@article{10.1371/journal.pcbi.1002860, doi = {10.1371/journal.pcbi.1002860}, author = {Schaefer, Martin H. AND Lopes, Tiago J. S. AND Mah, Nancy AND Shoemaker, Jason E. AND Matsuoka, Yukiko AND Fontaine, Jean-Fred AND Louis-Jeune, Caroline AND Eisfeld, Amie J. AND Neumann, Gabriele AND Perez-Iratxeta, Carol AND Kawaoka, Yoshihiro AND Kitano, Hiroaki AND Andrade-Navarro, Miguel A.}, journal = {PLOS Computational Biology}, publisher = {Public Library of Science}, title = {Adding Protein Context to the Human Protein-Protein Interaction Network to Reveal Meaningful Interactions}, year = {2013}, month = {01}, volume = {9}, url = {https://doi.org/10.1371/journal.pcbi.1002860}, pages = {1-11}, 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.}, number = {1}, }