HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks

Abstract

The increasing number of experimentally detected interactions between proteins makes it difficult for researchers to extract the interactions relevant for specific biological processes or diseases. This makes it necessary to accompany the large-scale detection of protein-protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein-Protein Interaction rEference or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of experimental information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.

Figure 1.

Data in Human Integrated Protein–Protein Interaction rEference (HIPPIE) and how it can be used to reconstruct signaling events. (A) HIPPIE integrates heterogeneous data types: PPIs from expert-curated source databases are constantly updated and integrated into HIPPIE. Experimental meta-information (e.g. on the experimental methods employed to detect PPIs and on their reproducibility) is extracted and used to compute a confidence score for each interaction in HIPPIE. Gene expression, gene function and phenotypic data are aggregated and used to annotate the PPIs stored in HIPPIE and to infer edge effect and directionality. (B) The reconstruction of central components of the MAPK pathway downstream of BRAF is shown as a HIPPIE query example. The kinases BRAF, MEK1 (MAP2K1) and ERK1 (MAPK3) are members of the Mitogen-activated protein kinase (MAPK) signaling cascade and activate each other in the stated order. BRAF is frequently mutated in several cancers, colon cancer among them, where BRAF mutations are found in approximately 9% of all patients (51). Querying HIPPIE with the kinases BRAF, MEK1 and ERK1 and filtering for high confidence PPIs and colon expression results in the depicted network. Displaying shortest paths between BRAF (‘source’, in green) and transcription factors (‘sinks’) correctly reproduces the chain of signaling events (BRAF activates MEK1, which activates ERK1 in turn). All terminal nodes in pink (ELK1, MYC, JUN, TP53, SREBF1/2) are known substrates of ERK1 (52).

Figure 2.

HIPPIE query types and result network interpretation. (A) When a single protein is used as an input in HIPPIE's Protein Query Tab, it produces a table with the interacting partners of that protein. The confidence score for each interaction is also listed and the interactors of each partner can be easily queried with a single click. (B) If a list of proteins or interactions is used as an input in HIPPIE's Network Query Tab, it produces a network of interactions between these proteins. In this query type, it is possible to implement filters that put the list of interactions in a functional and cellular context and the user can choose between different output types. In the example, HIPPIE was queried for high-confidence interactions between the core members of the Wnt signaling pathway, showing predicted information flow (arrow direction) and interaction effects (activating interactions are indicated by triangle arrowheads). (C) HIPPIE's Screen Annotation Tab allows to check whether a list of measured interactions is present in the database and how reliable each link is. In the example, a tab-separated file is uploaded to HIPPIE and it outputs a new file with the confidence score of each interaction or −1 if it is not present. (D and E) The results of the Protein and Network Queries allow one to perform a disease or functional enrichment analysis for the members of the resulting protein subnetwork. These analyses are carried out via the tools Gene Set to Diseases (32) and PANTHER (33), respectively.