Visualizing the GPCR Network: Classification and Evolution

Geng Ming Hu, Te Lun Mai, Chi Ming Chen

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number15495
JournalScientific Reports
Volume7
Issue number1
DOIs
Publication statusPublished - 2017 Dec 1

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Proteins
Peptides
Clustering algorithms

ASJC Scopus subject areas

  • General

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Visualizing the GPCR Network : Classification and Evolution. / Hu, Geng Ming; Mai, Te Lun; Chen, Chi Ming.

In: Scientific Reports, Vol. 7, No. 1, 15495, 01.12.2017.

Research output: Contribution to journalArticle

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