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 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.

Download PDF Subjects Phylogenetics Proteome informatics 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

related reading

• A Gentle Introduction to Graph Neural Networksdistill.pub
• A perspective on 16S rRNA operational taxonomic unit clustering using sequence similaritynature.com
• De novo design of miniproteins targeting GPCRs | Naturenature.com
• Highly accurate protein structure prediction with AlphaFoldnature.com
• Quantifying the effect of experimental perturbations at single-cell resolutionnature.com
• Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context - PMCpmc.ncbi.nlm.nih.gov
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationproceedings.neurips.cc
• Clustering analysis of tumor metabolic networks | BMC Bioinformatics | Springer Nature Linkbmcbioinformatics.biomedcentral.com
• G protein-coupled receptor - Wikipediaen.wikipedia.org
• Mutational interactions define novel cancer subgroupsnature.com
• Visualizing music similarity: clustering and mapping 500 classical music composers | Scientometrics | Springer Nature Linklink.springer.com
• Chapter 3 Feature selection | Basics of Single-Cell Analysis with Bioconductorbioconductor.org