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.

Visualizing Human Protein‐Protein Interactions and Subcellular Localizations on Cell Images Through CellMap

“…The first version of CellMap built upon a reliable dataset with multi‐localization annotations for the human proteome (Ramilowski et al., 2015) assembled from the Human Protein Atlas [HPA (Uhlén et al., 2015)], Swiss‐Prot [RRID:SCR_002380 (The UniProt Consortium, 2019)], and, when unavailable, from either homology or predictions (Goldberg et al., 2014). Experimental PPI interactions were taken from HIPPIE (Alanis‐Lobato et al., 2017), providing an estimate for the reliability of PPI annotations. While both the visualization tool and the web server may be extended greatly, and users may employ different data sources, the public instance of CellMap (http://cellmap.protein.properties) fulfills this purpose to date.…”

“…For this protocol, many small tweaks have been implemented in the user interface, based on feedback from 3 years of running the system. Additionally, the dataset has been updated with the latest annotations from HIPPIE (Alanis‐Lobato et al., 2017) and enriched with annotations for binary interactions from APID (Alonso‐López et al., 2019). We have removed the HIPPIE (Alanis‐Lobato et al., 2017) interactions with score below 0.01 to make sure that only reliable PPIs are presented.…”

“…Additionally, the dataset has been updated with the latest annotations from HIPPIE (Alanis‐Lobato et al., 2017) and enriched with annotations for binary interactions from APID (Alonso‐López et al., 2019). We have removed the HIPPIE (Alanis‐Lobato et al., 2017) interactions with score below 0.01 to make sure that only reliable PPIs are presented. To the best of our efforts, we could not find a multi‐compartment protein subcellular location dataset that was significantly better than previous work from former lab members (Ramilowski et al., 2015).…”

“…CellMap (http:// cellmap.protein.properties) visualizes proteins by their subcellular localizations on images of cells-optionally provided by the user-users can submit images of cells on the web server and perform the remainder of steps described in this protocol using the image provided. On top, CellMap displays lines (edges) between proteins that interact according to the PPI database HIPPIE (Alanis-Lobato, Andrade-Navarro, & Schaefer, 2017). This protocol explains (1) where to navigate to open up CellMap;…”

PPICurator: A Tool for Extracting Comprehensive Protein–Protein Interaction Information

“…None of them are capable of extracting other important PPI information, such as interaction directions, effects, and functional annotations. Such information is essential core knowledge in PPI repository for helping researchers construct protein interaction networks, detect signaling pathways, and discover meaningful protein interactions . Moreover, most of current systems lack batch processing capabilities and the ability to integrate and visualize results.…”