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

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.

本代码仓库包含支持研究论文《泛癌视角下G蛋白偶联受体(G-protein coupled receptors, GPCRs)体细胞突变的计算分析：进化保守性与自然变异的影响》结论的数据集与源代码。G蛋白偶联受体是目前被开发利用最为广泛的药物靶点家族，且在癌症患者中常被检测到存在体细胞突变。本研究采用源自基因组数据共享库(Genomic Data Commons, GDC)的癌症患者样本突变聚合数据集，并以千人基因组计划(1000 Genomes Project)所获数据作为自然人群遗传变异的参照基准，开展了对比分析。我们系统探究了上述突变在G蛋白偶联受体的蛋白质结构域与保守残基（如“DRY”基序）中的分布特征。随后，我们基于多目标优化策略——帕累托前沿排序(Pareto Front Ranking)，构建了高分候选G蛋白偶联受体的优先级排序清单。综上，本研究筛选出一批优先级靠前的G蛋白偶联受体，可为后续实验表征研究提供候选靶点，以阐明其在癌症发生发展中的具体作用。本研究所述的计算机分析框架可被推广应用，用于探究任意蛋白质家族在癌症发生发展中的功能机制。