Classification and Evolution of the GPCR Similarity Network and Interactive Graph Database
Geng-Ming Hu1*, M.K. Secario2, Te-Lun Mai1,3, Chi-Ming Chen1
1Department of Physics, National Taiwan Normal University, Taipei, Taiwan
2Institute of Chemistry, Academia Sinica, Taipei, Taiwan
3Genomics Research Center, Academia Sinica, Taipei, Taiwan
* Presenter:Geng-Ming Hu,
In the omics era, due to the explosively rapid growth of newly identified protein/gene sequences, to characterize and analyze the biological properties of proteome efficiently has become an increasingly difficult task. Here, we delineate an unsupervised clustering algorithm, minimum span clustering (MSC), and apply it to study the GPCR network using a dataset of 2770 GPCR sequences. We found a strong correlation between their sequences and functions and high detection accuracy. The classification results by MSC for GPCRs can be well explained by evaluating the selective pressure, 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 negative selective pressure. We also 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. In addition, we propose a web-based tool, SeQuery, for intuitively visualizing proteome networks of GPCR by integrating the sequence/structure/function information of protein or gene sequences. This tool can use to correctly and promptly identify whether the sequence is a GPCR for the user-given query sequence, and, if so, to delineate its sequence/function role in the GPCR network; and can use to present a panoramic view of the GPCR network and its centralities, and allow users to explore the network at various characteristic resolutions.

Keywords: GPCR, Network, clustering, evolution