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
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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ABSTRACT: Understanding how different genomic mutational landscapes in patients with cancer lead to different responses to anticancer drugs is an important challenge for realizing precision medicine for cancer. Many studies have analyzed the comprehensive anticancer drug-response profiles and genomic profiles of cancer cell lines to identify the relationship between the anticancer drug response and genomic alternations. However, few studies have focused on interpreting these profiles with a network perspective. In this work, we analyzed genomic alterations in cancer cell lines by considering which interactions in the signaling pathway were perturbed by mutations. With our interaction-centric approach, we identified novel interaction/drug response associations for two drugs (afatinib and ixabepilone) for which no gene-centric association could be found. When we compared the performance of classifiers for predicting the responses to 164 drugs, the classifiers trained with interaction-centric features outperformed the classifiers trained with gene-centric features, despite the smaller number of features (p-value = 2.0 × 10−3). By incorporating the interaction information from signaling pathways, we revealed associations between genomic alterations and drug responses that could be missed when using a gene-centric approach.
- "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. "