different experimental techniques have been developed to measure physical interactions between proteins ; these methods vary considerably , not least in terms of the data they produce . to give some examples , two widely used methods adapted for high - throughput approaches are the yeast two - hybrid ( y2h ) system   and affinity purification followed by mass spectrometry ( ap - ms ) . the y2h system assays whether two proteins physically interact with each other ( figure 1 ) . genetically modified yeast strains are used to express a ' bait ' and a ' prey ' protein , which , if they interact , trigger the expression of a reporter gene . the method has been used for large - scale screening studies of a variety of model organisms , including yeast , fly and humans . the yeast two - hybrid experiment . the yeast two - hybrid system utilises the dna binding domain and the activation domain of a yeast transcription factor . the bait proteins a and c are fused to a dna binding domain , allowing them to bind to a binding site in the promoter region of a reporter gene . the prey protein b is fused to an activation domain that can activate the expression of a gene . ( a ) protein b does not bind to protein a , therefore , the reporter gene is not activated . ( b ) protein b binds to protein c , thereby activating the reporter gene . in an ap - ms experiment , a protein of interest is fused to a protein fragment ( the ' tag ' ) , which allows its purification ( figure 2 ) . this modified or tagged protein is expressed and purified from the cell extract using the tag -- for example , by antibodies binding specifically to the tag . the most widely used variation of the ap - ms method is tandem affinity purification followed by mass spectrometry ( tap - ms ) . in tap - ms , the protein of interest is attached to a larger protein tag , which allows two consecutive affinity purification steps . large - scale tap - ms experiments have been performed for yeast and human proteins [ 3 - 5 ] . currently , several variations of these two methods , as well as a number of other methods , are used to identify protein -- protein interactions ( ppis ) [ 6 - 8 ] . an affinity purification experiment followed by mass spectrometry . the protein of interest , f ( red circle ) , is fused to a protein fragment -- the ' tag ' ( red rectangle ) . proteins binding to the tagged protein ( blue ) are co - purified , whereas proteins not binding to protein f ( yellow ) are discarded . the purified proteins can be released using enzymatic cleavage ( scissors ) or other methods , depending on the nature of the tag . for example , if the interaction between two proteins is detected by a y2h experiment , we represent this physical interaction by an undirected connection between the two nodes . in a more detailed representation , we could make a distinction between bait and prey proteins and use a directed connection to represent the interaction between two proteins , using an arrow pointing from bait to prey . the use of graphs to describe the experimental results of ap - ms protein interaction screens is not always as straightforward as for y2h data . due to the nature of an ap - ms experiment , which identifies a whole protein complex rather than pairwise interactions , its results can be represented as a graph , using either the matrix or the spokes model ( figure 3 ) . the matrix model assumes that all proteins of a purified complex interact ; therefore , in the graph each protein is connected to each other . the spokes model assumes no additional interactions between proteins in a complex other than between the tagged protein and each co - purified protein . spokes and matrix models . in order to represent the complexes identified by the ap - ms methods as binary pairs , the spokes model ( a ) or the matrix model ( b ) can be used . in the spokes model , it is assumed that only the tagged protein interacts with the other proteins in the complex . in the matrix model , it is assumed that all proteins in a complex interact with each other . while the spokes model probably leads to an underestimate of the real interactions , the matrix model leads to an overestimate . many graph analysis algorithms have been applied to ppi datasets ; these approaches have been reviewed in detail elsewhere [ 11 - 16 ] . several public databases collect published ppi data and provide researchers access to their curated datasets . these usually reference the original publication and the experimental method that determined every individual interaction . database designers choose to represent these data in different ways , and the wide spectrum of experimental methods makes it difficult to design a single data model to capture all necessary experimental detail . to overcome this problem , the international molecular exchange ( imex ; http://imex.sourceforge.net/ ) consortium was formed . imex aims to enable the exchange of data and to avoid the duplication of the curation effort . to that end , an xml - based proteomics standard , termed the proteomics standards initiative - molecular interaction ( psi - mi ) has been developed . at the time of writing , however , no data had yet been exchanged , and it was therefore necessary to combine ppi data from all available databases using the authors ' own scripts to obtain as comprehensive a network as possible . here , the focus is on six databases : the biological general repository for interaction datasets ( biogrid ) , the molecular interaction database ( mint ) , the biomolecular interaction network database ( bind ) , the database of interacting proteins ( dip ) , the intact molecular interaction database ( intact ) and the human protein reference database ( hprd ) ( see table 1 ) . dip , intact and mint are active members of the imex initiative ; the curation accuracy of these three databases was assessed recently by cusick et al . hprd focuses entirely on human proteins , providing not only information on protein interactions , but also a variety of protein - specific information , such as post - translational modifications , disease associations and enzyme - substrate relationships . one of the first interaction databases , bind , initiated in 2001 by the university of toronto and the university of british columbia , is part of the biomolecular object network databank ( bond ) and was subsequently acquired by the company thomson reuters . the following comparison is based on complete sets of binary interactions that were downloaded from the individual databases in may 2008 . intact and mint derive binary interactions from protein complexes using the spokes model . no other database provided any information on which model is applied . only ' physical interactions ' are considered here , although most databases also provide ' genetic interactions ' -- that is , two non - essential genes that lead to a non - viable phenotype if they are knocked out simultaneously . furthermore , interactions were only accepted if a publication identifier was provided along with the interacting proteins . currently , the most comprehensive database in terms of individual interactions is intact , with almost 130,000 unique interactions from up to 131 different organisms . despite these large numbers , it cites only about 3,000 different publications . whereas intact seems to be concentrating on high - throughput studies , hprd also takes into account small - scale publications . although being restricted to human proteins , it reports over 36,000 unique interactions from more than 18,000 publications . only biogrid cites a similar number of publications ( 16,369 ) ; it is also the second largest database in terms of the number of unique interactions . it should be noted that the databases examine publications in different depth , and that higher numbers of publications do not necessarily involve a higher curation effort . the majority of known protein interactions account for proteins from saccharomyces cerevisiae and homo sapiens . individual high - throughput interaction screens were carried out for some other organisms ; these high - throughput studies usually account for the majority of all known interactions in the corresponding organism . by contrast , for this reason , the number of interactions for humans and yeast can vary considerably between different databases , depending on their coverage of the literature . unfortunately , this is not the case . of the 14,899 publications shared by at least two databases , 5,782 ( 39 per cent ) were reported with a different number of interactions in different databases . for example , for the publication reporting the most interactions , a minimum of 18,877 ( bind ) and a maximum of 20,800 interactions ( dip ) were reported . according to the abstract , the number of interactions is 20,405 , which , again , is different from the number reported by all five databases that cite this publication . in this case , the variation is presumably due to problems with identifier mapping . many databases use different identifiers , which do not always map in a perfect one - to - one relationship to the originally published identifiers . biogrid ( 20,220 interactions ) uses the original gene identifiers , but still lacks 185 interactions . as a second example , using a y2h screen , rual et al . the authors compared their experimental findings with a literature - curated ppi network of 4,076 interactions . hprd ( 2,371 interactions ) , intact ( 2,671 interactions ) and mint ( 2,463 interactions ) report only experimentally detected interactions for this reference . biogrid reports 6,295 interactions for this study , of which 2,594 quote y2h as the detection method . the remaining 3,895 interactions quote affinity capture as the detection method and possibly refer to the literature - curated interactions . for a number of other publications , differences can be explained by different confidence sets or thresholds   or differences in the application of the matrix or spokes model . often , no obvious reason for different numbers of interactions could be found . although many databases provide their interactions in the proteomics standards initiative - molecular interactions ( psi - mi ) format , its controlled vocabulary is often not used or is used incorrectly . furthermore , a variety of different gene or protein identifiers are used , even within some of the databases . although a gene can give rise to several different proteins ( due to alternative splicing ) , we mapped all identifiers to ensembl gene identifiers to avoid any ambiguities . only interactions in which both proteins could be mapped to an ensembl gene identifier were considered for further analysis . after unifying all identifiers for eukaryotic organisms , the four model organisms caenorhabditis elegans , drosophila melanogaster , s. cerevisiae and h. sapiens showed the highest number of interactions ( table 2 ) . the focus here has been on ppis in eukaryotes , but the reader should note that high - throughput datasets also exist for a variety of pro - karyotes , including escherichia coli , campylobacter jejuni and helicobacter pylori . previous studies reported little overlap between individual ppi datasets . likewise , there is little redundancy in the combined set of interactions ( table 2 ) . between 1 per cent ( d. melanogaster ) and 18 per cent ( h. sapiens ) of all interactions interestingly , the proportion of interactions that were reported by different methods reaches up to 25 per cent for yeast and 42 per cent for humans ( table 2 ) . although many small - scale publications apply more than one method to confirm an interaction , this number is most likely an overestimate , because databases use different nomenclature and spelling variations to describe experimental detection methods . therefore , more interactions appear to be confirmed by several methods than really are . the total number of proteins and interactions ( that could be mapped to ensembl gene identifiers ) , as well as the number of interactions reported by more than one publication , more than one method or more than one database , is shown . relative numbers were obtained through normalisation with the total number of interactions . as mentioned above , consequently , only a subset of all protein interactions can be found in more than one database ( table 2 ) . these range from 42 per cent of yeast interactions and 51 per cent of human interactions to 72 per cent of fly interactions and 86 per cent of worm interactions . to assess these differences in more detail , the relative pairwise overlap of human protein interactions between databases was calculated ( table 3 ) . all databases have their highest relative overlap when compared with hprd , which reports the most interactions . high overlaps were also found between dip and biogrid ( 55 per cent ) and between mint and intact ( 59 per cent ) . even the most abundant database ( hprd ) , however , covers only two - thirds of all reported human protein interactions . therefore , some groups have set up meta - databases that provide protein interaction data extracted and integrated from other databases . currently , one of the most comprehensive meta - database appears to be the agile protein interaction database ( apid ) . apid extracts interactions from the six databases described above , mapping all proteins to uniprot identifiers . via a web interface apid references the database from which an interaction is derived and provides the related information available in the original database , such as the detection method and the publication identifier . in addition , apid incorporates biological information from various other databases , such as the gene ontology   and pfam databases . unfortunately , a download of the complete dataset is currently not possible due to licensing issues . apid is generally in good agreement with the results of the authors ' data integration . for the time being , apid seems a good source of interactome data . several other meta - databases exist , but these usually focus on a single organism   or incorporate various other types of interactions , such as computationally predicted protein interactions and co - citation of proteins . for a comprehensive list of available databases , ppi databases not only report their data in different ways , using different ontologies , but their curators also report different ppis when examining the same publication . in addition , all databases include different publications . the pairwise overlap among databases analysed here reaches up to 75 per cent , but always falls short of a perfect 100 per cent . similar results were obtained in related studies . until a data exchange between databases is implemented , a comprehensive set of interactions can only be obtained through data integration of several databases . meta - databases , such as apid , provide access to more comprehensive datasets , but do not always allow the download of their complete data . furthermore , by their very nature , meta - databases will always be less up to date than the original databases . ppi databases have improved greatly over the past couple of years , and important issues , such as data exchange , are being currently addressed by some of the databases described here . an important step towards increasing the number and quality of protein interaction data would be to introduce a submission requirement -- as , indeed , already exists for sequence and microarray data . these data have to be submitted to public databases prior to publication in a scientific journal , which ensures data availability and consistent annotation , and enables researchers to utilise the data with greatest efficiency . the authors would like to thank all developers and curators of the protein -- protein interaction databases . without their effort , we are grateful for funding from the british council / daad as part of the arc programme ( arc1297 ) .
over the past few years , the number of known protein - protein interactions has increased substantially . to make this information more readily available , 
 a number of publicly available databases have set out to collect and store protein - protein interaction data . 
 protein - protein interactions have been retrieved from six major databases , integrated and the results compared . 
 the six databases ( the biological general repository for interaction datasets [ biogrid ] , the molecular interaction database [ mint ] , the biomolecular interaction network database [ bind ] , the database of interacting proteins [ dip ] , the intact molecular interaction database [ intact ] and the human protein reference database [ hprd ] ) differ in scope and content ; integration of all datasets is non - trivial owing to differences in data annotation . with respect to human protein - protein interaction data , hprd seems to be the most comprehensive . to obtain a complete dataset , however , interactions from all six databases have to be combined . to overcome this limitation , meta - databases such as the agile protein interaction database ( apid ) 
 offer access to integrated protein - protein interaction datasets , although these also currently have certain restrictions .