Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors

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 machi...

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Autores principales:	Amara Jabeen, Claire A. de March, Hiroaki Matsunami, Shoba Ranganathan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning random forest molecular descriptors virtual ligand screening olfactory receptor G protein-coupled receptors Biology (General) QH301-705.5 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/6b3d55e354404b5a8d1d1e509105cf8f
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spelling	oai:doaj.org-article:6b3d55e354404b5a8d1d1e509105cf8f2021-11-11T17:01:09ZMachine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors10.3390/ijms2221115461422-00671661-6596https://doaj.org/article/6b3d55e354404b5a8d1d1e509105cf8f2021-10-01T00:00:00Zhttps://www.mdpi.com/1422-0067/22/21/11546https://doaj.org/toc/1661-6596https://doaj.org/toc/1422-0067Olfactory 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 develop 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.Amara JabeenClaire A. de MarchHiroaki MatsunamiShoba RanganathanMDPI AGarticlemachine learningrandom forestmolecular descriptorsvirtual ligand screeningolfactory receptorG protein-coupled receptorsBiology (General)QH301-705.5ChemistryQD1-999ENInternational Journal of Molecular Sciences, Vol 22, Iss 11546, p 11546 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning random forest molecular descriptors virtual ligand screening olfactory receptor G protein-coupled receptors Biology (General) QH301-705.5 Chemistry QD1-999
spellingShingle	machine learning random forest molecular descriptors virtual ligand screening olfactory receptor G protein-coupled receptors Biology (General) QH301-705.5 Chemistry QD1-999 Amara Jabeen Claire A. de March Hiroaki Matsunami Shoba Ranganathan Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors
description	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 develop 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.
format	article
author	Amara Jabeen Claire A. de March Hiroaki Matsunami Shoba Ranganathan
author_facet	Amara Jabeen Claire A. de March Hiroaki Matsunami Shoba Ranganathan
author_sort	Amara Jabeen
title	Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors
title_short	Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors
title_full	Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors
title_fullStr	Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors
title_full_unstemmed	Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors
title_sort	machine learning assisted approach for finding novel high activity agonists of human ectopic olfactory receptors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6b3d55e354404b5a8d1d1e509105cf8f
work_keys_str_mv	AT amarajabeen machinelearningassistedapproachforfindingnovelhighactivityagonistsofhumanectopicolfactoryreceptors AT claireademarch machinelearningassistedapproachforfindingnovelhighactivityagonistsofhumanectopicolfactoryreceptors AT hiroakimatsunami machinelearningassistedapproachforfindingnovelhighactivityagonistsofhumanectopicolfactoryreceptors AT shobaranganathan machinelearningassistedapproachforfindingnovelhighactivityagonistsofhumanectopicolfactoryreceptors
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Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors

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