An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein, and edges are lines that connect two interacting proteins. These networks provide a way to visualize the complexity of the protein-protein interactions in a system. These maps can include both stable interactions, like the ones formed in protein complexes, as well as transient interactions. The protein interactions occurring in a cell, an organism, or a specific biological context can be collectively called an ‘interactome’.
Protein networks can be studied using various biochemical and computational methods. One of the first steps in studying protein interactions is to isolate a protein of interest along with the other associated proteins. This can be carried out by tagging the protein of interest with an affinity tag, such as a histidine tag. This tag can then be used to separate the protein along with the other proteins using affinity chromatography. Isolated proteins are then digested with a protease, such as trypsin, and then analyzed using liquid chromatography-mass spectrometry (LC-MS). The peptide mass can then be compared to a database with known protein sequences to determined its identity.
Computationally, protein-protein interactions can be analyzed using databases as well as prediction tools. There are various databases, such as IntAct managed by EMBL-EBI, that consist of experimentally validated and predicted protein interactions. Other tools like STRING by the Swiss Institute of Bioinformatics can be used to predict these interaction networks.
The study of protein networks can lead to scientific discoveries, such as determining the function of an unknown protein. Examining the changes in these networks can help elucidate the differences between healthy and diseased cells. This information can also be used for crucial applications such as designing drugs for the treatment of diseases. Analysis of protein networks can identify highly connected nodes that may be crucial for cellular survival, which can be targeted in cancer and diseases where cell death is desired but would be unsuitable for most diseases. On the other hand, less connected nodes that only interact with a few specific pathways may be targeted if a specific cell function is affected, and designing drugs that interact with these less connected nodes may lead to fewer side effects.
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