Once an interactome has been created, there are numerous ways to analyze its properties. However, there are two important goals of such analyses. First, scientists try to elucidate the systems properties of interactomes, e.g. the topology of its interactions. Second, studies may focus on individual proteins and their role in the network. Such analyses are mainly carried out using
bioinformatics methods and include the following, among many others:
Validation First, the coverage and quality of an interactome has to be evaluated. Interactomes are never complete, given the limitations of experimental methods. For instance, it has been estimated that typical
Y2H screens detect only 25% or so of all interactions in an interactome. Other methods filter out false positives calculating the similarity of known annotations of the proteins involved or define a likelihood of interaction using the subcellular localization of these proteins.
Predicting PPIs ''. Some algorithms use experimental evidence on structural complexes, the atomic details of binding interfaces and produce detailed atomic models of protein–protein complexes as well as other protein–molecule interactions. Other algorithms use only sequence information, thereby creating unbiased complete networks of interaction with many mistakes. Some methods use machine learning to distinguish how interacting protein pairs differ from non-interacting protein pairs in terms of pairwise features such as cellular colocalization, gene co-expression, how closely located on a DNA are the genes that encode the two proteins, and so on.
Random Forest has been found to be most-effective machine learning method for protein interaction prediction. Such methods have been applied for discovering protein interactions on human interactome, specifically the interactome of
Membrane proteins Protein function prediction Protein interaction networks have been used to predict the function of proteins of unknown functions. This is usually based on the assumption that uncharacterized proteins have similar functions as their interacting proteins (
guilt by association). For example, YbeB, a protein of unknown function was found to interact with ribosomal proteins and later shown to be involved in bacterial and eukaryotic (but not archaeal)
translation. Although such predictions may be based on single interactions, usually several interactions are found. Thus, the whole network of interactions can be used to predict protein functions, given that certain functions are usually enriched among the interactors.
Perturbations and disease The
topology of an interactome makes certain predictions how a network reacts to the
perturbation (e.g. removal) of nodes (proteins) or edges (interactions). Such perturbations can be caused by
mutations of genes, and thus their proteins, and a network reaction can manifest as a
disease. A network analysis can identify
drug targets and
biomarkers of diseases.
Network structure and topology Interaction networks can be analyzed using the tools of
graph theory. Network properties include the
degree distribution,
clustering coefficients,
betweenness centrality, and many others. The distribution of properties among the proteins of an interactome has revealed that the interactome networks often have
scale-free topology where
functional modules within a network indicate specialized subnetworks. Such modules can be functional, as in a
signaling pathway, or structural, as in a protein complex. In fact, it is a formidable task to identify protein complexes in an interactome, given that a network on its own does not directly reveal the presence of a stable complex. ==Studied interactomes==