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Spatial distribution of disease-associated variants in three-dimensional structures of protein complexes

机译:蛋白质复合物三维结构中疾病相关变异的空间分布

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Next-generation sequencing enables simultaneous analysis of hundreds of human genomes associated with a particular phenotype, for example, a disease. These genomes naturally contain a lot of sequence variation that ranges from single-nucleotide variants (SNVs) to large-scale structural rearrangements. In order to establish a functional connection between genotype and disease-associated phenotypes, one needs to distinguish disease drivers from neutral passenger variants. Functional annotation based on experimental assays is feasible only for a limited number of candidate mutations. Thus alternative computational tools are needed. A possible approach to annotating mutations functionally is to consider their spatial location relative to functionally relevant sites in three-dimensional (3D) structures of the harboring proteins. This is impeded by the lack of available protein 3D structures. Complementing experimentally resolved structures with reliable computational models is an attractive alternative. We developed a structure-based approach to characterizing comprehensive sets of non-synonymous single-nucleotide variants (nsSNVs): associated with cancer, non-cancer diseases and putatively functionally neutral. We searched experimentally resolved protein 3D structures for potential homology-modeling templates for proteins harboring corresponding mutations. We found such templates for all proteins with disease-associated nsSNVs, and 51 and 66% of proteins carrying common polymorphisms and annotated benign variants. Many mutations caused by nsSNVs can be found in protein–protein, protein–nucleic acid or protein–ligand complexes. Correction for the number of available templates per protein reveals that protein–protein interaction interfaces are not enriched in either cancer nsSNVs, or nsSNVs associated with non-cancer diseases. Whereas cancer-associated mutations are enriched in DNA-binding proteins, they are rarely located directly in DNA-interacting interfaces. In contrast, mutations associated with non-cancer diseases are in general rare in DNA-binding proteins, but enriched in DNA-interacting interfaces in these proteins. All disease-associated nsSNVs are overrepresented in ligand-binding pockets, and nsSNVs associated with non-cancer diseases are additionally enriched in protein core, where they probably affect overall protein stability.
机译:下一代测序可以同时分析与特定表型(例如疾病)相关的数百个人类基因组。这些基因组自然包含许多序列变异,范围从单核苷酸变异(SNV)到大规模结构重排。为了在基因型和疾病相关表型之间建立功能性联系,需要将疾病驱动因素与中性乘客变异区分开。基于实验分析的功能注释仅适用于有限数量的候选突变。因此,需要替代的计算工具。从功能上注释突变的一种可能方法是考虑相对于带有蛋白质的三维(3D)结构中功能相关位点的空间位置。由于缺少可用的蛋白质3D结构而受到阻碍。用可靠的计算模型补充实验解析的结构是一种有吸引力的选择。我们开发了一种基于结构的方法来表征非同义词单核苷酸变体(nsSNV)的全面集合:与癌症,非癌症疾病相关,并且假定具有功能中性。我们搜索了实验解析的蛋白质3D结构,以寻找具有相应突变的蛋白质的潜在同源性建模模板。我们为所有与疾病相关的nsSNVs的蛋白质找到了这样的模板,并且51%和66%的蛋白质带有常见的多态性和带注释的良性变异。 nsSNV引起的许多突变可以在蛋白质-蛋白质,蛋白质-核酸或蛋白质-配体复合物中发现。对每种蛋白质可用模板数量的校正表明,蛋白质-蛋白质相互作用界面在癌症nsSNV或与非癌症疾病相关的nsSNV中并未富集。尽管与癌症相关的突变富含DNA结合蛋白,但它们很少直接位于与DNA相互作用的界面中。相反,与非癌症疾病相关的突变通常在DNA结合蛋白中很少见,但在这些蛋白中的DNA相互作用界面中却很丰富。与疾病相关的所有nsSNV在配体结合袋中均过分表达,与非癌症疾病相关的nsSNV在蛋白质核心中也富集,它们可能会影响整体蛋白质的稳定性。

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