Proteins don’t just work in isolation, they form complex cliques and partnerships while some particularly gregarious proteins take multiple partners. It’s becoming increasingly apparent that in order to better understand a system, it’s insufficient to understand its component parts in isolation, especially if the simplest cog in the works end up being part of system like this.
On a macroscopic scale, a cell doesn’t care if the glucose it needs comes from lactose, converted by lactase into galactose and glucose, or from starch converted by amalase, or from glycogen, or from amino acids converted by gluconeogenesis. All it cares about is the glucose. If one of these multiple pathways should become unavailable, as long as the output is the same (glucose) the cell can continue to function. At a lower level, by forming networks of cooperating proteins, these increase a system’s robustness to change. The internal workings may be rewired, but many systems don’t care where their raw materials come from, just so long as they get them.
Whilst sequence similarity and homology modelling can explain the structure and function of an individual protein, its role in the greater scheme of things may still be in question. By modelling interaction networks, higher level questions can be asked such as: ‘What does this newly discovered complex do’? – ‘I don’t know, but yeast’s got something that looks quite like it.’ Homology modelling therefore isn’t just for single proteins.
Scoring the similarity of proteins in two species can be done using many non-exclusive metrics including:
Subsequently clustering these proteins based on their interaction partners, highlights the groups of proteins which form functional units. These are highly connected internally whilst having few edges to adjacent clusters. This can provide insight into previously un-investigated proteins which by virtue of being in a cluster of known purpose, their function can be inferred.