decades of targeted studies and recent progress in phosphoproteomics has resulted in a large body of protein phosphorylation data ( 2 ) .
determining how these phosphorylation sites change through time , for example during the cell cycle or following exposure to extracellular stimuli is now possible with techniques such as quantitative mass spectrometry ( 3 ) .
however , it remains difficult to determine which of the 518 human kinases is responsible for the phosphorylation of an observed site ; a glance at the phospho.elm database reveals that only about a quarter of known in vivo phosphorylation sites have been assigned as substrates of a specific kinase , and this fraction is constantly decreasing ( 2 ) .
this has motivated the development of numerous computational methods for predicting kinase  substrate relations , for example , scansite ( 4 ) , netphosk ( 5,6 ) , predikin ( 7 ) , predphospho ( 8) gps ( 9 ) , ppsp ( 10 ) and kinasephos ( 11 ) .
these methods all rely on consensus sequence motifs recognized by the active site of the enzymes , represented by either position - specific scoring matrices ( pssms ) , neural networks , support vector machines or other machine - learning representations .
however , kinase specificity is known to also depend on other factors , such as auxiliary protein interactions , scaffolds , coexpression and colocalization ( collectively referred to as 
we recently introduced a computational framework , networkin , which uses a probabilistic protein association network [ string ( 12 ) ] to model the context of kinases and substrates ; combined with consensus sequence motifs , this gave a 2.5-fold leap in prediction accuracy over previous methods ( 13 ) . here , we present a database of predicted kinase 
substrate relations based on the latest human phosphoproteome and protein association network from the phospho.elm ( 2 ) , phosphosite ( 14 ) and string ( 12 ) databases .
this database is available via a web interface at http://networkin.info , which enables the user to query the database for any kinases or substrates of interest , to submit new substrates and to explore the evidence underlying a prediction .
the foundation of the networkin algorithm is the fact that signalling proteins are modular in nature , that is they consist of discrete functional modules , such as protein kinase domains and the linear peptide motifs they recognize and phosphorylate .
this makes it possible to model the behaviour of such proteins by coupling the prediction of linear motifs to that of identifying the corresponding binding module in a network context . due to the improved capability of mass spectrometry to identify phosphorylation sites and other post - translational modifications ,
the scope of modelling these events has changed from predicting what could get phosphorylated to predicting what kinase phosphorylates which sites .
the networkin algorithm is designed to work from a set of experimentally identified in vivo phosphorylation sites ( although the algorithm can also be used ab initio ) .
the precomputed results in the database are based on the latest human phosphoproteome from the phospho.elm and phosphosite databases ( 2,14 ) ( figure 1 ) .
the release cycle of the database is approximately every 3 months due to the high throughput of mass - spectrometry - driven proteomics , and we intend to keep networkin up - to - date with future releases of phospho.elm and phosphosite . 

networkin starts from a set of known in vivo phosphorylation sites , which are obtained and kept synchronized with the phospho.elm and phosphosite databases to facilitate reciprocal database cross - links .
we first compare these phosphorylation sites to consensus sequence motifs from the scansite and netphosk resources in order to predict possible kinase families responsible for the phosphorylation .
second , we capture the kinase and substrate context using a probabilistic functional association network from the string database ( 12 ) .
substrate interaction network is stored in a sql database , which is accessible via a web interface .
networkin starts from a set of known in vivo phosphorylation sites , which are obtained and kept synchronized with the phospho.elm and phosphosite databases to facilitate reciprocal database cross - links .
we first compare these phosphorylation sites to consensus sequence motifs from the scansite and netphosk resources in order to predict possible kinase families responsible for the phosphorylation .
second , we capture the kinase and substrate context using a probabilistic functional association network from the string database ( 12 ) .
substrate interaction network is stored in a sql database , which is accessible via a web interface .
these data are processed through the networkin algorithm , which is implemented in python and c. the sites are classified by matching them to a motif collection ( figure 1 ) based on the position - specific scoring matrices from scansite ( 4 ) ( http://scansite.mit.edu ) and the artificial neural networks from netphosk ( 5 ) ( http://www.cbs.dtu.dk/services/netphosk ) .
each consensus sequence motif is considered to be a representative for a family of closely related kinases ; for example , the netphosk cdk5 predictor is used for predicting possible phosphorylation sites for all cyclin - dependent kinases . within a proteome ,
kinases are identified and assigned to these families based on their best hit in a blastp ( 15 ) sequence similarity search against a set of 82 representative kinase domain sequences , which have been manually assigned to families . only hits with an e - value better than 10 and with at least 50% sequence identity
are considered . to capture the biological context of a substrate , we use a probabilistic network of functional associations extracted from the string database ( 12 ) ( http://string.embl.de , 
 figure 1 ) .
this network is based on four fundamentally different types of evidence : genomic context ( gene fusion , gene neighbourhood and phylogentic profiles ) , primary experimental evidence ( physical protein interactions and gene co - expression ) , manually curated pathway databases , and automatic literature mining .
we showed that the three latter evidence types are of comparable importance , whereas genomic context methods contribute very little towards the predictions made by networkin ( 13 ) .
as the curated pathway databases generally contain few errors , a confidence score of 0.9 is assigned to this type of evidence .
the best candidate kinases within the appropriate kinase families are identified from a protein network of functional associations [ generated using the string database ( 12 ) ] by calculating the proximity to the substrate for all kinases , defined as the probability of the most probable path connecting them ( floyd  warshall algorithm ) .
the context is thus used as a filter that eliminates many of the false - positive predictions obtained from the sequence motifs and hence improves the prediction accuracy . however , the current algorithm is unable to recover sites that are missed by the sequence motifs ( i.e. false - negative predictions ) .
the database also contains cross - references to the phospho.elm ( 2 ) , phosphosite ( 14 ) and string ( 12 ) databases .
this database can be accessed via a web interface , which consists of a collection of cgi scripts , that query the database backend and format the results as xhtml for display in a web browser .
, we will explain the various features of the web interface , using the tumour suppressor 53bp1 as an example . for large - scale analysis or visualization
, most users will probably prefer to download the complete set of predictions for human phosphoproteins , which is available in tab - separated and cytoscape format .
for all other users , the primary entry point to networkin is its search interface shown in figure 2a .
the user can select a specific substrate and/or kinase to view the corresponding subset of predictions ; in our example , we query for 53bp1 as the substrate and use the wildcard * to obtain predictions for all kinases .
the web interface also offers an advanced search form , which enables the user to pose much more refined queries . in either case
, the search results will be presented as a table in which each row shows a predicted relation between a kinase and a specific phosphorylation site in a substrate . in case of 53bp1 , we get a list of 78 predictions for 39 sites and 12 kinases ; the first 10 of these predictions are shown in figure 2b .
for each prediction we list two scores , namely the context score and the motif score , both of which should preferably be high .
it should be noted that the motif scores for different kinase families are not comparable ; in particular , motif scores from netphosk should not be compared with motif scores from scansite . for this reason ,
as the results of a single query may be extensive , the results can also be downloaded in the formats mentioned previously . 

, this protein is chosen as the substrate protein to query the database for predictions relating any kinase to specific phosphorylation sites within 53bp1 ( a ) .
the system returns a total of 78 relations involving 12 different kinases that are predicted to phosphorylated 39 different sites , which are presented in a tabular view ( b ) , ( only the first 10 predictions are shown ) .
these predictions can now be investigated in further detail by following either the links to the phospho.elm and phosphosite databases for curated knowledge related to the sites ( c ) , or by following the links to the string network viewer to visualize the most probable path in the protein assocation network , which connects the kinase and the substrate ( d ) .
a researcher is interested in the 53bp1 tumour surpressor . from the homepage of networkin , this protein is chosen as the substrate protein to query the database for predictions relating any kinase to specific phosphorylation sites within 53bp1 ( a ) .
the system returns a total of 78 relations involving 12 different kinases that are predicted to phosphorylated 39 different sites , which are presented in a tabular view ( b ) , ( only the first 10 predictions are shown ) .
these predictions can now be investigated in further detail by following either the links to the phospho.elm and phosphosite databases for curated knowledge related to the sites ( c ) , or by following the links to the string network viewer to visualize the most probable path in the protein assocation network , which connects the kinase and the substrate ( d ) .
furthermore , the user can investigate the predictions in greater detail via the web interface .
for each substrate , we link to phospho.elm or phosphosite where the user can find manually curated information on in vivo phosphorylation sites including , when known , the kinase(s ) involved ( figure 2c ) . to allow the user to investigate how a specific prediction was made by networkin
, we provide a link to the string network viewer , in which the most probable path connecting the kinase and the substrate will be highlighted ( figure 2d ) .
alternatively , the user can select multiple predictions and display the network context for all the proteins involved . from the network viewer ,
this ability to thoroughly investigate individual predictions is particularly useful for interpreting non - obvious cases , which are often based on indirect links between the kinase and the substrate .
although phospho.elm , phosphosite and hence networkin are kept up - to - date with new published phosphorylation sites , many researchers will be interested in predictions for their own , unpublished sites .
we thus allow users to submit protein sequences and a corresponding set of phosphorylation sites for analysis ; although possible , we discourage submitting sequences without prior knowledge on phosphorylation .
after uploading the data , the user will be presented with a confirmation page where potential data entry errors can be detected and fixed .
the final predictions will be presented in a tabular format similar to the one used when querying the precomputed results in the database .
many users are interested in specific kinases or substrates ; however , others may want to get an overview of the complete phosphorylation network . to facilitate this ,
all kinases and substrates are shown using a colour scale to signify their connectivity , namely the number of substrates for a given kinase or the number of kinases for a given substrate . by selecting one or more kinases ,
deselecting one kinase will deselect only the substrates specific for that kinase , keeping the other ones .
we find this approach to be an intuitive way to gain insight into pleiotropic properties of kinases .
similar to the search interface , map selections can be visualized in their network context .
in the future , we intend to keep networkin up - to - date with the latest data on phosphorylation sites from phospho.elm and phosphosite , functional associations from string and consensus sequence motifs from scansite and other sources .
furthermore , the algorithm will be extended to take into account docking motifs ( e.g. for map kinases ) and phosphorylation - dependent binding modules ( e.g. sh2 , ptb and brct domains ) , which is expected to both improve the prediction accuracy and facilitate more comprehensive modelling of signalling networks .
we also intend to extend the method to include phosphatases as soon as data for this is available to us .
although , networkin is so far specifically aimed at protein phosphorylation , many other post - translational modifications are mediated by enzymes that recognize short linear motifs .
for example , the modifications of histone tails through acetylation , methylation and phosphorylation has been shown to be context dependent ( 16 ) , and acetylated or methylated sites in turn bind interaction domains , such as bromo- or chromodomains .
extending the resource to cover also other post - translational modifications is thus a long - term goal .
 protein kinases control cellular responses by phosphorylating specific substrates . 
 recent proteome - wide mapping of protein phosphorylation sites by mass spectrometry has discovered thousands of in vivo sites . systematically assigning all 518 human kinases to all these sites 
 is a challenging problem . 
 the networkin database ( http://networkin.info ) integrates consensus substrate motifs with context modelling for improved prediction of cellular kinase  substrate relations . based on the latest human phosphoproteome from the phospho.elm and phosphosite databases , 
 the resource offers insight into phosphorylation - modulated interaction networks . here 
 , we describe how networkin can be used for both global and targeted molecular studies . via 
 the web interface users can query the database of precomputed kinase  
 substrate relations or obtain predictions on novel phosphoproteins . 
 the database currently contains a predicted phosphorylation network with 20 224 site - specific interactions involving 3978 phosphoproteins and 73 human kinases from 20 families . 
INTRODUCTION
RESOURCE OVERVIEW
USING NETWORKIN
OUTLOOK