Editing Interactome (section)

===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"/>