Protein Characterization Services


Advantages Of Protein Characterization

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. The full comprehension of cellular function depends on a full characterization within 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:

Right here is the normally used technology for Protein Characterization Services.Many new protein interactions are actually discovered by this method. The strategy can characterize only bimolecular interactions.

2. Affinity chromatography:

This can be a protein separation method that uses specific binding interactions that occur between molecules.

3. DIPTM database:

This is usually a database of interacting catalogues and proteins experimentally determined interactions between proteins. It combines information from numerous sources to develop a single consistent couple of protein-protein interactions.

Non homology approaches to inferring protein-protein interactions

With the help of sequence similarity approach, Computational methods usually assign protein function. The non-homology strategy is not going to be based upon sequence similarity. Instead the strategy is to group proteins which can be element 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 plan identifies fusion protein consisting of two non-homologous components found separately in another genome. Such components are expected to have interaction physically collectively. An interface between two interacting component is very likely to evolve in the event the proteins are fused in a single chain. A number of respects the domain-fusion analysis resembles making use of gene clusters for inferring functional links from gene proximity.

2. Correlated messenger RNA expression patterns:

This analysis draws on the premise that proteins with correlated levels within the same line of conditions are functionally linked. The functional annotations are often broad, with functions specified as e.g. 'metabolism' or 'transcription'. Also a random pair of proteins carries a 50% probability of similar function at a real broad level. These are even more informative than random links-comparable, within the best case, to experimental determination of protein-protein interactions, being the annotations are likely to be resulting from several linkages.

3. Phylogenetic profiles:

Phylogenetic profiling will depend 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 on the protein's occurrence over a group of genomes. The Phylogenetic profile is often calculated precisely provided that several complete genomes are compared. Two proteins that share a similar Phylogenetic profile are functionally linked. So, clustering of proteins depending on their Phylogenetic profiles can provide more knowledge about the function of an uncharacterized protein that is certainly grouped with several functionally defined proteins.


The protein-protein interactions which are important for a lot of the cellular studies could be detected via the experimental methods as well as non-homology methods. This lays the idea for the following step up protein studies, for instance drug discovery.