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A single organism can contain thousands of different proteins, and most of these proteins must interact with other proteins in order to perform their functions.

Many proteins have multiple binding partners, which results in a complex network of protein interactions.

Various laboratory techniques, including both biochemical and computational methods, help scientists understand these protein networks.

Affinity tagging is a method often used to isolate a protein along with any other interacting molecules. An affinity tag is an amino acid motif that is genetically added to the protein of interest. This tag can be used to isolate the target protein along with other proteins associated with it. 

Proteins interacting with the tagged protein can be analyzed to determine their identity and novel interactions.

One method of analyzing these unknown protein interactions is to break the proteins into short segments and analyze with a mass spectrometer to determine the amino acid sequences. These sequences can be compared to existing protein sequence databases to identify unknown associated proteins.  

Additionally, freely available databases, such as IntAct, have protein-protein interaction data uploaded by researchers worldwide available for comparison. Other databases coupled with a growing number of advanced bioinformatics tools, can be used to predict the molecular interactions of an unknown protein

Protein-protein interaction network maps are used to visualize these interactions between proteins in a cell.  The circles are nodes, which represent a particular protein, and lines are edges, which connect a protein to the other interacting proteins. 

Examining protein interaction maps can help predict protein functions. If an unknown protein's interaction pattern of connecting edges is similar to that of another node, these two nodes may act similarly.

Some nodes will appear as highly connected hubs, the study of which may lead to discovering fundamental mechanisms for maintaining the health of the cell thereby aiding in drug development.