by using molecular descriptors. We present a scheme for selecting the optimal features required to train an ML-based model, based on which we selected the random forest (RF) as the best performer. High activity agonist prediction involved screening five databases comprising ~23 M compounds, using the trained RF classifier. To evaluate the effectiveness of the machine learning based virtual screening and check receptor binding site compatibility, we used docking of the top target ligands to carefully developed receptor model structures. Finally, experimental validation of selected compounds with significant docking scores through in vitro assays revealed two high activity novel agonists for OR1A1 and one for OR2W1.
Original languageEnglish
Article number11546
Pages (from-to)1-17
Number of pages17
JournalInternational Journal of Molecular Sciences
Volume22
Issue number21
Early online date26 Oct 2021
DOIs
Publication statusPublished - 1 Nov 2021

Bibliographical note

Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • machine learning
  • random forest
  • molecular descriptors
  • virtual ligand screening
  • olfactory receptor
  • G protein-coupled receptors
  • luciferase assay

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