Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat Biotechnol

Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.
Nature Biotechnology (Impact Factor: 41.51). 01/2012; 30(2):159-64. DOI: 10.1038/nbt.2106
Source: PubMed


To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.

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    • "This shows that even among conserved interactions, only a few key alterations at important binding sites can make the cross-species interactions incompatible and the interactions coevolved. Thus, these sites are critical to protein binding and subsequent function, and changes at these sites alter protein interactions in a manner analogous to a single amino acid change disrupting protein interactions in human disease (Wang et al., 2012; Wei et al., 2014). Among interactions for which we were able to determine coevolution status, we found that the likelihood for an interaction to be intact between S. pombe-S. "
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    ABSTRACT: Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ∼50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution.
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    • "As a result, 5739 PPIs and 20,405 PCPIs were prepared. To identify the interacting domains of each interacting protein, we adopted and expanded the method described by Wang et al. [6] . We annotated each interacting protein with the domains participating in the interaction if the interaction between the domains on both partners was described in iPfam or 3did (Fig. 1A). "
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    • "To take advantage of the dynamic nature of PPI data, a new three dimensional representation should be stated integrating protein structure, conformation, isoforms and spatial information. Several recent research works take advantage of this idea to incorporate atomic-level protein structure information in PPI networks (Das et al., 2014) in order to examine the structural principles of disease mutations over a PPI network, or even to elucidate the genetic and molecular mechanisms of underlying human diseases (Wang et al., 2012). One of the ultimate goals of PPI analysis should be the biomarkers' discovery. "

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