protein phosphorylation , performed by a group of enzymes known as kinases and phosphotransferases , is a post - translational modification essential to correct functioning within cells ( 1 ) . the post - translational modification of proteins by phosphorylation is the most abundant form of cellular regulation , affecting many cellular signal pathways , including metabolism , growth , differentiation and membrane transport ( 2 ) . the enzymes must be specific and act only on a defined subset of cellular targets to ensure signal fidelity . because of owing to its importance in cellular control , a computational scheme to quickly and efficiently identify phosphorylation sites in protein sequences and the catalytic kinases involved in the phosphorylation is desirable . therefore , in this work , a prediction method was designed and implemented to facilitate the identification of the phosphorylation sites and the related catalytic kinases . ( 1 ) presented several prediction methods for identifying the phosphorylation site prediction concentrating on only the substrate specificity . netphosk ( 4 ) is an artificial neural network algorithm to identify protein kinase a ( pka ) phosphorylation sites with 100% sensitivity and 40% specificity in experiments . scansite 2.0 ( 5 ) identified short protein motifs that are recognized by phosphorylation protein serine / threonine or tyrosine kinases . each motif used in the scansite was constructed from a set of experimentally validated phosphorylation sites and was represented as a position - specific scoring matrix . rather than search a protein motif of phosphorylation substrate against the target sequences based on the homolog to the motifs , the kinasephos web server developed here was based on the concept of machine learning , the same as netphos and diphos . computer models were trained for the detection of phosphorylation sites . by comparison of the prediction accuracy between the predictive computer model methods and the motif search tools , the known phosphorylation sites from data sources in public domain were categorized by their annotated protein kinases . based on the profile hidden markov model ( hmm ) , computational models were determined from the kinase - specific groups of the phosphorylation sites . a web - based prediction application was implemented to facilitate the identification of protein kinase - specific phosphorylation sites . the phosphobase ( 6 ) consists of 1883 experimentally verified phosphorylation sites within 597 protein entries . the number of serine , threonine and tyrosine sites is 984 , 246 and 653 , respectively . swiss - prot ( 7 ) ( release 45 of october 2004 ) maintains 163 500 protein entries , of which 3614 have phosphorylation annotation . among these entries , the number of serine , threonine and tyrosine sites was 1005 , 281 and 321 , respectively . generally , the serine , threonine and tyrosine , which are not annotated as phosphorylation residues , within the experimentally validated phosphorylated proteins , are selected as negative sets , i.e. the non - phosphorylated sites . therefore , two negative ( non - phosphorylated ) datasets were obtained from the phosphobase and swiss - prot based on the phosphorylation annotation . because of the absence of good negative dataset exists for non - phosphorylated sites , the residues that had not been previously annotated as phosphorylated in phosphorylation annotated proteins were chosen as a reflection of more general non - phosphorylated sites . supplementary table s1 summarizes the statistics of kinase - specific phosphorylated sites used for learning models in the proposed application . this work confirms the existence of two major protein kinases phosphorylating either at serine / threonine residues or at tyrosine residues . phosphorylated sites were first extracted as positive sets ; non - phosphorylated sites were extracted as negative sets , and the catalytic kinase annotations were obtained from phosphobase and swiss - prot . alternatively , in larger positive groups , the sequences of the phosphorylated sites can be clustered into subgroups by maximal dependence decomposition ( mdd ) ( 8) . the mdd was first applied in nucleotides and is a recursive process to divide a sequence set into tree - like subgroups based on the positional dependency of the sequences . here as the example given in figure 1 , 232 phosphorylation serine substrates are grouped into subgroups . when applying mdd to cluster the sequences of a positive set , a parameter , i.e. the minimum - cluster - size , should be set . if the size of a subgroup is less than the minimum - cluster - size , the subgroup is terminated to be divided . the mdd process terminates until all the subgroup sizes are less than the minimum - cluster - size . thereupon , the concept of the profile hmm was adopted to learn computational models from positive sets of phosphorylation sites . to evaluate the learned models , k - fold cross - validation and leave - one - out cross - validation were performed on them . after evaluating the models , the model with highest accuracy in each dataset was chosen . for each kinase - specific positive set of the phosphorylated sites , the best performed model is selected and used to identify the phosphorylation sites within the input protein sequences by hmmsearch ( 9 ) . to search the hits of a model , hmmer returns both a hmmer bit score and an expectation value ( e - value ) . a search of a model with the hmmer score greater than the threshold t is defined as a positive prediction , i.e. a hmm recognizes a phosphorylation site . the threshold t of each model is decided by maximizing the accuracy measure during a variety of cross - validations with the hmm bit score value range from 0 to 10 . for example , supplementary figure s1 depicts the optimization of the threshold of the hmm bit scores in the s_pka model . the threshold of the s_pka model is set to 4.5 to maximize the accuracy measure of the model . when considering a mdd - clustered dataset , for example , mdd - clustered pka catalytic serine ( s_pka ) , the hmms are trained separately from the subgroups of the phosphorylated sites resulted by mdd . each model is used to search in the given protein sequences for the phosphorylated sites . a positive prediction of a model group is defined by at least one of the models that makes a positive prediction , whereas a negative prediction is defined as all the models that make negative predictions . the predictive models for protein phosphorylation sites were learned and evaluated using experimentally validating datasets from swiss - prot ( 7 ) and phosphobase ( 6 ) . the web server can accurately and efficiently predict the kinase - specific phosphorylation sites in the input protein sequences . as depicted in figure 2 , users can submit their uncharacterized protein sequences to the query interface and choose the kinase - specific models to predict phosphorylated serine , threonine or tyrosine . the web server locates the predictive phosphorylation sites and the involved catalytic protein kinases . in order to reveal the characteristics of the phosphorylated sites , including the phosphorylated residues and surrounding sequences , the training phosphorylation sites and constructed sequence logos corresponding to each protein kinase the following measures of the predictive performance of the models are then calculated : precision = tp/(tp+fp ) , sensitivity = tp/(tp+fn ) , specificity = tn/(tn+fp ) and the accuracy = ( sensitivity + specificity)/2 , where tp , tn , fp and fn are true positive , true negative , false positive and false negative predictions , respectively . especially , we make the equal sizes of the positive samples and the negative samples during the cross - validation processes . for each kinase - specific group , the most accurate model was chosen and applied in the web application , as presented in table 1 . for example , the precision , sensitivity , specificity and accuracy of the model learned from the mdd - clustered s_pka dataset , which was constructed by the combined phosphobase and swiss - prot data sources , were 0.85 , 0.91 , 0.84 and 0.88 , respectively . the average precision , sensitivity , specificity and accuracy of all the kinase - specific serine models were 0.88 , 0.84 , 0.88 and 0.86 , respectively . the prediction accuracy of the proposed web application was compared with several previously developed phosphorylation prediction tools , such as netphos ( 2 ) , disphos ( 3 ) and rbpnn ( 1 ) . only the average accuracy from the best model was chosen in each kinase - specific model . as presented in table 2 , the average accuracy of the models learned from the serine , threonine and tyrosine sets was 0.86 , 0.91 and 0.84 , respectively . all the accuracies of the serine , threonine and tyrosine models were higher than those from netphos and disphos . first , the species - specific phosphorylation sites can be considered to evaluate protein phosphorylation - related mechanisms in different organisms , possibly improving the accuracy of the models learned from a species - specific dataset . second , protein structural properties , such as solvent accessibility , of the phosphorylated sites can be considered to reduce the number of false positive predictions of phosphorylated sites located in buried regions . for proteins with known structures , the solvent accessibility of a phosphorylated site can be calculated trivially . however , as to the proteins without structures , the solvent accessibility of a residue should be computationally determined . the selected models learned and used in the web server the dataset is clustered by mdd .
kinasephos is a novel web server for computationally identifying catalytic kinase - specific phosphorylation sites . 
 the known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases . 
 based on the profile hidden markov model , computational models are learned from the kinase - specific groups of the phosphorylation sites . 
 after evaluating the learned models , the model with highest accuracy was selected from each kinase - specific group , for use in a web - based prediction tool for identifying protein phosphorylation sites . 
 therefore , this work developed a kinase - specific phosphorylation site prediction tool with both high sensitivity and specificity . 
 the prediction tool is freely available at .