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.<p></p>
ER -