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.

Informatics on Drug Repurposing for Breast Cancer

“… 46 Integration of a PPI network in a multi-omics context is conducive to highlighting key proteins or central clusters in drug action and response mechanisms, and removing the false interactions. 47 Cross-verification using multiple binding free energy calculation methods as all such methods currently have limitations and advantages that partially overlap and complement one another. 45 …”

“…Integration of a PPI network in a multi-omics context is conducive to highlighting key proteins or central clusters in drug action and response mechanisms, and removing the false interactions. 47 …”

Proteomic analysis of cerebrospinal fluid of amyotrophic lateral sclerosis patients in the presence of autologous bone marrow derived mesenchymal stem cells

“…It has to be mentioned that we are still not able to address whether Extracellular Matrix/Cell Adhesion Molecules highlighted in this study are related to putative MSC actions after intrathecal delivery in ALS patients, to ongoing ALS motor neuron degeneration or a possible interaction of both, a matter that must be the subject of further investigations. Anyhow, extracellular matrix and cell adhesion have been largely Network evaluation (73,74) in the criteria herein applied to point out those highlighted molecules. Importantly, it is the rst time APOA1, APOE, APP, C4A, C5, FGA, FGB, FGG and PLG have been mentioned…”

The Use of Graph Databases in Systems Biology: A Systematic Review

“…In a graph, proteins can be represented as nodes and PPIs as edges [28,37]. Due to their capabilities in facilitating network-based integration, querying and analysis, GDB approaches gained popularity for managing PPI data [38]. Here, we outline the specific advantages of GDBs for management of PPI data focusing on i) heterogeneous data integration and exploration and ii) support for network-based analysis and modelling.…”