Abstract
Molecular interaction databases are essential resources that enable access to a wealth of information on associations between proteins and other biomolecules. Network graphs generated from these data provide an understanding of the relationships between different proteins in the cell, and network analysis has become a widespread tool supporting –omics analysis. Meaningfully representing this information remains far from trivial and different databases strive to provide users with detailed records capturing the experimental details behind each piece of interaction evidence. A targeted curation approach is necessary to transfer published data generated by primarily low-throughput techniques into interaction databases. In this review we present an example highlighting the value of both targeted curation and the subsequent effective visualization of detailed features of manually curated interaction information. We have curated interactions involving LRRK2, a protein of largely unknown function linked to familial forms of Parkinson's disease, and hosted the data in the IntAct database. This LRRK2-specific dataset was then used to produce different visualization examples highlighting different aspects of the data: the level of confidence in the interaction based on orthogonal evidence, those interactions found under close-to-native conditions, and the enzyme–substrate relationships in different in vitro enzymatic assays. Finally, pathway annotation taken from the Reactome database was overlaid on top of interaction networks to bring biological functional context to interaction maps.
Highlights
As proteins form larger assemblies to confer context-specific functionality [1], the systematic analysis of protein–protein interactions (PPIs) is a powerful strategy for identifying physiological pathways associated with a protein of interest, following the “guilt of association” principle, and to further characterize these pathways by unveiling the physical contacts that underlie them [2]
Biocurators working for primary databases [5] such as IntAct [6], Molecular INTeraction database (MINT) [7], MatrixDB [8], Database of Interacting Proteins (DIP) [9], or BioGRID [10] extract interaction evidence from published literature following defined rules, but these rules are not always defined by a community and can vary from database to database
Secondary resources aim to offer a more comprehensive view of the interactome by either merging several of these primary, curated datasets (Agile Protein Interaction DataAnalyzer (APID) [11], mentha [12], Human Integrated Protein-Protein Interaction rEference (HIPPIE) [13]) or by adding computationally predicted unexplored interactions based on the experimental data (Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) [14], Unified Human Interactome (UniHI) [15])
Summary
As proteins form larger assemblies to confer context-specific functionality [1], the systematic analysis of protein–protein interactions (PPIs) is a powerful strategy for identifying physiological pathways associated with a protein of interest, following the “guilt of association” principle, and to further characterize these pathways by unveiling the physical contacts that underlie them [2]. The detailed IMEx curation model means that the data can subsequently be subjected to sophisticated filtering and analysis, for example searching experimental evidence according to the host system in which they were generated, producing a tissue-specific interactome. Metadatabases such as STRING and mentha or some services accessing the data through the Proteomics Standard Initiative Common Query Interface (PSICQUIC) protocol [19] still lose part of this detailed information in order to reconcile IMEx entries with data from providers that do not curate to this level. Main procedures followed to generate the data described in this publication are highlighted
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