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Adding protein context to the human protein-protein interaction network to reveal meaningful interactions

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Item Type:Article
Title:Adding protein context to the human protein-protein interaction network to reveal meaningful interactions
Creators Name:Schaefer, M.H. and Lopes, T.J.S. and Mah, N. and Shoemaker, J.E. and Matsuoka, Y. and Fontaine, J.F. and Louis-Jeune, C. and Eisfeld, A.J. and Neumann, G. and Perez-Iratxeta, C. and Kawaoka, Y. and Kitano, H. and Andrade-Navarro, M.A.
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.
Keywords:Alzheimer Disease, Biocatalysis, Phosphorylation, Protein Binding, Proteins, Proteome, Signal Transduction, Viral Proteins
Source:PLoS Computational Biology
Publisher:Public Library of Science
Page Range:e1002860
Date:3 January 2013
Official Publication:https://doi.org/10.1371/journal.pcbi.1002860
PubMed:View item in PubMed

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