Protein – Protein Interactions

Protein-Protein interactions lie at the heart of most cellular processes, including carbohydrate and lipid metabolism, cell-cycle regulation, protein and nucleic acid metabolism, signal transduction, and cellular architecture. A complete understanding of cellular function depends on a full characterization of the complex network of cellular protein-protein association.

Experimental methods

1. Yeast two-hybrid system.

2. Affinity chromatography.

3. DIPTM database.

1. Yeast two-hybrid system:

This is the most commonly used technology for protein-protein interaction. Many new protein interactions have been discovered by this method. The method can characterize only bimolecular interactions.

2. Affinity chromatography:

This is a protein separation method that takes advantage of specific binding interactions that occur between molecules.

3. DIPTM database:

This is a database of interacting proteins and catalogues experimentally determined interactions between proteins. It combines information from a variety of sources to create a single consistent set of protein-protein interactions.

Non homology methods of inferring protein-protein interactions

Computational methods usually assign protein function by using sequence similarity approach. The non-homology strategy does not depend on sequence similarity. Instead the strategy is to group proteins that are part of same pathway and define them as being functionally linked.

The non-homology approaches are;

1. Domain fusion analysis.

2. Correlated messenger RNA expression patterns.

3. Phylogenetic profiles.

1. Domain fusion:

This strategy identifies fusion protein consisting of two non-homologous components found separately in another genome. Such components are expected to interact physically with each other. An interface between two interacting component is more likely to evolve when the proteins are fused in a single chain. In some respects the domain-fusion analysis is similar to the use of gene clusters for inferring functional links from gene proximity.

2. Correlated messenger RNA expression patterns:

This analysis is based on the premise that proteins with correlated levels over the same series of conditions are functionally linked. The functional annotations are usually broad, with functions specified as e.g. ‘metabolism’ or ‘transcription’. Even a random pair of proteins has a 50% chance of similar function at such a broad level. However as the annotations are generally derived from a number of linkages, they are much more informative than random links-comparable, in the best case, to experimental determination of protein-protein interactions.

3. Phylogenetic profiles:

Phylogenetic profiling relies on the correlated evolution of proteins. The evolution of two proteins is correlated when they share a Phylogenetic profile, which is defined as the pattern of a protein’s occurrence over a set of genomes. The Phylogenetic profile can be calculated precisely only when several complete genomes are compared. Two proteins that share a similar Phylogenetic profile are functionally linked. So clustering of proteins based on their Phylogenetic profiles can provide information about the function of an uncharacterized protein that is grouped with one or more functionally defined proteins.


The protein-protein interactions which are very important for most of the cellular studies can be detected by the experimental methods or the non-homology methods. This lays the basis for the next step in protein studies, such as drug discovery.