Decoding Partner Specificity of Opioid Receptor Family
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2021 |
| Outros Autores: | , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Texto Completo: | https://hdl.handle.net/10316/104528 https://doi.org/10.3389/fmolb.2021.715215 |
Resumo: | This paper describes an exciting big data analysis compiled in a freely available database, which can be applied to characterize the coupling of different G-Protein coupled receptors (GPCRs) families with their intracellular partners. Opioid receptor (OR) family was used as case study in order to gain further insights into the physiological properties of these important drug targets, known to be associated with the opioid crisis, a huge socio-economic issue directly related to drug abuse. An extensive characterization of all members of the ORs family (μ (MOR), δ (DOR), κ (KOR), nociceptin (NOP)) and their corresponding binding partners (ARRs: Arr2, Arr3; G-protein: Gi1, Gi2, Gi3, Go, Gob, Gz, Gq, G11, G14, G15, G12, Gssh, Gslo) was carried out. A multi-step approach including models' construction (multiple sequence alignment, homology modeling), complex assembling (protein complex refinement with HADDOCK and complex equilibration), and protein-protein interface characterization (including both structural and dynamics analysis) were performed. Our database can be easily applied to several GPCR sub-families, to determine the key structural and dynamical determinants involved in GPCR coupling selectivity. |
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Decoding Partner Specificity of Opioid Receptor Familydatabasefunctional signatureGPCRsopioid receptorG-proteinarrestinThis paper describes an exciting big data analysis compiled in a freely available database, which can be applied to characterize the coupling of different G-Protein coupled receptors (GPCRs) families with their intracellular partners. Opioid receptor (OR) family was used as case study in order to gain further insights into the physiological properties of these important drug targets, known to be associated with the opioid crisis, a huge socio-economic issue directly related to drug abuse. An extensive characterization of all members of the ORs family (μ (MOR), δ (DOR), κ (KOR), nociceptin (NOP)) and their corresponding binding partners (ARRs: Arr2, Arr3; G-protein: Gi1, Gi2, Gi3, Go, Gob, Gz, Gq, G11, G14, G15, G12, Gssh, Gslo) was carried out. A multi-step approach including models' construction (multiple sequence alignment, homology modeling), complex assembling (protein complex refinement with HADDOCK and complex equilibration), and protein-protein interface characterization (including both structural and dynamics analysis) were performed. Our database can be easily applied to several GPCR sub-families, to determine the key structural and dynamical determinants involved in GPCR coupling selectivity.Frontiers Media S.A.2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://hdl.handle.net/10316/104528https://hdl.handle.net/10316/104528https://doi.org/10.3389/fmolb.2021.715215eng2296-889XBarreto, Carlos A. V.Baptista, SaletePreto, Antonio J.Silvério, DanielMelo, RitaMoreira, Irina S.info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2023-01-16T21:44:57Zoai:estudogeral.uc.pt:10316/104528Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T05:54:45.416334Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
Decoding Partner Specificity of Opioid Receptor Family |
| title |
Decoding Partner Specificity of Opioid Receptor Family |
| spellingShingle |
Decoding Partner Specificity of Opioid Receptor Family Barreto, Carlos A. V. database functional signature GPCRs opioid receptor G-protein arrestin |
| title_short |
Decoding Partner Specificity of Opioid Receptor Family |
| title_full |
Decoding Partner Specificity of Opioid Receptor Family |
| title_fullStr |
Decoding Partner Specificity of Opioid Receptor Family |
| title_full_unstemmed |
Decoding Partner Specificity of Opioid Receptor Family |
| title_sort |
Decoding Partner Specificity of Opioid Receptor Family |
| author |
Barreto, Carlos A. V. |
| author_facet |
Barreto, Carlos A. V. Baptista, Salete Preto, Antonio J. Silvério, Daniel Melo, Rita Moreira, Irina S. |
| author_role |
author |
| author2 |
Baptista, Salete Preto, Antonio J. Silvério, Daniel Melo, Rita Moreira, Irina S. |
| author2_role |
author author author author author |
| dc.contributor.author.fl_str_mv |
Barreto, Carlos A. V. Baptista, Salete Preto, Antonio J. Silvério, Daniel Melo, Rita Moreira, Irina S. |
| dc.subject.por.fl_str_mv |
database functional signature GPCRs opioid receptor G-protein arrestin |
| topic |
database functional signature GPCRs opioid receptor G-protein arrestin |
| description |
This paper describes an exciting big data analysis compiled in a freely available database, which can be applied to characterize the coupling of different G-Protein coupled receptors (GPCRs) families with their intracellular partners. Opioid receptor (OR) family was used as case study in order to gain further insights into the physiological properties of these important drug targets, known to be associated with the opioid crisis, a huge socio-economic issue directly related to drug abuse. An extensive characterization of all members of the ORs family (μ (MOR), δ (DOR), κ (KOR), nociceptin (NOP)) and their corresponding binding partners (ARRs: Arr2, Arr3; G-protein: Gi1, Gi2, Gi3, Go, Gob, Gz, Gq, G11, G14, G15, G12, Gssh, Gslo) was carried out. A multi-step approach including models' construction (multiple sequence alignment, homology modeling), complex assembling (protein complex refinement with HADDOCK and complex equilibration), and protein-protein interface characterization (including both structural and dynamics analysis) were performed. Our database can be easily applied to several GPCR sub-families, to determine the key structural and dynamical determinants involved in GPCR coupling selectivity. |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021 |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
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article |
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publishedVersion |
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https://hdl.handle.net/10316/104528 https://hdl.handle.net/10316/104528 https://doi.org/10.3389/fmolb.2021.715215 |
| url |
https://hdl.handle.net/10316/104528 https://doi.org/10.3389/fmolb.2021.715215 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
2296-889X |
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info:eu-repo/semantics/openAccess |
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openAccess |
| dc.publisher.none.fl_str_mv |
Frontiers Media S.A. |
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Frontiers Media S.A. |
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reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia instacron:RCAAP |
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info@rcaap.pt |
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