Editorial: Protein Interaction Networks in Health and Disease

The identification and annotation of protein-protein interactions (PPIs) is of great importance in systems biology. Big data produced from experimental or computational approaches allow not only the construction of large protein interaction maps but also expand our knowledge on how proteins build up molecular complexes to perform sophisticated tasks inside a cell. However, if we want to accurately understand the functionality of these complexes, we need to go beyond the simple identification of PPIs. We need to know when and where an interaction happens in the cell and also understand the flow of information through a protein interaction network. Another perspective of the research on PPI networks is the study of their relation to disease. In disease conditions, mutations that alter the secondary structure of one protein might perturb its PPIs, as well. Thereafter many things can go wrong via cascading effects, caused by the inter-relatedness of the mutated protein to other proteins through the PPI network. Such perturbations could block the formation of a protein complex or lead to the formation of new protein complexes and the activation of abnormal signaling pathways. These events could alter the cellular transcriptome profile and further contribute to disease pathogenesis. That is why the maintenance of the proper structure and functionality of a PPI network is crucial for cellular homeostasis. Its disruption can cause complex effects and understanding them requires advanced methods for analysis. The aim of this Research Topic is to present novel findings and recent achievements in the field of PPI networks. Thematically, it is divided into two parts. First, we present methods for the identification and quantification of PPIs; second, we describe computational approaches to annotate interactomes and extract information related to disease prediction or disease progression. The first four articles deal with the identification and quantification of PPIs. In the first work, Suter et al. describe the application of next generation sequencing (NGS) for the characterization of binary PPIs. Authors present an accurate method to analyze yeast two-hybrid data by NGS and also interpret interaction data via quantitative statistics. They also discuss how this methodology can be used to discover differential PPIs allowing the identification of disease mechanisms (Suter et al.). The next two review articles describe mass spectrometry (MS) based approaches. Yang et al. present methods that can determine the relative abundance of purified proteins in a sample enabling the identification of transient PPIs in different conditions. Additionally, …