Odorant receptors as potential drug targets

Naressi, Rafaella G.; Schechtman, Deborah; Malnic, Bettina

doi:10.1016/j.tips.2022.08.003

Cited by 12 publications

(11 citation statements)

References 15 publications

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Section: Structure Predictionmentioning

confidence: 99%

“…3,4 The investigation of the physiological roles of these extranasal ORs, as well as their possible involvement in pathological conditions, is attracting a growing interest. 5,6 Moreover, given that GPCRs are the target of ∼34% of FDA-approved drugs 7 and the wide range of biologically active molecules binding to ORs, 8 these receptors are being explored as potential novel drug targets. 9,10 However, the lack of high-resolution structures for ORs has hindered the understanding of their functional mechanisms and the development of OR-targeting drugs.Recently, the field of computational biology has made significant strides in protein structure prediction, following the development of AlphaFold2, 11 a deep learning(DL)-based algorithm that can predict the 3D structures of proteins from their amino acid sequences with high accuracy.…”

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confidence: 99%

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Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

Alfonso‐Prieto

Capelli

2023

Preprint

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Atomistic-level investigation of olfactory receptors (ORs) is a challenging task due to the experimental/computational difficulties in the structural determination/prediction for members of this family of G-protein coupled receptors. Here we have developed a protocol that performs a series of molecular dynamics simulations from a set of structures predicted de novo by recent machine learning algorithms and apply it to a well-studied receptor, the human OR51E2. Our study demonstrates the need for extensive simulations to refine and validate such models. Furthermore, we demonstrate the need for the sodium ion at a binding site near D2.50 and E3.39 to stabilize the inactive state of the receptor. Considering the conservation of these two acidic residues across human ORs, we surmise this requirement also applies to the other ~400 members of this family.

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Section: Structure Predictionmentioning

confidence: 99%

mentioning

confidence: 99%

Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

Alfonso‐Prieto

Capelli

2023

Preprint

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“…The human genome encodes for approximately 800 GPCRs, out of which 50% are ORs . Although initially identified in the nose, ORs are expressed in different parts of the body. , The investigation of the physiological roles of these extranasal ORs, as well as their possible involvement in pathological conditions, is attracting a growing interest. , Moreover, given that GPCRs are the target of ∼34% of FDA-approved drugs and the wide range of biologically active molecules binding to ORs, these receptors are being explored as potential novel drug targets. , However, the lack of high-resolution structures for ORs has hindered the understanding of their functional mechanisms and the development of OR-targeting drugs.…”

Section: Initial Modelsmentioning

confidence: 99%

Machine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study

Alfonso‐Prieto

Capelli

2023

J. Chem. Inf. Model.

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Atomistic-level investigation of olfactory receptors (ORs) is a challenging task due to the experimental/computational difficulties in the structural determination/prediction for members of this family of G-protein coupled receptors. Here, we have developed a protocol that performs a series of molecular dynamics simulations from a set of structures predicted de novo by recent machine learning algorithms and apply it to a well-studied receptor, the human OR51E2. Our study demonstrates the need for simulations to refine and validate such models. Furthermore, we demonstrate the need for the sodium ion at a binding site near D 2.50 and E 3.39 to stabilize the inactive state of the receptor. Considering the conservation of these two acidic residues across human ORs, we surmise this requirement also applies to the other ∼400 members of this family. Given the almost concurrent publication of a CryoEM structure of the same receptor in the active state, we propose this protocol as an in silico complement to the growing field of ORs structure determination.

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“…Here, we take advantage of the genotype-tissue expression (GTEx) public data set ( Consortium, 2013 ) to investigate the tissue expression patterns of protein-coding olfactory receptor (OR) genes. OR genes account for approximately 50% of G protein-coupled receptor genes, which constitute the largest group in human membrane receptors ( Naressi et al, 2022 ). ORs are known to be distributed at many tissues, such as lung, heart, kidney, brain, prostate, airway, and testis ( Kang and Koo, 2012 ; Massberg and Hatt, 2018 ; Oh, 2018 ; Lee et al, 2019 ; Raka et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Distinct sets of olfactory receptors highly expressed in different human tissues evaluated by meta-transcriptome analysis: Association of OR10A6 in skin with keratinization

Nakanishi

Tsutsui

Itai

et al. 2023

Front. Cell Dev. Biol.

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Olfactory receptors (ORs) are expressed in many tissues and have multiple functions. However, most studies have focused on individual ORs. Here, we aimed to conduct a comprehensive meta-transcriptome analysis of OR gene expression in human tissues by using open-source tools to search a large, publicly available genotype-tissue expression (GTEx) data set. Analysis of RNA-seq data from GTEx revealed that OR expression patterns were tissue-dependent, and we identified distinct sets of ORs that were highly expressed in 12 tissues, involving 97 ORs in total. Among them, OR5P2, OR5P3 and OR10A6 were associated with skin. We further examined the roles of these ORs in skin by performing weighted gene correlation network analysis (WGCNA) and c3net analysis. WGCNA suggested that the three ORs are involved in epidermal differentiation and water-impermeable barrier homeostasis, and OR10A6 showed the largest gene sub-network in the c3net network. Immunocytochemical examination of human skin keratinocytes revealed a sparse expression pattern of OR10A6, suggesting that it is not uniformly distributed among all keratinocytes. An OR10A6 agonist, 3-phenylpropyl propionate (3PPP), transiently increased intracellular Ca2+ concentration and increased cornified envelope (CE) production in cultured keratinocytes. Knock-down of OR10A6 diminished the effect of 3PPP. Overall, integration of meta-transcriptome analysis and functional analysis uncovered distinct expression patterns of ORs in various human tissues, providing basic data for future studies of the biological functions of highly expressed ORs in individual tissues. Our results further suggest that expression of OR10A6 in skin is related to epidermal differentiation, and OR10A6 may be a potential target for modulation of keratinization.

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Odorant receptors as potential drug targets

Cited by 12 publications

References 15 publications

Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

Machine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study

Distinct sets of olfactory receptors highly expressed in different human tissues evaluated by meta-transcriptome analysis: Association of OR10A6 in skin with keratinization

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