Interactome

Interactome
Part of the DISC1 interactome. From Hennah and Porteous, 2009.

Interactome is defined as the whole set of molecular interactions in cells. It is usually displayed as a directed graph. Molecular interactions can occur between molecules belonging to different biochemical families (proteins, nucleic acids, lipids, carbohydrates, etc.) and also within a given family. When spoken in terms of proteomics, interactome refers to protein–protein interaction network (PPI), or protein interaction network (PIN). Another extensively studied type of interactome is the protein–DNA interactome (network formed by transcription factors (and DNA or chromatin regulatory proteins) and their target genes. The word "interactome" was originally coined in 1999 by a group of French scientists headed by Bernard Jacq (see Nucleic acids research 27(1):89-94; PubMed ID: 9847149).

It has been suggested that the size of an organism's interactome correlates better than genome size with the biological complexity of the organism (Stumpf, et al., 2008). 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. In 2010, the most "complete" gene interactome produced to date was compiled from 54 million two-gene comparisons to describe "the interaction profiles for ~75% of all genes in the Budding yeast," with 170,000 gene interactions.[1]

Although extremely important and useful, the interactome is still being developed and is not complete (as of October 2010). 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.[2]

Contents

Methods of mapping the interactome

The study of the interactome is called interactomics. The basic unit of protein network is protein–protein interaction (PPI). Because the interactome considers the whole organism, there is a need to collect a massive amount of information.

Experimental methods have been devised to determine PPI, such as affinity purification and yeast two hybrid (Y2H). The former is suited to identify a protein complex, while the latter is suited to explore the binary interactions in mass quantities. The former is considered as a low-throughput method (LTP), while the latter is considered as high-throughput method (HTP).

Using the experimental data as a starting point, the concept of homology transfer has been used to develop algorithms to map the interactome, including ones that produce detailed atomic models of protein protein complexes [3] as well as other protein–molecule interactions.[4]

There have been several efforts to map the eukaryotic interactome through HTP methods. As of 2006, yeast, fly, worm, and human HTP maps have been created. Recently, pathogen-host interactome (Hepatitis C Virus/Human (2008),[5] Epstein Barr virus/Human (2008), Influenza virus/Human (2009)) was also delineated through HTP to identify essential molecular components for pathoghens but also for the host to recognize pathogens and trigger efficient innate immune response.[6]

Using the interactome

Researchers have begun to use preliminary versions of the interactome to gain understanding about the biology and function of the molecules within them. For example, protein interaction networks have been used to produce improved protein functional annotations (or nannotations) for proteins with unknown functions.[7]

Interactome web servers

  • Protinfo PPC predicts the atomic 3D structure of protein protein complexes.[8]

Interactome databases

See also

References

  1. ^ Costanzo M, Baryshnikova A, Bellay J, et al. (2010-01-22). "The genetic landscape of a cell". Science 327 (5964): 425–431. doi:10.1126/science.1180823. PMID 20093466. 
  2. ^ Welch GR (2008). "The Fuzzy Interactome". Cell Press. http://200.145.134.134/twiki/pub/Main/Miscelanea/0312081.pdf. 
  3. ^ Kittichotirat W, Guerquin M, Bumgarner RE, 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. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2703994. 
  4. ^ McDermott J, Guerquin M, Frazier Z, Chang AN, Samudrala R. (2005). "BIOVERSE: Enhancements to the framework for structural, functional, and contextual annotations of proteins and proteomes.". Nucleic Acids Research 33 (Web Server issue): W324–W325. doi:10.1093/nar/gki401. PMC 1160162. PMID 15980482. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1160162. 
  5. ^ de Chassey B, Navratil V, Tafforeau L, et al. (2008-11-04). "Hepatitis C virus infection protein network". Molecular Systems Biology 4 (4:230): 230. doi:10.1038/msb.2008.66. PMC 2600670. PMID 18985028. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2600670. 
  6. ^ Navratil V, de Chassey B, et al. (2010-11-05). "Systems-level comparison of protein–protein interactions between viruses and the human type I interferon system network.". Journal of Proteome Research 9 (7): 3527–36. doi:10.1021/pr100326j. PMID 20459142. 
  7. ^ McDermott J, Bumgarner RE, Samudrala R. (2005). "Functional annotation from predicted protein interaction networks.". Bioinformatics 21 (15): 3217–3226. doi:10.1093/bioinformatics/bti514. PMID 15919725. 
  8. ^ 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: W519-W525. 

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