Contextualized Protein-Protein Interactions

Protein-protein interaction (PPI) databases are an important bioinformatics resource, yet existing literature-curated databases usually represent cell-type-agnostic interactions, which is at variance with our understanding that protein dynamics are context specific and highly dependent on their environment. Here, we provide a resource derived through data mining to infer disease- and tissue-relevant interactions by annotating existing PPI databases with cell-contextual information extracted from reporting studies. This resource is applicable to the reconstruction and analysis of disease-centric molecular interaction networks. We have made the data and method publicly available and plan to release scheduled updates in the future. We expect these resources to be of interest to a wide audience of researchers in the life sciences. •We present PPI Context: contextualization of existing literature-curated PPIs•A resource for filtering PPIs by cell-line information mined from reporting studies•A fast and flexible pipeline implementing the presented data mining method Existing literature-curated protein-protein interaction (PPI) databases usually aggregate cell-type-agnostic interactions, yet PPIs are dependent on environmental conditions. Thus, new methods and resources for inferring the context in which a PPI is reported will extend their application and use in disease-centric modeling. We expect the resource presented in this article to be of high interest to those querying known interactions of proteins of interest, reconstruction and analyses of molecular interaction networks, and multi-omics data integration approaches. Literature-curated protein-protein interactions (PPIs) are an essential bioinformatics resource. A major challenge is determining the biological context in which an interaction was observed. Here, we present a data mining method for extracting cell-line information for existing PPIs from reporting studies and make the resulting data available for building disease-centric interaction networks.