Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context

Abstract

At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Keywords: biological network; computational prediction; graphic view; integrated strategies; interactome; protein-protein interaction.

FIGURE 1

Workflow of key steps to design a PPI network assembly. PPI networks can be integrated horizontally and/or vertically. Horizontal integration creates a PPI network by concatenating interaction information from different PPI databases (here networks 1 and 2 represent two PPI networks from two different databases), while vertical integration gathered information from different omics databases for a given interaction. In the vertical integration box, each omics network represents different interactomes such as protein–protein, drug–protein, and RNA–protein. Once the networks are generated, it is necessary to evaluate its interactions confidence to filter the network. Interactions in red are interactions with a high confidence score. After narrowing the network, specialized tools can be used to visualize the network and information about the connected entities (e.g., identify proteins with a central role in the mechanisms).