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• W2011353204 endingPage "e1002860" @default.
• W2011353204 startingPage "e1002860" @default.
• W2011353204 abstract "Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways." @default.
• W2011353204 created "2016-06-24" @default.
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• W2011353204 date "2013-01-03" @default.
• W2011353204 modified "2023-09-30" @default.
• W2011353204 title "Adding Protein Context to the Human Protein-Protein Interaction Network to Reveal Meaningful Interactions" @default.
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• W2011353204 doi "https://doi.org/10.1371/journal.pcbi.1002860" @default.
• W2011353204 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3536619" @default.
• W2011353204 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23300433" @default.
• W2011353204 hasPublicationYear "2013" @default.
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