Editing Interactome (section)

==Computational methods to study interactomes==
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 [[Yeast two-hybrid|Y2H]] screens detect only 25% or so of all interactions in an interactome.<ref name="Chen" /> 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.<ref name="Raja2009">{{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 }}</ref> 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.<ref>{{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 (journal)|Bioinformatics]] | volume = 22 | issue = 14 | pages = e402–e407 | date = July 2006 | doi = 10.1093/bioinformatics/btl258 | pmid = 16873500 | doi-access = free }}</ref>

===Predicting PPIs===
[[File:Schziophrenia PPI.jpg|400px|right|thumb | Schizophrenia PPI.<ref name="mkgnpjS"/>]]
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).<ref>{{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 }}</ref> 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]]''.<ref>{{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 }}</ref>

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 |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}}</ref><ref>{{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.}}</ref> as well as other protein–molecule interactions.<ref>{{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}}</ref><ref>{{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 }}</ref> Other algorithms use only sequence information, thereby creating unbiased complete networks of interaction with many mistakes.<ref>{{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}}
</ref>

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.<ref name="mkgnpjS">{{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}}</ref><ref name="yqProteomics">{{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}}</ref> [[Random Forest]] has been found to be most-effective machine learning method for protein interaction prediction.<ref>{{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}}</ref> Such methods have been applied for discovering protein interactions on human interactome, specifically the interactome of [[Membrane proteins]]<ref name="yqProteomics"/> and the interactome of Schizophrenia-associated proteins.<ref name="mkgnpjS"/>

===Text mining of PPIs===
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.<ref>{{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 }}</ref>

===Protein function prediction===
Protein interaction networks have been used to predict the function of proteins of unknown functions.<ref name="Schwikowski2000">{{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 }}</ref><ref name="McDermottJ2005">{{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}}</ref> 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 (biology)|translation]].<ref name="YbeB">{{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 }}</ref> 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.<ref name="Schwikowski2000"/> The term ''hypothome'' has been used to denote an interactome wherein at least one of the genes or proteins is a [[hypothetical protein]].<ref>{{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 }}</ref>

===Perturbations and disease===
{{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).<ref name="networkbio">{{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 }}</ref> Such perturbations can be caused by [[mutations]] of genes, and thus their proteins, and a network reaction can manifest as a [[disease]].<ref name="disnetwork">{{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 }}</ref> A network analysis can identify [[drug target]]s and [[biomarker]]s of diseases.<ref name="Barabasi">{{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 }}</ref>

===Network structure and topology===
Interaction networks can be analyzed using the tools of [[graph theory]]. Network properties include the [[Degree (graph theory)|degree]] distribution, [[clustering coefficient]]s, [[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 network|scale-free topology]]<ref>{{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 }}</ref> where '''functional modules''' within a network indicate specialized subnetworks.<ref name="modules">{{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
}}</ref> 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.