Studying candidate disease mutations in the context of these networks may provide important clues as to how mutations affect biological processes. Because of the limited availability of co-crystallization protein structures [46]
strategies have been developed to predict structure at protein interfaces using homology models [26•]. Nonetheless, this type of analysis will only be possible for a subset of candidate disease mutations. Joint study of co-evolution of amino-acid residues at protein interfaces and network structure may provide insights into which residues are essential for maintaining interactions [40, 47 and 48]. Fridman et al. found that affinity-altering mutations in proliferating cell nuclear antigen (PCNA) BTK inhibitor clinical trial could have more severe consequences for DNA replication and repair
than mutations completely abolishing interactions [ 40]. Their findings suggest that even within interfaces, mutations are likely to have distinct phenotypic consequences. Thus it may be important to include manipulation of specific find more interactions as part of mutagenesis studies when experimentally evaluating candidate disease genes. Emerging genome engineering strategies provide exciting opportunities for experimentally characterizing domain specific effects of mutations on network activities [ 49]. The non-random organization of biological networks suggests that their topology may encode information about how molecular interactions contribute to biological phenotypes [50]. Molecular interaction networks within the cell tend to be modular; that is, proteins related to
the same biological activities often form connected modules within networks [5, 6, 7, 50 and 51]. Goh et al. showed that this phenomenon extends to disease genes as well; genes implicated in the same diseases often cluster within PPI networks [ 52 and 53]. The existence of functional and disease modules within interactome networks supports a of ‘guilt-by-association’ (GBA) strategy for identifying novel disease-associated genes [5 and 54]. GBA has been used to intelligently reduce the list of candidate disease genes in association studies [54 and 55]. Bergholdt et al. combined PPI network overlap with genes located at GWAS risk loci and subnetwork-based enrichment for differential expression to identify new candidate type I diabetes disease genes [ 56]. Identification of network modules enriched for mutation or variable expression under disease conditions can point to specific biological processes disrupted in disease. For example, analysis of the network distribution of de novo mutations in sporadic cases with autism spectrum disorders implicated a highly interconnected subnetwork of proteins involved in β-catenin/chromatin remodeling [ 57]. Goh et al. also investigated differences in network connectivity of three classes of genes: essential, inherited and somatic disease genes [ 52 and 53].
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