Abstract
Many studies have shown that missense mutations might play an important role in carcinogenesis. However, the extent to which cancer mutations might affect biomolecular interactions remains unclear. Here, we map glioblastoma missense mutations on the human protein interactome, model the structures of affected protein complexes and decipher the effect of mutations on protein-protein, protein-nucleic acid and protein-ion binding interfaces. Although some missense mutations over-stabilize protein complexes, we found that the overall effect of mutations is destabilizing, mostly affecting the electrostatic component of binding energy. We also showed that mutations on interfaces resulted in more drastic changes of amino acid physico-chemical properties than mutations occurring outside the interfaces. Analysis of glioblastoma mutations on interfaces allowed us to stratify cancer-related interactions, identify potential driver genes, and propose two dozen additional cancer biomarkers, including those specific to functions of the nervous system. Such an analysis also offered insight into the molecular mechanism of the phenotypic outcomes of mutations, including effects on complex stability, activity, binding and turnover rate. As a result of mutated protein and gene network analysis, we observed that interactions of proteins with mutations mapped on interfaces had higher bottleneck properties compared to interactions with mutations elsewhere on the protein or unaffected interactions. Such observations suggest that genes with mutations directly affecting protein binding properties are preferably located in central network positions and may influence critical nodes and edges in signal transduction networks.
Highlights
Most cancers are characterized by genomic instability which is considered to be one of the important factors driving tumor development [1]
Cancer Mutations might Affect Phosphorylation Sites Many proteins that play an important role in cancer may participate in signaling pathways, typically mediating signals through phosphorylation events
We embedded these interactions in a web of 4,073 interactions between 2,928 human proteins where each interaction was obtained by high-throughput methods as well as confirmed by the IBIS structural inference approach. In such a ‘confirmed interaction network’ we considered interactions that involved a protein with a mutation anywhere in a protein, allowing us to collect 444 ‘‘all mutant interactions’’ (AI)
Summary
Most cancers are characterized by genomic instability which is considered to be one of the important factors driving tumor development [1]. These genetic perturbations potentially lead to abnormal oncogene activation and/or tumor suppressor gene inactivation. Various methods have been applied to predict the deleterious effects of mutations [5,6], to find positively selected mutants and to distinguish driver from passenger mutations [7,8] Their predictive power remains limited, largely depends on the level of evolutionary conservation [9] and the background mutation rate which is difficult to determine for each sample [10]. Recent results suggest that a large majority of single nucleotide variations predicted to be functionally important are rare (with minor allele frequency less than 0.5%) [11], making such rare disease-associated variants difficult to detect
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