Overview

Protein-protein interaction (PPI) networks are graph representations of the physical contacts between proteins within a cell. Each protein is a node, and each experimentally detected or computationally predicted interaction is an edge. These networks reveal the functional organization of the proteome — proteins that interact often participate in the same biological process, reside in the same cellular compartment, or form stable complexes. By studying network topology, researchers can identify highly connected hub proteins, functional modules, and pathways that are dysregulated in disease. PPI networks integrate data from multiple experimental techniques and are essential for systems biology.

Key Concepts

Experimental sources for PPI data include yeast two-hybrid screening, affinity purification followed by mass spectrometry (AP-MS), and co-immunoprecipitation. Each method captures different aspects of interactions — binary versus co-complex, stable versus transient. Network properties such as degree distribution, clustering coefficient, and betweenness centrality characterize the global architecture. Hub proteins that connect many partners are often essential for cell viability. Functional modules are densely connected subgraphs that correspond to protein complexes or signaling pathways. Diseases frequently arise from mutations that disrupt specific edges or nodes within these networks, a concept known as network medicine.

Applications

PPI networks are used to predict protein function by guilt-by-association — an uncharacterized protein interacting with known DNA repair proteins is likely involved in DNA repair. In drug discovery, networks identify disease modules and prioritize therapeutic targets. The field builds on experimental methods such as the yeast two-hybrid system and antigen-antibody interactions for validation. Network analysis also contextualizes data from protein-protein interactions to generate mechanistic hypotheses about cellular regulation.