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

Abstract

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.

Figure 1

Distance between residues corresponding to nsSNVs and the nearest interaction partner (log scale). Biological data sets are shown in a darker shade. The fraction of mapped nsSNVs, for which a template with a co-resolved corresponding interaction partner is provided below boxes representing distribution of distances to protein, ligand and DNA interaction partners for each biological data set. For randomized data sets, all 10 replicas are used to create the plots. (a) Distances to the nearest protein chain. (b) Distances to the nearest ligand. (c) Distances to the nearest DNA chain.

Figure 2

Chemical difference between wild-type and mutated residues. Gray bars indicate biological data sets, light-gray bars indicate randomized data sets. Chemical distance is calculated as Euclidean distances between the end points of the vectors representing five most important numerical descriptors of physical and chemical properties of the wild-type and mutant amino acids.

Figure 3

Spatial distribution of nsSNVs in the analyzed data sets. For randomized data sets, mean values over 10 replicas are used. (a) For templates with ⩾35% sequence identity. (b) For templates with ⩾90% sequence identity.

Figure 4

Protein complexes with nsSNVs in multiple subunits. (a) Mitochondrial respiratory complex II (mapped onto a homologous complex from porcine heart, PDB id 1ZOY) and the corresponding sub-network (see text). FAD-binding protein is shown in green, mutations therein in pink; iron–sulfur protein is shown in cyan, mutations therein in orange; large cytochrome binding protein is shown in magenta, mutations therein in purple; small cytochrome binding protein is shown in yellow, mutation therein in limegreen. In the sub-network, nodes correspond to individual proteins, edges depict interactions between them. (b) Sub-network corresponding to complexes of CDK6 with its inhibitors CDKN2A and CDKN2C. Stoichiometry of the complexes is not accounted for, and nodes with a single loop edge correspond to associations of multiple identical subunits. (c) Sub-network corresponding to NRas, KRas and HRas and their downstream kinase RAF1 and activity factors SOS1 and PLCE1. (d) PIK3CA-PIK3R1 complex with mutations corresponding to cancer-associated somatic nsSNVs (top) and to nsSNVs associated with non-cancer diseases (bottom), PDB id 4L1B and the PIK3CA-PIK3R1 sub-network. PIK3CA subunit is shown in green, mutations therein in magenta and purple. PIK3R1 subunit is shown in cyan, mutations therein in orange and red.

Figure 5

Contacts and distance distributions for oncogenes and tumor-suppressor genes (TSG). (a) Distribution of nsSNVs into structural classes. (b–d) Distances to the nearest interaction partners: (b) protein chain, (c) ligand, (d) DNA chain.