In molecular biology, an interactome is the whole set of molecular interactions in a particular cell. The term specifically refers to physical interactions among molecules but can also mean indirect interactions among genes, i.e. genetic interactions. It is generally displayed as an undirected graph. The word "interactome" was originally coined in 1999 by a group of French scientists headed by Bernard Jacq. Though interactomes may be described as biological networks, they should not be confused with other networks such as neural networks or food webs.
- 1 Molecular interaction networks
- 2 Size of interactomes
- 3 Genetic interaction networks
- 4 Methods of mapping the interactome
- 5 Studied interactomes
- 6 Interactome analysis
- 7 See also
- 8 References
- 9 Further reading
- 10 External links
Molecular interaction networks
Molecular interactions can occur between molecules belonging to different biochemical families (proteins, nucleic acids, lipids, carbohydrates, etc.) and also within a given family. Whenever such molecules are connected by physical interactions, they form molecular interaction networks that are generally classified by the nature of the compounds involved. Most commonly, interactome refers to protein–protein interaction (PPI) network (PIN) or subsets thereof. For instance, the Sirt-1 protein interactome and Sirt family second order interactome  is the network involving Sirt-1 and its directly interacting proteins where as second order interactome illustrates interactions up to second order of neighbors (Neighbors of neighbors). Another extensively studied type of interactome is the protein–DNA interactome, also called a gene-regulatory network, a network formed by transcription factors, chromatin regulatory proteins, and their target genes. Even metabolic networks can be considered as molecular interaction networks: metabolites, i.e. chemical compounds in a cell, are converted into each other by enzymes, which have to bind their substrates physically.
In fact, all interactome types are interconnected. For instance, protein interactomes contain many enzymes which in turn form biochemical networks. Similarly, gene regulatory networks overlap substantially with protein interaction networks and signaling networks.
Size of interactomes
It has been suggested that the size of an organism's interactome correlates better than genome size with the biological complexity of the organism. Although protein–protein interaction maps containing several thousands of binary interactions are now available for several organisms, none of them is presently complete and the size of interactomes is still a matter of debate.
Yeast. The yeast interactome, i.e. all protein-protein interactions among yeast proteins, has been estimated to contain between 10,000 and 30,000 interactions. A reasonable estimate may be on the order of 20,000 interactions. Larger estimates often include indirect or predicted interactions, often from affinity purification/mass spectrometry (AP/MS) studies.
Genetic interaction networks
Genes interact in the sense that they affect each other's function. For instance, a mutation may be harmless, but when it is combined with another mutation, the combination may turn out to be lethal. Such genes are said to "interact genetically". Genes that are connected in such a way form genetic interaction networks. Some of the goals of these networks are: develop a functional map of a cell's processes, drug target identification, and to predict the function of uncharacterized genes.
In 2010, the most "complete" gene interactome produced to date was compiled from about 5.4 million two-gene comparisons to describe "the interaction profiles for ~75% of all genes in the Budding yeast," with ~170,000 gene interactions. The genes were grouped based on similar function so as to build a functional map of the cell's processes. Using this method the study was able to predict known gene functions better than any other genome-scale data set as well as adding functional information for genes that hadn't been previously described. From this model genetic interactions can be observed at multiple scales which will assist in the study of concepts such as gene conservation. Some of the observations made from this study are that there were twice as many negative as positive interactions, negative interactions were more informative than positive interactions, and genes with more connections were more likely to result in lethality when disrupted.
Although extremely important and useful, the interactome is still being developed and is not complete (as of October 2010[update]). There are various factors that have a role in protein interactions that have yet to be incorporated in the interactome. Many[who?] have termed the interactome as a whole as being fuzzy. The binding strength of the various proteins, microenvironmental factors, sensitivity to various procedures, and the physiological state of the cell all affect protein–protein interactions, yet are not accounted for in the interactome. Although the interactome is useful in some ways, it must be analyzed knowing that these factors exist and can affect the protein interactions.
Methods of mapping the interactome
The study of interactomes is called interactomics. The basic unit of a protein network is the protein–protein interaction (PPI). Because an interactome considers the whole cells or organisms, there is a need to collect a massive amount of information.
Experimental methods to identify PPIs: the yeast two hybrid system (Y2H) is suited to explore the binary interactions among two proteins at a time. Affinity purification and subsequent mass spectrometry is suited to identify a protein complex. Both methods can be used in a high-throughput (HTP) fashion. Yeast two hybrid screens allow include false positive interactions between proteins that are never expressed in the same time and place; affinity capture mass spectrometry does not have this drawback, and is the current gold standard. Yeast two-hybrid data better indicates non-specific tendencies towards sticky interactions rather while affinity capture mass spectrometry better indicates functional in vivo protein-protein interactions.
Predicting PPIs: Using experimental data as a starting point, homology transfer is one way to predict interactomes. Here, PPIs from one organism are used to predict interactions among homologous proteins in another organism. 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.
Viral protein interactomes consist of interactions among viral or phage proteins. They were among the first interactome projects as their genomes are small and all proteins can be analyzed with limited resources. Viral interactomes are connected to their host interactomes, forming virus-host interaction networks. Some published virus interactomes include
- Escherichia coli bacteriophage lambda 
- Streptococcus pneumoniae bacteriophage Dp-1 
- Streptococcus pneumoniae bacteriophage Cp-1 
- Human Varicella Zoster Virus (VZV) 
- Kaposi's sarcoma-associated herpesvirus (KSHV) 
- Epstein-Barr virus (EBV) 
- Herpes simplex virus 1 (HSV-1) 
- Murine cytomegalovirus (mCMV) 
- Chandipura virus
The lambda and VZV interactomes are not only relevant for the biology of these viruses but also for technical reasons: they were the first interactomes that were mapped with multiple Y2H vectors, proving an improved strategy to investigate interactomes more completely than previous attempts have shown.
Relatively few bacteria have been comprehensively studied for their protein-protein interactions. However, none of these interactomes are complete in the sense that they captured all interactions. In fact, it has been estimated that none of them covers more than 20% or 30% of all interactions, primarily because most of these studies have only employed a single method, all of which discover only a subset of interactions. Among the published bacterial interactomes (including partial ones) are
|Synechocystis sp. PCC6803||3,264||3,236||Y2H|||
The E. coli and Mycoplasma interactomes have been analyzed using large-scale protein complex affinity purification and mass spectrometry (AP/MS), hence it is not easily possible to infer direct interactions. The others have used extensive Yeast two-hybrid (Y2H) screens. The Mycobacterium tuberculosis interactome has been analyzed using a bacterial two-hybrid screen (B2H).
There have been several efforts to map eukaryotic interactomes through HTP methods. As of 2006[update], yeast, fly, worm, and human HTP maps have been created. Recently, a pathogen-host interactome (Hepatitis C Virus/Human (2008), Epstein Barr virus/Human (2008), Influenza virus/Human (2009)) was also delineated through HTP to identify essential molecular components for pathogens and for their host's immune system.
Interactome data has been analyzed in many different ways and a huge body of literature has been published on interactome analyses. 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. The coverage of an interactome can be assessed by comparing it to benchmarks of well-known interactions that have been found and validated by independent assays.
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 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). Similarly, mutations of genes (and thus their proteins) can cause perturbations of networks and thus disease.
Network structure and modules: The distribution of properties among the proteins of an interactome has revealed functional modules within a network that indicate specialized subnetworks. Such modules can be purely 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 typically no affinities are known.
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Interactome web servers
- Protinfo PPC predicts the atomic 3D structure of protein protein complexes.Kittichotirat W, Guerquin M, Bumgarner R, Samudrala R. (2009). "Protinfo PPC: A web server for atomic level prediction of protein complexes". Nucleic Acids Research 37 (Web Server issue): W519–W525. doi:10.1093/nar/gkp306. PMC 2703994. PMID 19420059.
- IBIS (server) reports, predicts and integrates multiple types of conserved interactions for proteins.
- BioGRID database
- Bioverse database
- mentha the interactome browser (Calderone et al., 2013) mentha: a resource for browsing integrated protein-interaction networks. Nature Methods 10: 690–691. Available: http://dx.doi.org/10.1038/nmeth.2561.
- IntAct: The Molecular Interaction Database
- Interactome.org — a dedicated interactome web site.