Jump to content

Interactome: Difference between revisions

From Wikipedia, the free encyclopedia
Browse history interactively
← Previous editNext edit →
Content deleted Content added
VisualWikitext
Kalapat (talk | contribs)
34 edits
No edit summary
Kalapat (talk | contribs)
34 edits
No edit summary
Line 21: Line 21:
Affinity purification and subsequent [[mass spectrometry]] is suited to identify a protein complex. Both methods can be used in a high-throughput (HTP) fashion.
Affinity purification and subsequent [[mass spectrometry]] is suited to identify a protein complex. Both methods can be used in a high-throughput (HTP) fashion.


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 <ref>{{cite journal |doi=10.1093/nar/gkp306 |author=Kittichotirat W, Guerquin M, Bumgarner RE, Samudrala R.|title=Protinfo PPC: A web server for atomic level prediction of protein complexes.|journal=Nucleic Acids Research|volume=37 |issue=Web Server issue|pages=W519–W525|year=2009. |pmid=19420059 |pmc=2703994}}</ref>Tyagi et al "Large-scale mapping of human protein interactome using structural complexes", EMBO Reports, 2012. url: http://www.ncbi.nlm.nih.gov/pubmed/22261719</ref>
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 <ref>{{cite journal |doi=10.1093/nar/gkp306 |author=Kittichotirat W, Guerquin M, Bumgarner RE, Samudrala R.|title=Protinfo PPC: A web server for atomic level prediction of protein complexes.|journal=Nucleic Acids Research|volume=37 |issue=Web Server issue|pages=W519–W525|year=2009. |pmid=19420059 |pmc=2703994}}</ref><ref>Tyagi et al "Large-scale mapping of human protein interactome using structural complexes", EMBO Reports, 2012. url: http://www.ncbi.nlm.nih.gov/pubmed/22261719</ref>
as well as other protein–molecule interactions.<ref>{{cite journal |doi=10.1093/nar/gki401 |author=McDermott J, Guerquin M, Frazier Z, Chang AN, Samudrala R.|title=BIOVERSE: Enhancements to the framework for structural, functional, and contextual annotations of proteins and proteomes.|journal=Nucleic Acids Research|volume=33 |issue=Web Server issue|pages=W324–W325|year=2005 |pmid=15980482 |pmc=1160162}}</ref><ref>Shoemaker et al. IBIS (Inferred Biomolecular Interaction Server) reports, predicts and integrates multiple types of conserved interactions for proteins", Nucleic Asics Res., 2012. url:http://www.ncbi.nlm.nih.gov/pubmed/22102591</ref>
as well as other protein–molecule interactions.<ref>{{cite journal |doi=10.1093/nar/gki401 |author=McDermott J, Guerquin M, Frazier Z, Chang AN, Samudrala R.|title=BIOVERSE: Enhancements to the framework for structural, functional, and contextual annotations of proteins and proteomes.|journal=Nucleic Acids Research|volume=33 |issue=Web Server issue|pages=W324–W325|year=2005 |pmid=15980482 |pmc=1160162}}</ref><ref>Shoemaker et al. IBIS (Inferred Biomolecular Interaction Server) reports, predicts and integrates multiple types of conserved interactions for proteins", Nucleic Asics Res., 2012. url:http://www.ncbi.nlm.nih.gov/pubmed/22102591</ref>


Revision as of 23:13, 23 December 2012

Part of the DISC1 interactome with genes represented by text in boxes and interactions noted by lines between the genes. From Hennah and Porteous, 2009.[1]

In molecular biology an Interactome is defined as the whole set of molecular interactions in cells. Specifically it means 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.[2] The term interactome is partly synonymous with biological network.

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 [3] is the network involving Sirt-1 and its surrounding proteins. 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.[4] 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.

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. [5]

Although extremely important and useful, the interactome is still being developed and is not complete (as of October 2010[citation needed]). 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.[6]

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.

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 [7][8] as well as other protein–molecule interactions.[9][10]

Studied interactomes

Viral interactomes

Viral protein interactomes consist of interactions among viral or phage proteins. They were among of 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.[11] Some published virus interactomes include

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.

Bacterial interactomes

Relatively few bacteria have been comprehensively studied for their protein-protein interactions. Among them are Treponema pallidum [18] (the syphilis spirochete) and Campylobacter jejuni,[19] which habe been analyzed using extensive Y2H screens. The Escherichia coli interactome was studied by large-scale protein complex purification and mass spectrometry .[20]

Eukaryotic interactomes

There have been several efforts to map eukaryotic interactomes 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),[21] 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.[22]

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.[23][24]

See also

References

  1. ^ Hennah W, Porteous D (2009). "The DISC1 pathway modulates expression of neurodevelopmental, synaptogenic and sensory perception genes". PLoS ONE. 4 (3): e4906. doi:10.1371/journal.pone.0004906. PMC 2654149. PMID 19300510.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  2. ^ Sanchez C, Lachaize C, Janody F; et al. (1999). "Grasping at molecular interactions and genetic networks in Drosophila melanogaster using FlyNets, an Internet database". Nucleic Acids Res. 27 (1): 89–94. doi:10.1093/nar/27.1.89. PMC 148104. PMID 9847149. {{cite journal}}: Explicit use of et al. in: |author= (help); Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
  3. ^ Sharma, Ankush (Feb). "Interactomic and pharmacological insights on human Sirt-1". Front. Pharmacol. 3: 40. doi:10.3389/fphar.2012.00040. PMC 3311038. PMID 22470339. {{cite journal}}: Check date values in: |year= (help); Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |month= ignored (help)CS1 maint: unflagged free DOI (link) CS1 maint: year (link)
  4. ^ Stumpf MP, Thorne T, de Silva E; et al. (2008). "Estimating the size of the human interactome". Proc. Natl. Acad. Sci. U.S.A. 105 (19): 6959–64. doi:10.1073/pnas.0708078105. PMC 2383957. PMID 18474861. {{cite journal}}: Explicit use of et al. in: |author= (help); Unknown parameter |month= ignored (help)CS1 maint: multiple names: authors list (link)
  5. ^ 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. {{cite journal}}: Explicit use of et al. in: |author= (help)CS1 maint: multiple names: authors list (link)
  6. ^ Welch GR (2008). "The Fuzzy Interactome" (PDF). Cell Press.[dead link‍]
  7. ^ 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. {{cite journal}}: Check date values in: |year= (help)CS1 maint: multiple names: authors list (link) CS1 maint: year (link)
  8. ^ Tyagi et al "Large-scale mapping of human protein interactome using structural complexes", EMBO Reports, 2012. url: http://www.ncbi.nlm.nih.gov/pubmed/22261719
  9. ^ 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.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  10. ^ Shoemaker et al. IBIS (Inferred Biomolecular Interaction Server) reports, predicts and integrates multiple types of conserved interactions for proteins", Nucleic Asics Res., 2012. url:http://www.ncbi.nlm.nih.gov/pubmed/22102591
  11. ^ Navratil V.; et al. (2009). "VirHostNet: a knowledge base for the management and the analysis of proteome-wide virus-host interaction networks". Nucleic Acids Res. 37: D661-8. doi:10.1093/nar/gkn794. PMID 18984613. {{cite journal}}: Explicit use of et al. in: |author= (help)
  12. ^ Rajagopala SV.; et al. (2011). "The protein interaction map of bacteriophage lambda". BMC Microbiol. 11: 213. doi:10.1186/1471-2180-11-213. PMC 3224144. PMID 21943085. {{cite journal}}: Explicit use of et al. in: |author= (help)CS1 maint: unflagged free DOI (link)
  13. ^ Sabri M.; et al. (2011). "Genome annotation and intraviral interactome for the Streptococcus pneumoniae virulent phage Dp-1". J. Bact. 193 (2): 551–62. doi:10.1128/JB.01117-10. PMC 3019816. PMID 21097633. {{cite journal}}: Explicit use of et al. in: |author= (help)
  14. ^ Häuser R.; et al. (2011). "The proteome and interactome of Streptococcus pneumoniae phage Cp-1". J. Bact. 193 (12): 3135–8. doi:10.1128/JB.01481-10. PMC 3133188. PMID 21515781. {{cite journal}}: Explicit use of et al. in: |author= (help)
  15. ^ Stellberger, T. (2010). "Improving the yeast two-hybrid system with permutated fusions proteins: the Varicella Zoster Virus interactome". Proteome Sci. 8:8. 8: 8. doi:10.1186/1477-5956-8-8. PMC 2832230. PMID 20205919. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)CS1 maint: unflagged free DOI (link)
  16. ^ a b c d Fossum, E (2009). "Evolutionarily conserved herpesviral protein interaction networks". PLoS Pathog. 5(9):e1000570. 5 (9): e1000570. doi:10.1371/journal.ppat.1000570. PMC 2731838. PMID 19730696. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)CS1 maint: unflagged free DOI (link)
  17. ^ Attention: This template ({{cite pmid}}) is deprecated. To cite the publication identified by PMID 22763614, please use {{cite journal}} with |pmid= 22763614 instead.
  18. ^ Titz B.; et al. (2008). "The binary protein interactome of Treponema pallidum--the syphilis spirochete". Plos ONE. 3: e2292. doi:10.1371/journal.pone.0002292. PMC 2386257. PMID 18509523. {{cite journal}}: Explicit use of et al. in: |author= (help)CS1 maint: unflagged free DOI (link)
  19. ^ Parrish, JR (2007). "A proteome-wide protein interaction map for Campylobacter jejuni". Genome Biol. 8(7): R130. 8 (7): R130. doi:10.1186/gb-2007-8-7-r130. PMC 2323224. PMID 17615063. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)CS1 maint: unflagged free DOI (link)
  20. ^ Hu, P (2009). "Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins". PLoS Biol. 7(4):e96. 7 (4): e96. doi:10.1371/journal.pbio.1000096. PMC 2672614. PMID 19402753. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)CS1 maint: unflagged free DOI (link)
  21. ^ 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. {{cite journal}}: Explicit use of et al. in: |author= (help)CS1 maint: multiple names: authors list (link)
  22. ^ 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. {{cite journal}}: Explicit use of et al. in: |author= (help)
  23. ^ Attention: This template ({{cite pmid}}) is deprecated. To cite the publication identified by PMID 11101803, please use {{cite journal}} with |pmid=11101803 instead.
  24. ^ 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.{{cite journal}}: CS1 maint: multiple names: authors list (link)

Further reading

Interactome web servers

Interactome databases

Retrieved from "https://en.wikipedia.org/enwiki/w/index.php?title=Interactome&oldid=529507541"