Interactome

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{{cite journal|last=Sharma|first=Ankush |author2=Gautam VK |author3=Costantini S |author4=Paladino A |author5=Colonna G|title=Interactomic and pharmacological insights on human Sirt-1|journal=Front. Pharmacol.|date=Feb 2012|doi=10.3389/fphar.2012.00040|pmid=22470339|pmc=3311038|volume=3|pages=40|doi-access=free }}{{cite journal|last=Sharma|first=Ankush|author2=Costantini S |author3=Colonna G |title=The protein–protein interaction network of human Sirtuin family|journal=Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics|volume=1834|issue=10|pages=1998–2009|date=March 2013|arxiv=1302.6423|pmid=23811471|doi=10.1016/j.bbapap.2013.06.012|bibcode=2013arXiv1302.6423S|s2cid=15003130}} 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.

File:Yeast interactome size estimate.png

It has been suggested that the size of an organism's interactome correlates better than genome size with the biological complexity of the organism.{{cite journal |author=Stumpf MP |title=Estimating the size of the human interactome |journal=Proc. Natl. Acad. Sci. U.S.A. |volume=105 |issue=19 |pages=6959–64 |date=May 2008 |pmid=18474861 |pmc=2383957 |doi=10.1073/pnas.0708078105 |author2= Thorne T |author3= de Silva E |display-authors=etal|bibcode=2008PNAS..105.6959S |doi-access=free }} Although protein–protein interaction maps containing several thousand binary interactions are now available for several species, none of them is presently complete and the size of interactomes is still a matter of debate.

The yeast interactome, i.e. all protein–protein interactions among proteins of Saccharomyces cerevisiae, 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.

{{main|Genetic interaction network}}

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 using chemoproteomics, 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.{{cite journal |doi=10.1126/science.1180823 |author=Costanzo M |title=The genetic landscape of a cell |journal=Science |date=2010-01-22 |volume=327 |issue=5964 |pages=425–431 |pmid=20093466|pmc=5600254 |author2= Baryshnikova A |author3= Bellay J |display-authors=etal|bibcode=2010Sci...327..425C }}

Interactomics is a discipline at the intersection of bioinformatics and biology that deals with studying both the interactions and the consequences of those interactions between and among proteins, and other molecules within a cell.{{cite journal | last =Kiemer | first=L |author2=G Cesareni | year = 2007| title = Comparative interactomics: comparing apples and pears? | journal= Trends in Biotechnology | volume = 25 |pages = 448–454 | doi =10.1016/j.tibtech.2007.08.002 | pmid =17825444 | issue =10}} Interactomics thus aims to compare such networks of interactions (i.e., interactomes) between and within species in order to find how the traits of such networks are either preserved or varied.

Interactomics is an example of "top-down" systems biology, which takes an overhead view of a biosystem or organism. Large sets of genome-wide and proteomic data are collected, and correlations between different molecules are inferred. From the data new hypotheses are formulated about feedbacks between these molecules. These hypotheses can then be tested by new experiments.{{cite journal | last =Bruggeman | first=F J |author2=H V Westerhoff| year = 2006| title = The nature of systems biology | journal= Trends in Microbiology | volume = 15 |pages = 45–50| doi = 10.1016/j.tim.2006.11.003 |pmid =17113776 | issue =1}}

The study of interactomes is called interactomics. The basic unit of a protein network is the protein–protein interaction (PPI). While there are numerous methods to study PPIs, there are relatively few that have been used on a large scale to map whole interactomes.

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 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.{{cite journal|last=Brettner|first=Leandra M.|author2=Joanna Masel|title=Protein stickiness, rather than number of functional protein–protein interactions, predicts expression noise and plasticity in yeast|journal=BMC Systems Biology|year=2012|volume=6|pages=128|doi=10.1186/1752-0509-6-128|pmid=23017156|pmc=3527306 |doi-access=free }} {{open access}}{{cite journal |last=Mukherjee |first=K |author2=Slawson |author3=Christmann |author4=Griffith |date=June 2014 |title=Neuron-specific protein interactions of Drosophila CASK-ß are revealed by mass spectrometry |journal=Front. Mol. Neurosci. |volume= 7|pages= 58| pmid =25071438|doi = 10.3389/fnmol.2014.00058 | pmc=4075472|doi-access=free }}

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:

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.{{Cite journal | last1 = Rajagopala | first1 = S. V. | last2 = Hughes | first2 = K. T. | last3 = Uetz | first3 = P. | doi = 10.1002/pmic.200900282 | title = Benchmarking yeast two-hybrid systems using the interactions of bacterial motility proteins | journal = Proteomics | volume = 9 | issue = 23 | pages = 5296–5302 | year = 2009 | pmid = 19834901 | pmc =2818629 }} 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.{{Cite journal | author = Yanay Ofran, Guy Yachdav, Eyal Mozes, Ta-tsen Soong, Rajesh Nair & Burkhard Rost | title = Create and assess protein networks through molecular characteristics of individual proteins | journal = Bioinformatics | volume = 22 | issue = 14 | pages = e402–e407 | date = July 2006 | doi = 10.1093/bioinformatics/btl258 | pmid = 16873500 | doi-access = free }}

File:Schziophrenia PPI.jpg

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 ("interologs"). However, this approach has certain limitations, primarily because the source data may not be reliable (e.g. contain false positives and false negatives).{{cite journal |vauthors=Mika S, Rost B |title=Protein–Protein Interactions More Conserved within Species than across Species|journal=PLOS Computational Biology|volume=2|issue=7|pages=e79|year=2006|pmid=16854211|pmc=1513270|doi=10.1371/journal.pcbi.0020079|bibcode=2006PLSCB...2...79M |doi-access=free }} In addition, proteins and their interactions change during evolution and thus may have been lost or gained. Nevertheless, numerous interactomes have been predicted, e.g. that of Bacillus licheniformis.{{cite journal | last1 = Han | first1 = Y.-C. |display-authors=etal | year = 2016| title = Prediction and characterization of protein–protein interaction network in Bacillus licheniformis WX-02 | journal = Sci. Rep. | volume = 6 | page = 19486 | doi = 10.1038/srep19486 | pmid = 26782814 | pmc = 4726086 | bibcode = 2016NatSR...619486H }}

Some algorithms use experimental evidence on structural complexes, the atomic details of binding interfaces and produce detailed atomic models of protein–protein complexes{{cite journal |doi=10.1093/nar/gkp306 |vauthors=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}}{{cite journal | pmid = 22261719 | pmc=3296913 | doi=10.1038/embor.2011.261 | volume=13 | issue=3 | title=Large-scale mapping of human protein interactome using structural complexes | date=Mar 2012 | journal=EMBO Rep | pages=266–71| last1=Tyagi | first1=M | last2=Hashimoto | first2=K | last3=Shoemaker | first3=B. A. | last4=Wuchty | first4=S | last5=Panchenko | first5=A. R.}} as well as other protein–molecule interactions.{{cite journal |doi=10.1093/nar/gki401 |vauthors=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}}{{cite journal | pmid = 22102591 | pmc=3245142 | doi=10.1093/nar/gkr997 | volume=40 | issue=Database issue | title=IBIS (Inferred Biomolecular Interaction Server) reports, predicts and integrates multiple types of conserved interactions for proteins | date=Jan 2012 | journal=Nucleic Acids Res | pages=D834–40| last1=Shoemaker | first1=B. A. | last2=Zhang | first2=D | last3=Tyagi | first3=M | last4=Thangudu | first4=R. R. | last5=Fong | first5=J. H. | last6=Marchler-Bauer | first6=A | last7=Bryant | first7=S. H. | last8=Madej | first8=T | last9=Panchenko | first9=A. R.}} {{cite journal|doi=10.7554/eLife.03430|vauthors=Hopf TA, Schaerfe CP, Rodrigues JP, Green AG, Kohlbacher O, Sander C, Bonvin AM, Marks DS |title=Sequence co-evolution gives 3D contacts and structures of protein complexes|journal=eLife|volume=3|pages=e03430|year=2014|pmid= 25255213 |pmc=4360534|arxiv=1405.0929 |bibcode=2014arXiv1405.0929H |doi-access=free }} Other algorithms use only sequence information, thereby creating unbiased complete networks of interaction with many mistakes.{{cite journal|doi=10.1038/nmeth.3178|vauthors=Kotlyar M, Pastrello C, Pivetta F, Lo Sardo A, Cumbaa C, Li H, Naranian T, Niu Y, Ding Z, Vafaee F, Broackes-Carter F, Petschnigg J, Mills GB, Jurisicova A, Stagljar I, Maestro R, Jurisica I |title=In silico prediction of physical protein interactions and characterization of interactome orphans|journal=Nature Methods|volume=12|issue=1|pages=79–84|year=2015|pmid= 25402006 |s2cid=5287489 }}

{{cite journal|doi=10.1093/bioinformatics/btv077|vauthors=Hamp T, Rost B |title=Evolutionary profiles improve protein–protein interaction prediction from sequence|journal=Bioinformatics|volume=31|issue=12|pages=1945–1950|year=2015|pmid= 25657331 |doi-access=free}}

{{cite journal|doi=10.1038/srep00239|vauthors=Pitre S, Hooshyar M, Schoenrock A, Samanfar B, Jessulat M, Green JR, Dehne F, Golshani A |title=Short Co-occurring Polypeptide Regions Can Predict Global Protein Interaction Maps|journal=Scientific Reports|volume=2|pages=239|year=2012|pmid= 22355752|pmc=3269044|bibcode=2012NatSR...2..239P}}

{{cite journal|doi=10.1038/srep00239|vauthors=Pitre S, Hooshyar M, Schoenrock A, Samanfar B, Jessulat M, Green JR, Dehne F, Golshani A |title=Short co-occurring polypeptide regions can predict global protein interaction maps|journal=Scientific Reports|volume=2|pages=239|year=2012|pmid=22355752|pmc=3269044|bibcode=2012NatSR...2E.239P}}

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.{{cite journal | vauthors = Ganapathiraju MK, Thahir M, Handen A, Sarkar SN, Sweet RA, Nimgaonkar VL, Loscher CE, Bauer EM, Chaparala S| title = Schizophrenia interactome with 504 novel protein–protein interactions | journal = npj Schizophrenia | volume = 2 | date = April 2016 | doi = 10.1038/npjschz.2016.12 | pmid = 27336055 | pmc = 4898894 | pages=16012}}{{cite journal | vauthors = Qi Y, Dhiman HK, Bhola N, Budyak I, Kar S, Man D, Dutta A, Tirupula K, Carr BI, Grandis J, Bar-Joseph Z, Klein-Seetharaman J | title = Systematic prediction of human membrane receptor interactions | journal = Proteomics | volume = 9 | issue = 23 | doi = 10.1002/pmic.200900259 | pmid = 19798668 | pmc = 3076061 | pages = 5243–55 | date = December 2009}} Random Forest has been found to be most-effective machine learning method for protein interaction prediction.{{cite journal | vauthors = Qi Y, Bar-Joseph Z, Klein-Seetharaman J | title = Evaluation of different biological data and computational classification methods for use in protein interaction prediction | journal = Proteins | volume = 63 | issue = 3 | pages = 490–500 | date = May 2006 | doi=10.1002/prot.20865| pmid = 16450363 | pmc = 3250929}} Such methods have been applied for discovering protein interactions on human interactome, specifically the interactome of Membrane proteins and the interactome of Schizophrenia-associated proteins.

Some efforts have been made to extract systematically interaction networks directly from the scientific literature. Such approaches range in terms of complexity from simple co-occurrence statistics of entities that are mentioned together in the same context (e.g. sentence) to sophisticated natural language processing and machine learning methods for detecting interaction relationships.{{Cite journal | pmid = 15886388 | year = 2005 | last1 = Hoffmann | first1 = R | title = Text mining for metabolic pathways, signaling cascades, and protein networks | journal = Science Signaling | volume = 2005 | issue = 283 | pages = pe21 | last2 = Krallinger | first2 = M | last3 = Andres | first3 = E | last4 = Tamames | first4 = J | last5 = Blaschke | first5 = C | last6 = Valencia | first6 = A | s2cid = 15301069 | doi = 10.1126/stke.2832005pe21 }}

Protein interaction networks have been used to predict the function of proteins of unknown functions.{{Cite journal | last1 = Schwikowski | first1 = B. | last2 = Uetz | first2 = P. | last3 = Fields | first3 = S. | title = A network of protein–protein interactions in yeast | journal = Nature Biotechnology | volume = 18 | issue = 12 | pages = 1257–1261 | year = 2000 | doi = 10.1038/82360 | pmid = 11101803 | s2cid = 3009359 }}{{cite journal |doi=10.1093/bioinformatics/bti514 |vauthors=McDermott J, Bumgarner RE, Samudrala R |title=Functional annotation from predicted protein interaction networks|journal=Bioinformatics|volume=21 |issue=15|pages=3217–3226|year=2005|pmid=15919725|doi-access=free}} 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.{{Cite journal | last1 = Rajagopala | first1 = S. V. | last2 = Sikorski | first2 = P. | last3 = Caufield | first3 = J. H. | last4 = Tovchigrechko | first4 = A. | last5 = Uetz | first5 = P. | title = Studying protein complexes by the yeast two-hybrid system | doi = 10.1016/j.ymeth.2012.07.015 | journal = Methods | volume = 58 | issue = 4 | pages = 392–399 | year = 2012 | pmid = 22841565 | pmc =3517932 }} 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. The term hypothome has been used to denote an interactome wherein at least one of the genes or proteins is a hypothetical protein.{{cite journal |vauthors=Desler C, Zambach S, Suravajhala P, Rasmussen LJ |title=Introducing the hypothome: a way to integrate predicted proteins in interactomes |journal=International Journal of Bioinformatics Research and Applications |volume=10 |issue=6 |pages=647–52 |year=2014 |pmid=25335568 |doi=10.1504/IJBRA.2014.065247 }}

{{main|Network medicine}}

The topology of an interactome makes certain predictions how a network reacts to the perturbation (e.g. removal) of nodes (proteins) or edges (interactions).{{Cite journal| first1 = A. -L. | first2 = Z. | title = Network biology: understanding the cell's functional organization | last1 = Barab | journal = Nature Reviews Genetics | volume = 5 | issue = 2 | pages = 101–113 | year = 2004 | pmid = 14735121 | doi = 10.1038/nrg1272 | last2 = Oltvai | s2cid = 10950726 }} Such perturbations can be caused by mutations of genes, and thus their proteins, and a network reaction can manifest as a disease.{{Cite journal | last1 = Goh | first1 = K. -I. | last2 = Choi | first2 = I. -G. | doi = 10.1093/bfgp/els032 | title = Exploring the human diseasome: The human disease network | journal = Briefings in Functional Genomics | volume = 11 | issue = 6 | pages = 533–542 | year = 2012 | pmid = 23063808 | doi-access = free }} A network analysis can identify drug targets and biomarkers of diseases.{{Cite journal | pmid = 21164525 | pmc = 3140052 | year = 2011 | last1 = Barabási | first1 = A. L. | title = Network medicine: A network-based approach to human disease | journal = Nature Reviews Genetics | volume = 12 | issue = 1 | pages = 56–68 | last2 = Gulbahce | first2 = N | last3 = Loscalzo | first3 = J | doi = 10.1038/nrg2918 }}

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{{Cite journal | author = Albert-László Barabási & Zoltan N. Oltvai | title = Network biology: understanding the cell's functional organization | journal = Nature Reviews. Genetics | volume = 5 | issue = 2 | pages = 101–113 | date = February 2004 | doi = 10.1038/nrg1272 | pmid = 14735121 | s2cid = 10950726 }} where functional modules within a network indicate specialized subnetworks.{{Cite journal | doi = 10.1142/S0219720009004023 | last1 = Gao | first1 = L. | last2 = Sun | first2 = P. G. | last3 = Song | first3 = J. | title = Clustering algorithms for detecting functional modules in protein interaction networks | journal = Journal of Bioinformatics and Computational Biology | volume = 7 | issue = 1 | pages = 217–242 | year = 2009 | pmid = 19226668

}} 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.

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.{{cite journal |author=Navratil V.|title=VirHostNet: a knowledge base for the management and the analysis of proteome-wide virus-host interaction networks|journal=Nucleic Acids Res.|volume=37 |pages=D661–8|year=2009 |pmid= 18984613 |doi=10.1093/nar/gkn794 |last2=De Chassey |first2=B. |last3=Meyniel |first3=L. |last4=Delmotte |first4=S. |last5=Gautier |first5=C. |last6=Andre |first6=P. |last7=Lotteau |first7=V. |last8=Rabourdin-Combe |first8=C. |issue=Database issue |pmc=2686459|display-authors=1}} Some published virus interactomes include

Bacteriophage

• Escherichia coli bacteriophage lambda{{cite journal |author=Rajagopala SV.|title=The protein interaction map of bacteriophage lambda|journal= BMC Microbiol.|volume=11 |pages=213|year=2011 |pmid= 21943085 |doi=10.1186/1471-2180-11-213 |pmc=3224144 |last2=Casjens |first2=Sherwood |last3=Uetz |first3=Peter|display-authors=1 |doi-access=free }} {{open access}}
• Escherichia coli bacteriophage T7{{cite journal |vauthors=Bartel PL, Roecklein JA, SenGupta D, Fields S |title=A protein linkage map of Escherichia coli bacteriophage T7 |journal=Nat. Genet. |volume=12 |issue=1 |pages=72–7 |year=1996 |pmid=8528255 |doi=10.1038/ng0196-72 |s2cid=37155819 }}
• Streptococcus pneumoniae bacteriophage Dp-1{{cite journal |author=Sabri M.|title=Genome annotation and intraviral interactome for the Streptococcus pneumoniae virulent phage Dp-1|journal=J. Bacteriol.|volume=193 |pages=551–62|year=2011 |pmid= 21097633 |doi=10.1128/JB.01117-10 |pmc=3019816 |issue=2 |last2=Hauser |first2=R. |last3=Ouellette |first3=M. |last4=Liu |first4=J. |last5=Dehbi |first5=M. |last6=Moeck |first6=G. |last7=Garcia |first7=E. |last8=Titz |first8=B. |last9=Uetz |first9=P.|display-authors=1}}
• Streptococcus pneumoniae bacteriophage Cp-1{{cite journal |author=Häuser R.|title=The proteome and interactome of Streptococcus pneumoniae phage Cp-1|journal=J. Bacteriol.|volume=193 |pages=3135–8|year=2011 |pmid= 21515781 |doi=10.1128/JB.01481-10 |pmc=3133188 |issue=12 |last2=Sabri |first2=M. |last3=Moineau |first3=S. |last4=Uetz |first4=P.|display-authors=1}}

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.

Human (mammalian) viruses

• Human varicella zoster virus (VZV){{cite journal|last=Stellberger|first=T.|display-authors=etal|title=Improving the yeast two-hybrid system with permutated fusions proteins: the Varicella Zoster Virus interactome|journal=Proteome Sci.|year=2010|pmid=20205919|doi=10.1186/1477-5956-8-8|pmc=2832230|volume=8|pages=8 |doi-access=free }}
• Chandipura virus{{Cite journal | last1 = Kumar | first1 = K. | last2 = Rana | first2 = J. | last3 = Sreejith | first3 = R. | last4 = Gabrani | first4 = R. | last5 = Sharma | first5 = S. K. | last6 = Gupta | first6 = A. | last7 = Chaudhary | first7 = V. K. | last8 = Gupta | first8 = S. | doi = 10.1007/s00705-012-1389-5 | title = Intraviral protein interactions of Chandipura virus | journal = Archives of Virology | volume = 157 | issue = 10 | pages = 1949–1957 | year = 2012 | pmid = 22763614 | s2cid = 17714252 }}
• Epstein-Barr virus (EBV)
• Hepatitis C virus (HPC),{{Cite journal | pmid = 24797426 | year = 2014 | last1 = Hagen | first1 = N | title = The intra viral protein interaction network of hepatitis C virus | journal = Molecular & Cellular Proteomics | last2 = Bayer | first2 = K | last3 = Roesch | first3 = K | last4 = Schindler | first4 = M | doi = 10.1074/mcp.M113.036301 | volume=13 | issue=7 | pages=1676–89 | doi-access = free | pmc = 4083108 }} Human-HCV interactions{{cite journal | last1 = Han | first1 = Y | last2 = Niu | first2 = J | last3 = Wang | first3 = D | last4 = Li | first4 = Y | year = 2016 | title = Hepatitis C Virus Protein Interaction Network Analysis Based on Hepatocellular Carcinoma | journal = PLOS ONE | volume = 11 | issue = 4| page = e0153882 | doi = 10.1371/journal.pone.0153882| pmid = 27115606 | pmc = 4846009 | bibcode = 2016PLoSO..1153882H | doi-access = free }}
• Hepatitis E virus (HEV){{cite journal |vauthors=Osterman A, Stellberger T, Gebhardt A, Kurz M, Friedel CC, Uetz P, Nitschko H, Baiker A, Vizoso-Pinto MG |title=The Hepatitis E virus intraviral interactome |journal=Sci Rep |volume=5 |pages=13872 |year=2015 |pmid=26463011 |pmc=4604457 |doi=10.1038/srep13872 |bibcode=2015NatSR...513872O }}
• Herpes simplex virus 1 (HSV-1)
• Kaposi's sarcoma-associated herpesvirus (KSHV){{cite journal|last=Fossum|first=E|display-authors=etal|title=Evolutionarily conserved herpesviral protein interaction networks|journal=PLOS Pathog |year=2009|pmid=19730696|doi=10.1371/journal.ppat.1000570|pmc=2731838|volume=5|issue=9|pages=e1000570|editor1-last=Sun|editor1-first=Ren|doi-access=free}} {{open access}}
• Murine cytomegalovirus (mCMV)

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.{{Cite journal

| last1 = Chen | first1 = Y. C.

| last2 = Rajagopala | first2 = S. V.

| last3 = Stellberger | first3 = T.

| last4 = Uetz | first4 = P.

| title = Exhaustive benchmarking of the yeast two-hybrid system

| doi = 10.1038/nmeth0910-667

| journal = Nature Methods

| volume = 7

| issue = 9

| pages = 667–668; author 668 668

| year = 2010

| pmid = 20805792

| s2cid = 35834541

| pmc = 10332476

}} Among the published bacterial interactomes (including partial ones) are

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).

Note that numerous additional interactomes have been predicted using computational methods (see section above).

There have been several efforts to map eukaryotic interactomes through HTP methods. While no biological interactomes have been fully characterized, over 90% of proteins in Saccharomyces cerevisiae have been screened and their interactions characterized, making it the best-characterized interactome.{{Cite journal | last1 = Uetz | first1 = P. | last2 = Giot | first2 = L. | last3 = Cagney | first3 = G. | last4 = Mansfield | first4 = T. A. | last5 = Judson | first5 = R. S. | last6 = Knight | first6 = J. R. | last7 = Lockshon | first7 = D. | last8 = Narayan | first8 = V. | title = A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae | journal = Nature | volume = 403 | issue = 6770 | pages = 623–627 | doi = 10.1038/35001009 | year = 2000 | pmid = 10688190 | bibcode = 2000Natur.403..623U | s2cid = 4352495 }}{{cite journal |last =Krogan |first= NJ |display-authors=etal | year=2006|title = Global landscape of protein complexes in the yeast Saccharomyeses Cerivisiae | journal = Nature| volume= 440 |pages = 637–643|doi = 10.1038/nature04670 |pmid =16554755|issue =7084|bibcode=2006Natur.440..637K|s2cid= 72422 }} Species whose interactomes have been studied in some detail include

• Schizosaccharomyces pombe{{cite journal |vauthors=Pancaldi V, Saraç OS, Rallis C, McLean JR, Převorovský M, Gould K, Beyer A, Bähler J |title=Predicting the fission yeast protein interaction network |journal=G3: Genes, Genomes, Genetics |volume=2 |issue=4 |pages=453–67 |year=2012 |pmid=22540037 |pmc=3337474 |doi=10.1534/g3.111.001560 }}{{cite journal | last1 = Vo | first1 = T.V. | display-authors = etal |year = 2016 | title = A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human | journal = Cell | volume = 164 | issue = 1–2| pages = 310–323 | doi = 10.1016/j.cell.2015.11.037 | pmid = 26771498 | pmc = 4715267}}
• Caenorhabditis elegans
• Drosophila melanogaster
• Homo sapiens

Recently, the pathogen-host interactomes of Hepatitis C Virus/Human (2008),{{cite journal |author=de Chassey B |title=Hepatitis C virus infection protein network |journal=Molecular Systems Biology |date=2008-11-04 |volume=4 |issue= 4 |page=230 |pmid=18985028 |pmc=2600670 |doi=10.1038/msb.2008.66|author2= Navratil V |author3= Tafforeau L |display-authors=etal}} Epstein Barr virus/Human (2008), Influenza virus/Human (2009) were delineated through HTP to identify essential molecular components for pathogens and for their host's immune system.{{cite journal |author=Navratil V |title=Systems-level comparison of protein–protein interactions between viruses and the human type I interferon system network|journal=Journal of Proteome Research |volume=9 |issue=7 |pages=3527–36 |date=2010-11-05|pmid=20459142 |doi=10.1021/pr100326j|author2= de Chassey B|display-authors=etal}}

As described above, PPIs and thus whole interactomes can be predicted. While the reliability of these predictions is debatable, they are providing hypotheses that can be tested experimentally. Interactomes have been predicted for a number of species, e.g.

• Human (Homo sapiens){{cite journal |vauthors=Brown KR, Jurisica I |title=Online predicted human interaction database |journal=Bioinformatics |volume=21 |issue=9 |pages=2076–82 |year=2005 |pmid=15657099 |doi=10.1093/bioinformatics/bti273 |doi-access=free }}
• Rice (Oryza sativa){{cite journal |vauthors=Gu H, Zhu P, Jiao Y, Meng Y, Chen M |title=PRIN: a predicted rice interactome network |journal=BMC Bioinformatics |volume=12 |pages=161 |year=2011 |pmid=21575196 |pmc=3118165 |doi=10.1186/1471-2105-12-161 |doi-access=free }}
• Xanthomonas oryzae{{cite journal |vauthors=Guo J, Li H, Chang JW, Lei Y, Li S, Chen LL |title=Prediction and characterization of protein–protein interaction network in Xanthomonas oryzae pv. oryzae PXO99 A |journal=Res. Microbiol. |volume=164 |issue=10 |pages=1035–44 |year=2013 |pmid=24113387 |doi=10.1016/j.resmic.2013.09.001 |doi-access=free }}
• Arabidopsis thaliana{{cite journal |vauthors=Geisler-Lee J, O'Toole N, Ammar R, Provart NJ, Millar AH, Geisler M |title=A predicted interactome for Arabidopsis |journal=Plant Physiol. |volume=145 |issue=2 |pages=317–29 |year=2007 |pmid=17675552 |pmc=2048726 |doi=10.1104/pp.107.103465 }}
• Tomato (Solanum lycopersicum){{Cite journal|last1=Yue|first1=Junyang|last2=Xu|first2=Wei|last3=Ban|first3=Rongjun|last4=Huang|first4=Shengxiong|last5=Miao|first5=Min|last6=Tang|first6=Xiaofeng|last7=Liu|first7=Guoqing|last8=Liu|first8=Yongsheng|date=2016-01-01|title=PTIR: Predicted Tomato Interactome Resource|journal=Scientific Reports|volume=6|pages=25047|doi=10.1038/srep25047|issn=2045-2322|pmid=27121261|pmc=4848565|bibcode=2016NatSR...625047Y}}
• Field mustard (Brassica rapa){{Cite journal|last1=Yang|first1=Jianhua|last2=Osman|first2=Kim|last3=Iqbal|first3=Mudassar|last4=Stekel|first4=Dov J.|last5=Luo|first5=Zewei|last6=Armstrong|first6=Susan J.|last7=Franklin|first7=F. Chris H.|date=2012-01-01|title=Inferring the Brassica rapa Interactome Using Protein–Protein Interaction Data from Arabidopsis thaliana|journal=Frontiers in Plant Science|volume=3|pages=297|doi=10.3389/fpls.2012.00297|issn=1664-462X|pmc=3537189|pmid=23293649|doi-access=free}}
• Maize, corn (Zea mays){{Cite journal|last1=Zhu|first1=Guanghui|last2=Wu|first2=Aibo|last3=Xu|first3=Xin-Jian|last4=Xiao|first4=Pei-Pei|last5=Lu|first5=Le|last6=Liu|first6=Jingdong|last7=Cao|first7=Yongwei|last8=Chen|first8=Luonan|last9=Wu|first9=Jun|date=2016-02-01|title=PPIM: A Protein–Protein Interaction Database for Maize|journal=Plant Physiology|volume=170|issue=2|pages=618–626|doi=10.1104/pp.15.01821|issn=1532-2548|pmc=4734591|pmid=26620522}}
• Poplar (Populus trichocarpa){{Cite journal|last1=Rodgers-Melnick|first1=Eli|last2=Culp|first2=Mark|last3=DiFazio|first3=Stephen P.|date=2013-01-01|title=Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS|journal=BMC Genomics|volume=14|pages=608|doi=10.1186/1471-2164-14-608|issn=1471-2164|pmc=3848842|pmid=24015873 |doi-access=free }}
• SARS-CoV-2{{cite journal |vauthors=Guzzi PH, Mercatelli D, Ceraolo C, Giorgi FM |title=Master Regulator Analysis of the SARS-CoV-2/Human Interactome |journal=Journal of Clinical Medicine |volume=9 |issue=4 |pages=982–988 |year=2020 |pmid=32244779 |doi=10.3390/jcm9040982 |pmc=7230814 |doi-access=free }}

File:Figure1 interactome highres.tif

Protein interaction networks can be analyzed with the same tool as other networks. In fact, they share many properties with biological or social networks. Some of the main characteristics are as follows.

File:The protein interaction network of Treponema pallidum.png protein interactome.]]

The degree distribution describes the number of proteins that have a certain number of connections. Most protein interaction networks show a scale-free (power law) degree distribution where the connectivity distribution P(k) ~ k^−γ with k being the degree. This relationship can also be seen as a straight line on a log-log plot since, the above equation is equal to log(P(k)) ~ —y•log(k). One characteristic of such distributions is that there are many proteins with few interactions and few proteins that have many interactions, the latter being called "hubs".

Highly connected nodes (proteins) are called hubs. Han et al.{{Cite journal | pmid = 15190252 | year = 2004 | last1 = Han | first1 = J. D. | title = Evidence for dynamically organized modularity in the yeast protein–protein interaction network | journal = Nature | volume = 430 | issue = 6995 | pages = 88–93 | last2 = Bertin | first2 = N | last3 = Hao | first3 = T | last4 = Goldberg | first4 = D. S. | last5 = Berriz | first5 = G. F. | last6 = Zhang | first6 = L. V. | last7 = Dupuy | first7 = D | last8 = Walhout | first8 = A. J. | last9 = Cusick | first9 = M. E. | last10 = Roth | first10 = F. P. | last11 = Vidal | first11 = M | doi = 10.1038/nature02555 | bibcode = 2004Natur.430...88H | s2cid = 4426721 }} have coined the term "party hub" for hubs whose expression is correlated with its interaction partners. Party hubs also connect proteins within functional modules such as protein complexes. In contrast, "date hubs" do not exhibit such a correlation and appear to connect different functional modules. Party hubs are found predominantly in AP/MS data sets, whereas date hubs are found predominantly in binary interactome network maps.{{Cite journal | pmid = 18719252 | year = 2008 | last1 = Yu | first1 = H | title = High-quality binary protein interaction map of the yeast interactome network | journal = Science | volume = 322 | issue = 5898 | pages = 104–10 | last2 = Braun | first2 = P | last3 = Yildirim | first3 = M. A. | last4 = Lemmens | first4 = I | last5 = Venkatesan | first5 = K | last6 = Sahalie | first6 = J | last7 = Hirozane-Kishikawa | first7 = T | last8 = Gebreab | first8 = F | last9 = Li | first9 = N | last10 = Simonis | first10 = N | last11 = Hao | first11 = T | last12 = Rual | first12 = J. F. | last13 = Dricot | first13 = A | last14 = Vazquez | first14 = A | last15 = Murray | first15 = R. R. | last16 = Simon | first16 = C | last17 = Tardivo | first17 = L | last18 = Tam | first18 = S | last19 = Svrzikapa | first19 = N | last20 = Fan | first20 = C | last21 = De Smet | first21 = A. S. | last22 = Motyl | first22 = A | last23 = Hudson | first23 = M. E. | last24 = Park | first24 = J | last25 = Xin | first25 = X | last26 = Cusick | first26 = M. E. | last27 = Moore | first27 = T | last28 = Boone | first28 = C | last29 = Snyder | first29 = M | last30 = Roth | first30 = F. P. | doi = 10.1126/science.1158684 | pmc = 2746753 | bibcode = 2008Sci...322..104Y }} Note that the validity of the date hub/party hub distinction was disputed.{{Cite journal | pmid = 16984220 | year = 2006 | last1 = Batada | first1 = N. N. | title = Stratus not altocumulus: A new view of the yeast protein interaction network | journal = PLOS Biology | volume = 4 | issue = 10 | pages = e317 | last2 = Reguly | first2 = T | last3 = Breitkreutz | first3 = A | last4 = Boucher | first4 = L | last5 = Breitkreutz | first5 = B. J. | last6 = Hurst | first6 = L. D. | last7 = Tyers | first7 = M | doi = 10.1371/journal.pbio.0040317 | pmc = 1569888 | doi-access = free }}{{Cite journal | pmid = 17564493 | year = 2007 | last1 = Bertin | first1 = N | title = Confirmation of organized modularity in the yeast interactome | journal = PLOS Biology | volume = 5 | issue = 6 | pages = e153 | last2 = Simonis | first2 = N | last3 = Dupuy | first3 = D | last4 = Cusick | first4 = M. E. | last5 = Han | first5 = J. D. | last6 = Fraser | first6 = H. B. | last7 = Roth | first7 = F. P. | last8 = Vidal | first8 = M | doi = 10.1371/journal.pbio.0050153 | pmc = 1892830 | doi-access = free }} Party hubs generally consist of multi-interface proteins whereas date hubs are more frequently single-interaction interface proteins.{{Cite journal | pmid = 17185604 | year = 2006 | last1 = Kim | first1 = P. M. | title = Relating three-dimensional structures to protein networks provides evolutionary insights | journal = Science | volume = 314 | issue = 5807 | pages = 1938–41 | last2 = Lu | first2 = L. J. | last3 = Xia | first3 = Y | last4 = Gerstein | first4 = M. B. | s2cid = 2489619 | doi = 10.1126/science.1136174 | bibcode = 2006Sci...314.1938K }} Consistent with a role for date-hubs in connecting different processes, in yeast the number of binary interactions of a given protein is correlated to the number of phenotypes observed for the corresponding mutant gene in different physiological conditions.

Nodes involved in the same biochemical process are highly interconnected.

The evolution of interactome complexity is delineated in a study published in Nature.{{cite journal | last =Fernandez | first=A |author2=M Lynch| year = 2011| title = Non-adaptive origins of interactome complexity | journal= Nature | volume = 474 |pages = 502–505| doi = 10.1038/nature09992 |pmid =21593762 | issue=7352 | pmc=3121905}} In this study it is first noted that the boundaries between prokaryotes, unicellular eukaryotes and multicellular eukaryotes are accompanied by orders-of-magnitude reductions in effective population size, with concurrent amplifications of the effects of random genetic drift. The resultant decline in the efficiency of selection seems to be sufficient to influence a wide range of attributes at the genomic level in a nonadaptive manner. The Nature study shows that the variation in the power of random genetic drift is also capable of influencing phylogenetic diversity at the subcellular and cellular levels. Thus, population size would have to be considered as a potential determinant of the mechanistic pathways underlying long-term phenotypic evolution. In the study it is further shown that a phylogenetically broad inverse relation exists between the power of drift and the structural integrity of protein subunits. Thus, the accumulation of mildly deleterious mutations in populations of small size induces secondary selection for protein–protein interactions that stabilize key gene functions, mitigating the structural degradation promoted by inefficient selection. By this means, the complex protein architectures and interactions essential to the genesis of phenotypic diversity may initially emerge by non-adaptive mechanisms.

{{Original research|section|date=August 2015}}

Kiemer and Cesareni raise the following concerns with the state (circa 2007) of the field especially with the comparative interactomic: The experimental procedures associated with the field are error prone leading to "noisy results". This leads to 30% of all reported interactions being artifacts. In fact, two groups using the same techniques on the same organism found less than 30% interactions in common. However, some authors have argued that such non-reproducibility results from the extraordinary sensitivity of various methods to small experimental variation. For instance, identical conditions in Y2H assays result in very different interactions when different Y2H vectors are used.

Techniques may be biased, i.e. the technique determines which interactions are found. In fact, any method has built in biases, especially protein methods. Because every protein is different no method can capture the properties of each protein. For instance, most analytical methods that work fine with soluble proteins deal poorly with membrane proteins. This is also true for Y2H and AP/MS technologies.

Interactomes are not nearly complete with perhaps the exception of S. cerevisiae. This is not really a criticism as any scientific area is "incomplete" initially until the methodologies have been improved. Interactomics in 2015 is where genome sequencing was in the late 1990s, given that only a few interactome datasets are available (see table above).

While genomes are stable, interactomes may vary between tissues, cell types, and developmental stages. Again, this is not a criticism, but rather a description of the challenges in the field.

It is difficult to match evolutionarily related proteins in distantly related species. While homologous DNA sequences can be found relatively easily, it is much more difficult to predict homologous interactions ("interologs") because the homologs of two interacting proteins do not need to interact. For instance, even within a proteome two proteins may interact but their paralogs may not.

Each protein–protein interactome may represent only a partial sample of potential interactions, even when a supposedly definitive version is published in a scientific journal. Additional factors may have roles in protein interactions that have yet to be incorporated in interactomes. The binding strength of the various protein interactors, microenvironmental factors, sensitivity to various procedures, and the physiological state of the cell all impact protein–protein interactions, yet are usually not accounted for in interactome studies.{{cite journal|last1=Welch|first1=G. Rickey|title=The 'fuzzy' interactome|journal=Trends in Biochemical Sciences|date=January 2009|volume=34|issue=1|pages=1–2|doi=10.1016/j.tibs.2008.10.007|pmid=19028099}}

• Bioinformatics, Omics, Proteomics, Genomics
• BioPlex
• Connectome
• Glossary of graph theory
• Human interactome
• List of omics topics in biology
• Mathematical biology
• Metabolic network
• Metabolic network modelling
• Metabolic pathway
• Network medicine

{{Reflist|30em}}

• {{cite journal | doi = 10.1006/jmbi.2001.4526 |vauthors=Park J, Lappe M, Teichmann SA |date=Mar 2001 | title = Mapping protein family interactions: intramolecular and intermolecular protein family interaction repertoires in the PDB and yeast | journal = J Mol Biol | volume = 307 | issue = 3| pages = 929–38 | pmid = 11273711}}

• [https://web.archive.org/web/20111004173202/http://protinfo.compbio.washington.edu/ppc/ Protinfo PPC] predicts the atomic 3D structure of protein protein complexes.{{cite journal |vauthors=Kittichotirat W, Guerquin M, Bumgarner R, Samudrala R |title=Protinfo PPC: A web server for atomic level prediction of protein complexes|journal=Nucleic Acids Research|volume=37|pages=W519–W525|year=2009|doi=10.1093/nar/gkp306|pmid=19420059|pmc=2703994 |issue=Web Server issue}}
• IBIS (server) reports, predicts and integrates multiple types of conserved interactions for proteins.

• [http://gpsprot.org GPS-Prot] {{Webarchive|url=https://web.archive.org/web/20230412221334/http://gpsprot.org/ |date=2023-04-12 }} Web-based data visualization for protein interactions
• PINV - [http://biosual.cbio.uct.ac.za/pinv.html Protein Interaction Network Visualizer] {{Webarchive|url=https://web.archive.org/web/20221203160134/http://biosual.cbio.uct.ac.za/pinv.html |date=2022-12-03 }}

• [http://www.thebiogrid.org/ BioGRID] database
• [http://mentha.uniroma2.it mentha] the interactome browser {{cite journal | last1 = Calderone |display-authors=etal | year = 2013 | title = mentha: a resource for browsing integrated protein-interaction networks | journal = Nature Methods | volume = 10 | issue = 8| pages = 690–691 | doi = 10.1038/nmeth.2561 | pmid = 23900247|s2cid=9733108 |url=https://zenodo.org/record/924817 }}
• [http://www.ebi.ac.uk/intact IntAct: The Molecular Interaction Database]
• [http://interactome.org Interactome.org] {{Webarchive|url=https://web.archive.org/web/20080820063335/http://interactome.org/ |date=2008-08-20 }} — a dedicated interactome web site.

Category:Bioinformatics

Category:Molecular biology

Category:Systems biology

Category:Protein complexes