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Visualizing the GPCR Network: Classification and Evolution

In this study, we delineate an unsupervised clustering algorithm, minimum span clustering (MSC), and apply it to detect G-protein coupled receptor (GPCR) sequences and to study the GPCR network using a base dataset of 2770 GPCR and 652 non-GPCR sequences. High detection accuracy can be achieved with...

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Autores principales: Hu, Geng-Ming, Mai, Te-Lun, Chen, Chi-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686146/
https://www.ncbi.nlm.nih.gov/pubmed/29138525
http://dx.doi.org/10.1038/s41598-017-15707-9
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author Hu, Geng-Ming
Mai, Te-Lun
Chen, Chi-Ming
author_facet Hu, Geng-Ming
Mai, Te-Lun
Chen, Chi-Ming
author_sort Hu, Geng-Ming
collection PubMed
description In this study, we delineate an unsupervised clustering algorithm, minimum span clustering (MSC), and apply it to detect G-protein coupled receptor (GPCR) sequences and to study the GPCR network using a base dataset of 2770 GPCR and 652 non-GPCR sequences. High detection accuracy can be achieved with a proper dataset. The clustering results of GPCRs derived from MSC show a strong correlation between their sequences and functions. By comparing our level 1 MSC results with the GPCRdb classification, the consistency is 87.9% for the fourth level of GPCRdb, 89.2% for the third level, 98.4% for the second level, and 100% for the top level (the lowest resolution level of GPCRdb). The MSC results of GPCRs can be well explained by estimating the selective pressure of GPCRs, as exemplified by investigating the largest two subfamilies, peptide receptors (PRs) and olfactory receptors (ORs), in class A GPCRs. PRs are decomposed into three groups due to a positive selective pressure, whilst ORs remain as a single group due to a negative selective pressure. Finally, we construct and compare phylogenetic trees using distance-based and character-based methods, a combination of which could convey more comprehensive information about the evolution of GPCRs.
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spelling pubmed-56861462017-11-29 Visualizing the GPCR Network: Classification and Evolution Hu, Geng-Ming Mai, Te-Lun Chen, Chi-Ming Sci Rep Article In this study, we delineate an unsupervised clustering algorithm, minimum span clustering (MSC), and apply it to detect G-protein coupled receptor (GPCR) sequences and to study the GPCR network using a base dataset of 2770 GPCR and 652 non-GPCR sequences. High detection accuracy can be achieved with a proper dataset. The clustering results of GPCRs derived from MSC show a strong correlation between their sequences and functions. By comparing our level 1 MSC results with the GPCRdb classification, the consistency is 87.9% for the fourth level of GPCRdb, 89.2% for the third level, 98.4% for the second level, and 100% for the top level (the lowest resolution level of GPCRdb). The MSC results of GPCRs can be well explained by estimating the selective pressure of GPCRs, as exemplified by investigating the largest two subfamilies, peptide receptors (PRs) and olfactory receptors (ORs), in class A GPCRs. PRs are decomposed into three groups due to a positive selective pressure, whilst ORs remain as a single group due to a negative selective pressure. Finally, we construct and compare phylogenetic trees using distance-based and character-based methods, a combination of which could convey more comprehensive information about the evolution of GPCRs. Nature Publishing Group UK 2017-11-14 /pmc/articles/PMC5686146/ /pubmed/29138525 http://dx.doi.org/10.1038/s41598-017-15707-9 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Hu, Geng-Ming
Mai, Te-Lun
Chen, Chi-Ming
Visualizing the GPCR Network: Classification and Evolution
title Visualizing the GPCR Network: Classification and Evolution
title_full Visualizing the GPCR Network: Classification and Evolution
title_fullStr Visualizing the GPCR Network: Classification and Evolution
title_full_unstemmed Visualizing the GPCR Network: Classification and Evolution
title_short Visualizing the GPCR Network: Classification and Evolution
title_sort visualizing the gpcr network: classification and evolution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686146/
https://www.ncbi.nlm.nih.gov/pubmed/29138525
http://dx.doi.org/10.1038/s41598-017-15707-9
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