Spatial distribution of disease-associated variants in three-dimensional structures of protein complexes.
Spatial distribution of disease-associated variants in three-dimensional structures of protein complexes.
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DOI：
10.1038/oncsis.2017.79
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发表时间：
2017-09-25
期刊：
影响因子：
6.2
通讯作者：
Kalinina OV
中科院分区：
文献类型：
--
作者：
Gress A;Ramensky V;Kalinina OV
下一代测序能够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大量的序列变异，从单核苷酸变异（SNV）到大规模的结构重排。为了建立基因型和疾病相关表型之间的功能联系，需要区分疾病驱动因子和中性乘客变体。基于实验测定的功能注释仅对于有限数量的候选突变是可行的。因此，需要替代的计算工具。功能性注释突变的一种可能方法是考虑它们相对于所携带蛋白质的三维（3D）结构中的功能相关位点的空间位置。这受到缺乏可用蛋白质3D结构的阻碍。用可靠的计算模型补充实验解析的结构是一个有吸引力的选择。我们开发了一种基于结构的方法来表征非同义单核苷酸变体（nsSNV）的综合集合：与癌症，非癌症疾病和功能中性的脓疱相关。我们搜索实验解决蛋白质的3D结构潜在的同源性建模模板的蛋白质窝藏相应的突变。我们发现了所有与疾病相关的nsSNV蛋白质的模板，以及51%和66%携带常见多态性和注释的良性变异的蛋白质。由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。对每个蛋白质的可用模板数的校正揭示了蛋白质-蛋白质相互作用界面在癌症nsSNV或与非癌症疾病相关的nsSNV中均不富集。尽管癌症相关突变富含DNA结合蛋白，但它们很少直接位于DNA相互作用界面。相反，与非癌症疾病相关的突变在DNA结合蛋白中通常是罕见的，但在这些蛋白中的DNA相互作用界面中富集。所有与疾病相关的nsSNV在配体结合口袋中过度表达，并且与非癌症疾病相关的nsSNV另外在蛋白质核心中富集，在那里它们可能影响整体蛋白质稳定性。
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.
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