TY - DATA T1 - Data underlying the article: Pan-cancer in silico analysis of somatic mutations in G-protein coupled receptors: The effect of evolutionary conservation and natural variance PY - 2021/10/26 AU - Brandon Bongers AU - Marina Gorostiola Gonzalez AU - Xuesong Wang AU - Herman W. T. van Vlijmen AU - Willem Jespers AU - Hugo Gutiérrez-de-Terán AU - Kai Ye AU - Adriaan P. IJzerman AU - Laura H. Heitman AU - Gerard van Westen UR - https://data.4tu.nl/articles/dataset/Data_underlying_the_article_Pan-cancer_in_silico_analysis_of_somatic_mutations_in_G-protein_coupled_receptors_The_effect_of_evolutionary_conservation_and_natural_variance/15022410/1 DO - 10.4121/15022410.v1 KW - GDC KW - 1000 Genomes KW - Cancer KW - Mutations KW - Natural variance KW - Pareto optimization KW - Multi-objective KW - GPCR N2 -
This repository contains the datasets and source code supporting the conclusions of the manuscript "Pan-cancer in silico analysis of somatic mutations in G-protein coupled receptors: The effect of evolutionary conservation and natural variance". G protein-coupled receptors (GPCRs) form the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used an aggregated dataset of mutations found in cancer patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 Genomes project. We investigated the location of these mutations across the protein domains and conserved residues in GPCRs such as the “DRY” motif. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). In conclusion, this study identifies a list of GPCRs that are prioritized for experimental follow up characterization to elucidate their role in cancer. The computational approach here described can be adapted to investigate the roles in cancer of any protein family.

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