BACKGROUND
post-translational modifications are observed on almost all proteins analysed to date.
during phosphorylation, a phosphate molecule is placed on another molecule resulting in the functional activation or inactivation of the receiving molecule.
these modifications have a substantial influence on the structure and functions of protein.
phosphorylation at the serine, threonine and tyrosine residues by enzymes of the kinase and phosphatise super-families is one of the most frequent forms of post-translational modifications in intracellular proteins.
as phosphorylation has a significant impact on diverse cellular signalling processes, it is needed in the regulation of cell differentiation, as a trigger for the progression of the cell cycle and control of metabolism, transcription, apoptosis, cytoskeletal rearrangements  <cit>  in animals.
as importantly, the phosphorylation of protein is considered as being a key event in many signal transduction pathways of biological systems  <cit> .
it is thus important for us to be able to accurately determine the phosphorylation state of proteins so as to better identify the state of a cell.
it has been widely reported in literature that a large number of human diseases are caused by a disruption of normal cellular phosphorylation events.
for example, phosphorylated tyrosines are recognised by specialised binding domains on other proteins, and such interactions are used to initiate intracellular signalling pathways.
aberrant tyrosine phosphorylation is a hallmark of many types of cancer.
tyrosine phosphorylation also plays major roles in cellular physiology, and functional perturbation of protein-tyrosine kinases and protein-tyrosine phosphatases underlie many human diseases  <cit> .
in order to determine phosphoproteins and individual phosphorylation sites, various experimental tools have been used.
however, in vivo or in vitro identification of phosphorylation sites is labour-intensive, time-consuming and often limited to the availability and optimisation of enzymatic reactions  <cit> .
several large-scale phosphoproteomic data using the mass-spectrometry approach have been collected and published  <cit>  but are still not helpful in distinguishing the kinase-specific sites on substrates.
for example, mass spectrometry methods have been shown to be disfavourable in the identification of phosphate-modified residues, leading to an underestimation of the extent of phosphorylation presents in vivo  <cit> .
due to the practical limitations and complexities of the previously-mentioned methods, many scientists have turned to computer-based methods.
computer-based methods can efficiently handle massive amounts of protein data, determine phosphoprotiens and identify individual phosphorylation sites from one dimensional atomic coordinates with high precision.
several computer-simulated machine learning techniques such as artificial neural networks  and support vector machines  have been extensively used in various biological sequence analyses as well as phosphorylation site prediction.
these methods are built based on the assumption that neighbouring residues to the phosphorylated site represents the main determinant for kinase specificity  <cit> .
although a large number of machine learning based methods have proved to be effective in the prediction of phosphorylation sites, several important issues that could potentially degrade the performance of machine learning or statistical-based methods have been largely ignored.
the high dimensionality of protein sequence data not only causes a dynamic increase in computational complexity but also creates an overfitting/generalisation problem for non-parametric methods.
with machine learning models, better generalisation and faster training  can be achieved when they have fewer weights to be adjusted with fewer inputs.
this study aims to develop an accurate and stable phosphorylation site predictor.
our proposed model named, siteseek uses a semi-parametric form of a state-of-the-art machine learning model dubbed, adaptive locality-effective kernel machine .
in addition, with the boosting algorithm, it adaptively combines the learners to find an optimised fit for the given phosphoprotiens.
to efficiently capture suitable information from protein sequences, it uses a newly developed compact evolutionary and hydrophobic profile  built on position specific scoring matrix  and simultaneously axially and radially alignment hydrophobicity  hydrophobicity scale.
in our experiments, the siteseek excels in efficiently processing high dimensional protein data with a more accurate and stable predictive performance than currently existing models.
the novel feature of this study is the use of a new machine learning based semi-parametric model, a newly developed profile  and the use of unique training dataset  that contains experimentally verified phosphorylation sites manually extracted from major protein sequence databases and the literature.
RESULTS
our experiments consist of four consecutive steps.
first, we demonstrate the usefulness of our proposed ceh-profile by comparing its prediction accuracy with four other well-known amino acids encoding methods.
second, the predictive performance of our proposed machine learning model, adaptive-lekm, specially designed for the high dimensional problem of protein sequence data is compared with other nine contemporary machine learning models for prediction accuracy, sensitivity, specificity, correlation-coefficient, type i and type ii errors on newly built ps-benchmark_ <dig> dataset.
next, we analyse the result from siteseek which uses the novel adaptive-lekm and the ceh-profile when tested on four main kinase groups and four main kinase families.
we then compare those results with the consensus results from literature.
lastly, the predictive performance of siteseek is directly compared with thee of the most widely known contemporary phosphorylation site predictors on ps-benchmark_ <dig> dataset.
compact evolutionary and hydrophobicity profile
in order to prove the usefulness of ceh-profile, the suitability of the newly proposed profile to the prediction of phosphorylation sites is compared with other four most widely used encoding schemes .
as shown in table  <dig>  the ceh-profile and compactpssm showed fairly improved performance for the prediction of phosphorylation sites.
with compactpssm, we obtain 3% increased prediction accuracy than the widely used pssm.
furthermore, ceh-profile which additionally uses hydrophobicity in the format of effective  <dig> bit binary representation  has a better accuracy at  <dig> % –  <dig> % than original pssm.
the sensitivity of ceh-profile has significantly increased by 5% from the original pssm.
it seems that with ceh-profile, we can combine the advantage of using both pssm and sarah <dig> profiles as sarah <dig> provides a good model sensitivity level of  <dig> .
this experiment based on our hypothesis proved that less discriminatory features that reside in original pssm can be replaced by an effective hydrophobicity scale so that a new profile which contains compact evolutionary information as well as hydrophobic values can be created.
as examined in discussion, several limitations of existing pssm and oe profiles can be effectively minimised by utilising the novel ceh-profile.
evidently, ceh-profile is more useful than widely known sequence profiles for protein phosphorylation site prediction.
comparison of adaptive-lekm with other machine learning models
the predictive performance of adaptive-lekm was compared with nine other existing state-of-the-art machine learning models such as general regression neural network , radial basis neural network , multi-layered perceptron , kernel nearest neighbour , decision tree , kernel logistic regression , and three different transductive svms, namely svmlight, adaboost-svm and locality-effective kernel machine .
table  <dig> shows the evaluation results of each model in terms of accuracy , sensitivity , specificity , correlation-coefficient , variance  and time on ps-benchmark_ <dig> dataset.
as shown in table  <dig>  one of our models, lekm successfully reached the best model stabilisation  with less computational requirements .
however, one of the methods used in lekm  showed a slightly less accurate learning.
hence, we utilised adaboost algorithm for the fine tuning of the lekm and it  finally achieved the best accuracy with a fair level of model stableness and furthermore reduced complexity.
in addition, adaptive-lekm achieved much better model robustness than other methods with the cc of  <dig> .
our methods used in adaptive-lekm, semi-parametric approximation and adaptive tuning of the model using adaboost were confirmed to be more suitable in processing high dimensional protein data than other non-parametric models.
it should be noted that the adaboost algorithm was also tested with the original svm, but no significant improvement was observed .
predictive performance of siteseek on major kinase families and groups
here we look at experimental results obtained by siteseek on four main kinase groups and four main kinase families in terms of ac, sn, sp, cc, type i and type ii ers.
siteseek uses the adaptive-lekm and is trained with the novel ceh-profile.
table  <dig> compares the results of siteseek with the consensus results of the literature.
in general, siteseek showed a 9% increase in prediction accuracy than the consensus results.
as for model stableness, siteseek also achieved a fairly low level of average variance in four evaluation measures .
the sensitivity of siteseek on cdk, pka and pkc kinase families are distinguishably higher than the consensus results.
this means that stable prediction capability of our model comes with effectively reducing the false negative values .
type i er indicates experimentally verified unmodified sites that are predicted  to be modified.
the experimental results of siteseek are written in bold and others are the consensus results of literature obtained by kim et al  <dig>
as for the four main kinase groups , the overall results are somewhat lower than those of kinase families.
the best accuracy was obtained with the cmgc kinase group  whereas the tk kinase group showed the lowest accuracy of  <dig> .
compared to the experiments on kinase families, cc values dropped significantly for  <dig>  points.
this result may reveal that computational models are more suitable to find stronger correlations among same kinase families than those of kinase groups.
comparison with other contemporary phosphorylation site predictors
the predictive performance of our proposed predictor, siteseek which uses above-mentioned adaptive-lekm and ceh-profile is compared with four contemporary phosphorylation site predictors on same testing dataset, ps-benchmark_ <dig>  disphos  <cit>  is trained on over  <dig> non-redundant experimentally confirmed protein phosphorylation sites and uses position-specific amino acid frequencies as well as disorder information to improve the discrimination between phosphorylation and non-phosphorylation sites.
the prediction accuracy of disphos reaches  <dig> % for serine,  <dig> % for threonine, and  <dig> % for tyrosine.
however, disphos provides little information about the corresponding protein kinases for the predicted phosphorylation sites.
scansite  <cit>  identifies short protein motifs that are recognised by phosphorylation protein serine/threonine or tyrosine kinases.
each motif used in scansite was constructed from a set of experimentally validated phosphorylation sites and was represented as a position-specific scoring matrix.
netphosk  <cit>  is an enhanced version of netphos uses an artificial neural network and incorporates the functionality of providing protein kinases  information for  <dig> pks.
in the identification of protein kinase a  phosphorylation sites, netphosk achieves 100% sensitivity and 40% specificity.
finally, predphospho  <cit>  uses support vector machine and demonstrates a superior predictive performance of 83–95% at the kinase family level, and 76–91% at the kinase group level.
although each predictor was trained using its own training dataset, they all were tested on same benchmark dataset,  which contains  <dig>  polypeptide chains .
discussion
over the past decades, many computational prediction algorithms have been developed for various proteomic studies.
they have evolved from simple linear statistics to complex machine learners.
however, the most significant breakthroughs were the incorporation of new biological information into an efficient prediction model and the development of new models which can efficiently exploit suitable information from its primary sequence.
for example, the exploitation of evolutionary information that is available from protein families has brought significant improvements in the prediction of protein secondary structure   <cit> .
compared to protein structure predictions, a small number of studies to find suitable information/representations for phosphorylation site prediction have been reported.
like protein secondary structure prediction, most of the phosphorylation site prediction methods also use the evolutionary information in the format of pssm   <cit> .
the theory behind using the sequence profile is based on the fact that the sequence alignment of homologous proteins accords with their structural alignment and aligned residues usually have similar structures.
thus, the sequence profile can provide more information about structure than the single sequence to its learner.
although the sequence profile provides more structural information, the structural information that resides in the sequence profile may not be a significant importance in the case of phosphorylation site prediction.
it has been observed that approximately only ten neighbouring residues are major determinants of phosphorylation sites.
many models have been built on this observation and performed reasonably well with a number of specific kinases.
however, the specificity determinants and rules remain elusive for a large number of protein kinases that display a number of substrates sharing little or no sequence similarity in the known phosphopeptides  <cit> .
furthermore, most databases searched by current alignment tools like psi-blast not only contain a number of non-phosphoprotiens, but also generate a large numbers of irrelevant hits from the protein databases  <cit> .
another commonly used amino acid encoding method is dubbed orthogonal encoding, also known as the binary representation or the distributed encoding.
in orthogonal encoding, each letter can be represented by a twenty dimensional binary vector indicating the presence of a particular amino acid type  <cit> .
the twenty standard amino acids are ordered one through twenty, and the ith amino acid has the binary codeword of twenty bits with the ith bit set to "1" and all others to "0"s, for i =  <dig>   <dig> ...,  <dig>  for example, 'alanine' is expressed by  <dig>  <dig>  'cysteine' is encoded as  <dig>  <dig> and so on.
among the first twenty units of the vector, each unit stands for one type of amino amid.
in order to allow a window to extend over the n terminus and the c terminus, the 21st unit has to be added.
a residue with window size n is encoded in 21* bits with the binary code-words of amino acids concatenated based on their order in the window  <cit> .
it has been one of the most widely adopted methods as it does not introduce any artificial correlations between the amino acids.
however, orthogonal encoding has several widely known drawbacks.
firstly, the dimension of residue vector can increase rapidly as n increases, it may lead to large computational cost and model complexity  input nodes and connecting weights), and recognition bias.
thus, it can cause poor performance of the classifiers  <cit> .
secondly, the use of euclidean space has no theoretic foundation in biology or chemistry and hence might reduce the accuracy of a model.
according to the numerical assignment in the distributed method, the distance between any two different amino acids is  <dig> and would conflict with reality in biology  <cit> .
the encoding methods discussed above are employed by most well-known protein structure predictors and are shown to be useful as they sufficiently contain information required for general protein structure prediction tasks.
however, as phosphorylation site prediction does not only involve various chemical interactions but also is known as a non-structural prediction task, the above-mentioned encoding methods may not be suitable for this problem.
in this study, we observed that widely known sequence profile contains irrelevant features which impede accurate recognition by the learner.
we thus developed a new profile that replaces the prediction irrelevant features by hydrophobic information represented in sarah <dig> scale.
this method when used in ceh-profile showed reasonable improvement over existing profiles.
in the literature, the number of encoding schemes proposed for phosphorylation site prediction is far less than the ones for other proteomic applications.
as discussed above, existing methods have shown several critical drawbacks for phosphorylation site prediction.
hence, we emphasise that researchers should devote their effort to seeking a suitable representation of amino acids for phosphorylation site prediction to reach the upper boundary of prediction accuracy.
several considerable issues have been inevitably raised in this study.
most importantly, the training set used in this study may contain a certain amount of incorrect information as some of non-site residues can be determined as site-residues in the future.
the prediction accuracy for general machine learning  models is highly dependent on the quality of the input/training dataset.
if the dataset were to contain inaccurate information, it can significantly affect the learning process thus predicting sites incorrectly.
the use of a semi-parametric model in this study takes upon assumptions that are stronger than those of non-parametric models so that it can more robustly perform its prediction task on noise-datasets than pure non-parametric methods.
however, even when the semi-parametric approach was shown to be more resistant to the noise-data, the problems caused by using potentially corrupted dataset cannot be resolved completely unless an experimentally complete and faultless phosphorylation data is provided.
despite of our effort to collect high quality phosphorylation data for all kind of kinase families, each kinase dataset is excessively biased to a few kinase families.
for example, pka and pkc kinase family data take approximately 69% of agc kinase group dataset.
hence, a learner trained with agc kinase group dataset may not perform well with other kinase families in the agc kinase group as it was trained mostly with pka or pkc kinase family data.
it is ideal if we can collect more experimentally verified data for those kinase families; however, in the case that this is not possible, matched sampling methods were shown to be somewhat effective.
one of the experiments conducted to compare the performance of siteseek with other existing phosphorylation site predictors  reveals several considerable issues.
first, the predictive performance of other predictors can be overestimated in that it is almost infeasible to confirm whether their training datasets contains any of amino acids sequences in our testing set.
the presence of testing examples in their training dataset not only violates the assumptions needed for learning but also makes evaluation measures unreliable.
hence, this issue should be taken into account carefully for more accurate evaluation and comparison of models.
it should be noted that as the seven-fold cross validation was also used for the evaluation of siteseek, no redundant data entry for training and testing sets was found.
second, the window sizes adopted in this study may not be suitable for kinase family or group based predictions.
in the literature, it has been discussed that the major determinants for phosphorylation sites are  <dig> neighbouring residues for tyrosine and threonine;  <dig> for serine  <cit> .
several studies using computational methods recently have shown that the optimal window sizes are found to be different depending on types of kinase families.
however, no wet-lab research has validated the kinase family based optimal window sizes to date.
hence, further research should be carried out so as to provide for a more accurate evaluation and prediction of phosphorylation sites.
CONCLUSIONS
this paper identified the effectiveness and utility of our newly proposed machine learning based predictor, siteseek for phosphorylation site prediction.
this study addressed two important issues in the computational prediction of protein phosphorylation sites.
current encoding schemes like pssm and orthogonal encodings do not provide sufficient information for accurate prediction of phosphorylation sites using existing computational models.
our approach uses compact pssm with efficient hydrophobicity scale proves to be more effective in the prediction of phosphorylation sites.
next, for a given set of high dimensional protein data, the combination of a parametric local model with a non-parametric global model provided a way of fine-tuning the model by the adjustment of a single smoothing parameter σ as well as providing efficient semi-parametric approximation.
this was demonstrated by our above four consecutive experiments.
the semi-parametric approach used in adaptive-lekm was shown to be effective by finding an optimal trade-off between parametric and non-parametric models with significantly reduced computations.
when tested with the newly built ps-benchmark_ <dig> dataset, siteseek which uses the adaptive-lekm and ceh-profile achieved the best prediction accuracy when compared with contemporary phosphorylation site predictors.
thus, allowing us to accurately predict phosphorylation sites in proteins so as to better understand their functions in biological systems.

 post-translational modifications are observed on almost all proteins analysed to date. during phosphorylation, a phosphate molecule is placed on another molecule resulting in the functional activation or inactivation of the receiving molecule. these modifications have a substantial influence on the structure and functions of protein. phosphorylation at the serine, threonine and tyrosine residues by enzymes of the kinase and phosphatise super-families is one of the most frequent forms of post-translational modifications in intracellular proteins. as phosphorylation has a significant impact on diverse cellular signalling processes, it is needed in the regulation of cell differentiation, as a trigger for the progression of the cell cycle and control of metabolism, transcription, apoptosis, cytoskeletal rearrangements in animals. as importantly, the phosphorylation of protein is considered as being a key event in many signal transduction pathways of biological systems. it is thus important for us to be able to accurately determine the phosphorylation state of proteins so as to better identify the state of a cell. it has been widely reported in literature that a large number of human diseases are caused by a disruption of normal cellular phosphorylation events. for example, phosphorylated tyrosines are recognised by specialised binding domains on other proteins, and such interactions are used to initiate intracellular signalling pathways. aberrant tyrosine phosphorylation is a hallmark of many types of cancer. tyrosine phosphorylation also plays major roles in cellular physiology, and functional perturbation of protein-tyrosine kinases and protein-tyrosine phosphatases underlie many human diseases. in order to determine phosphoproteins and individual phosphorylation sites, various experimental tools have been used. however, in vivo or in vitro identification of phosphorylation sites is labour-intensive, time-consuming and often limited to the availability and optimisation of enzymatic reactions. several large-scale phosphoproteomic data using the mass-spectrometry approach have been collected and published but are still not helpful in distinguishing the kinase-specific sites on substrates. for example, mass spectrometry methods have been shown to be disfavourable in the identification of phosphate-modified residues, leading to an underestimation of the extent of phosphorylation presents in vivo. due to the practical limitations and complexities of the previously-mentioned methods, many scientists have turned to computer-based methods. computer-based methods can efficiently handle massive amounts of protein data, determine phosphoprotiens and identify individual phosphorylation sites from one dimensional atomic coordinates with high precision. several computer-simulated machine learning techniques such as artificial neural networks and support vector machines have been extensively used in various biological sequence analyses as well as phosphorylation site prediction. these methods are built based on the assumption that neighbouring residues to the phosphorylated site represents the main determinant for kinase specificity. although a large number of machine learning based methods have proved to be effective in the prediction of phosphorylation sites, several important issues that could potentially degrade the performance of machine learning or statistical-based methods have been largely ignored. the high dimensionality of protein sequence data not only causes a dynamic increase in computational complexity but also creates an overfitting/generalisation problem for non-parametric methods. with machine learning models, better generalisation and faster training can be achieved when they have fewer weights to be adjusted with fewer inputs. this study aims to develop an accurate and stable phosphorylation site predictor. our proposed model named, siteseek uses a semi-parametric form of a state-of-the-art machine learning model dubbed, adaptive locality-effective kernel machine. in addition, with the boosting algorithm, it adaptively combines the learners to find an optimised fit for the given phosphoprotiens. to efficiently capture suitable information from protein sequences, it uses a newly developed compact evolutionary and hydrophobic profile built on position specific scoring matrix and simultaneously axially and radially alignment hydrophobicity hydrophobicity scale. in our experiments, the siteseek excels in efficiently processing high dimensional protein data with a more accurate and stable predictive performance than currently existing models. the novel feature of this study is the use of a new machine learning based semi-parametric model, a newly developed profile and the use of unique training dataset that contains experimentally verified phosphorylation sites manually extracted from major protein sequence databases and the literature.  our experiments consist of four consecutive steps. first, we demonstrate the usefulness of our proposed ceh-profile by comparing its prediction accuracy with four other well-known amino acids encoding methods. second, the predictive performance of our proposed machine learning model, adaptive-lekm, specially designed for the high dimensional problem of protein sequence data is compared with other nine contemporary machine learning models for prediction accuracy, sensitivity, specificity, correlation-coefficient, type i and type ii errors on newly built ps-benchmark_ dataset. next, we analyse the result from siteseek which uses the novel adaptive-lekm and the ceh-profile when tested on four main kinase groups and four main kinase families. we then compare those results with the consensus results from literature. lastly, the predictive performance of siteseek is directly compared with thee of the most widely known contemporary phosphorylation site predictors on ps-benchmark_ dataset. compact evolutionary and hydrophobicity profile in order to prove the usefulness of ceh-profile, the suitability of the newly proposed profile to the prediction of phosphorylation sites is compared with other four most widely used encoding schemes. as shown in table the ceh-profile and compactpssm showed fairly improved performance for the prediction of phosphorylation sites. with compactpssm, we obtain 3% increased prediction accuracy than the widely used pssm. furthermore, ceh-profile which additionally uses hydrophobicity in the format of effective bit binary representation has a better accuracy at % – % than original pssm. the sensitivity of ceh-profile has significantly increased by 5% from the original pssm. it seems that with ceh-profile, we can combine the advantage of using both pssm and sarah profiles as sarah provides a good model sensitivity level of. this experiment based on our hypothesis proved that less discriminatory features that reside in original pssm can be replaced by an effective hydrophobicity scale so that a new profile which contains compact evolutionary information as well as hydrophobic values can be created. as examined in discussion, several limitations of existing pssm and oe profiles can be effectively minimised by utilising the novel ceh-profile. evidently, ceh-profile is more useful than widely known sequence profiles for protein phosphorylation site prediction. comparison of adaptive-lekm with other machine learning models the predictive performance of adaptive-lekm was compared with nine other existing state-of-the-art machine learning models such as general regression neural network, radial basis neural network, multi-layered perceptron, kernel nearest neighbour, decision tree, kernel logistic regression, and three different transductive svms, namely svmlight, adaboost-svm and locality-effective kernel machine. table shows the evaluation results of each model in terms of accuracy, sensitivity, specificity, correlation-coefficient, variance and time on ps-benchmark_ dataset. as shown in table one of our models, lekm successfully reached the best model stabilisation with less computational requirements. however, one of the methods used in lekm showed a slightly less accurate learning. hence, we utilised adaboost algorithm for the fine tuning of the lekm and it finally achieved the best accuracy with a fair level of model stableness and furthermore reduced complexity. in addition, adaptive-lekm achieved much better model robustness than other methods with the cc of. our methods used in adaptive-lekm, semi-parametric approximation and adaptive tuning of the model using adaboost were confirmed to be more suitable in processing high dimensional protein data than other non-parametric models. it should be noted that the adaboost algorithm was also tested with the original svm, but no significant improvement was observed. predictive performance of siteseek on major kinase families and groups here we look at experimental results obtained by siteseek on four main kinase groups and four main kinase families in terms of ac, sn, sp, cc, type i and type ii ers. siteseek uses the adaptive-lekm and is trained with the novel ceh-profile. table compares the results of siteseek with the consensus results of the literature. in general, siteseek showed a 9% increase in prediction accuracy than the consensus results. as for model stableness, siteseek also achieved a fairly low level of average variance in four evaluation measures. the sensitivity of siteseek on cdk, pka and pkc kinase families are distinguishably higher than the consensus results. this means that stable prediction capability of our model comes with effectively reducing the false negative values. type i er indicates experimentally verified unmodified sites that are predicted to be modified. the experimental results of siteseek are written in bold and others are the consensus results of literature obtained by kim et al as for the four main kinase groups, the overall results are somewhat lower than those of kinase families. the best accuracy was obtained with the cmgc kinase group whereas the tk kinase group showed the lowest accuracy of. compared to the experiments on kinase families, cc values dropped significantly for points. this result may reveal that computational models are more suitable to find stronger correlations among same kinase families than those of kinase groups. comparison with other contemporary phosphorylation site predictors the predictive performance of our proposed predictor, siteseek which uses above-mentioned adaptive-lekm and ceh-profile is compared with four contemporary phosphorylation site predictors on same testing dataset, ps-benchmark_ disphos is trained on over non-redundant experimentally confirmed protein phosphorylation sites and uses position-specific amino acid frequencies as well as disorder information to improve the discrimination between phosphorylation and non-phosphorylation sites. the prediction accuracy of disphos reaches % for serine, % for threonine, and % for tyrosine. however, disphos provides little information about the corresponding protein kinases for the predicted phosphorylation sites. scansite identifies short protein motifs that are recognised by phosphorylation protein serine/threonine or tyrosine kinases. each motif used in scansite was constructed from a set of experimentally validated phosphorylation sites and was represented as a position-specific scoring matrix. netphosk is an enhanced version of netphos uses an artificial neural network and incorporates the functionality of providing protein kinases information for pks. in the identification of protein kinase a phosphorylation sites, netphosk achieves 100% sensitivity and 40% specificity. finally, predphospho uses support vector machine and demonstrates a superior predictive performance of 83–95% at the kinase family level, and 76–91% at the kinase group level. although each predictor was trained using its own training dataset, they all were tested on same benchmark dataset, which contains polypeptide chains. discussion over the past decades, many computational prediction algorithms have been developed for various proteomic studies. they have evolved from simple linear statistics to complex machine learners. however, the most significant breakthroughs were the incorporation of new biological information into an efficient prediction model and the development of new models which can efficiently exploit suitable information from its primary sequence. for example, the exploitation of evolutionary information that is available from protein families has brought significant improvements in the prediction of protein secondary structure. compared to protein structure predictions, a small number of studies to find suitable information/representations for phosphorylation site prediction have been reported. like protein secondary structure prediction, most of the phosphorylation site prediction methods also use the evolutionary information in the format of pssm. the theory behind using the sequence profile is based on the fact that the sequence alignment of homologous proteins accords with their structural alignment and aligned residues usually have similar structures. thus, the sequence profile can provide more information about structure than the single sequence to its learner. although the sequence profile provides more structural information, the structural information that resides in the sequence profile may not be a significant importance in the case of phosphorylation site prediction. it has been observed that approximately only ten neighbouring residues are major determinants of phosphorylation sites. many models have been built on this observation and performed reasonably well with a number of specific kinases. however, the specificity determinants and rules remain elusive for a large number of protein kinases that display a number of substrates sharing little or no sequence similarity in the known phosphopeptides. furthermore, most databases searched by current alignment tools like psi-blast not only contain a number of non-phosphoprotiens, but also generate a large numbers of irrelevant hits from the protein databases. another commonly used amino acid encoding method is dubbed orthogonal encoding, also known as the binary representation or the distributed encoding. in orthogonal encoding, each letter can be represented by a twenty dimensional binary vector indicating the presence of a particular amino acid type. the twenty standard amino acids are ordered one through twenty, and the ith amino acid has the binary codeword of twenty bits with the ith bit set to "1" and all others to "0"s, for i =..., for example,'alanine' is expressed by'cysteine' is encoded as and so on. among the first twenty units of the vector, each unit stands for one type of amino amid. in order to allow a window to extend over the n terminus and the c terminus, the 21st unit has to be added. a residue with window size n is encoded in 21* bits with the binary code-words of amino acids concatenated based on their order in the window. it has been one of the most widely adopted methods as it does not introduce any artificial correlations between the amino acids. however, orthogonal encoding has several widely known drawbacks. firstly, the dimension of residue vector can increase rapidly as n increases, it may lead to large computational cost and model complexity input nodes and connecting weights), and recognition bias. thus, it can cause poor performance of the classifiers. secondly, the use of euclidean space has no theoretic foundation in biology or chemistry and hence might reduce the accuracy of a model. according to the numerical assignment in the distributed method, the distance between any two different amino acids is and would conflict with reality in biology. the encoding methods discussed above are employed by most well-known protein structure predictors and are shown to be useful as they sufficiently contain information required for general protein structure prediction tasks. however, as phosphorylation site prediction does not only involve various chemical interactions but also is known as a non-structural prediction task, the above-mentioned encoding methods may not be suitable for this problem. in this study, we observed that widely known sequence profile contains irrelevant features which impede accurate recognition by the learner. we thus developed a new profile that replaces the prediction irrelevant features by hydrophobic information represented in sarah scale. this method when used in ceh-profile showed reasonable improvement over existing profiles. in the literature, the number of encoding schemes proposed for phosphorylation site prediction is far less than the ones for other proteomic applications. as discussed above, existing methods have shown several critical drawbacks for phosphorylation site prediction. hence, we emphasise that researchers should devote their effort to seeking a suitable representation of amino acids for phosphorylation site prediction to reach the upper boundary of prediction accuracy. several considerable issues have been inevitably raised in this study. most importantly, the training set used in this study may contain a certain amount of incorrect information as some of non-site residues can be determined as site-residues in the future. the prediction accuracy for general machine learning models is highly dependent on the quality of the input/training dataset. if the dataset were to contain inaccurate information, it can significantly affect the learning process thus predicting sites incorrectly. the use of a semi-parametric model in this study takes upon assumptions that are stronger than those of non-parametric models so that it can more robustly perform its prediction task on noise-datasets than pure non-parametric methods. however, even when the semi-parametric approach was shown to be more resistant to the noise-data, the problems caused by using potentially corrupted dataset cannot be resolved completely unless an experimentally complete and faultless phosphorylation data is provided. despite of our effort to collect high quality phosphorylation data for all kind of kinase families, each kinase dataset is excessively biased to a few kinase families. for example, pka and pkc kinase family data take approximately 69% of agc kinase group dataset. hence, a learner trained with agc kinase group dataset may not perform well with other kinase families in the agc kinase group as it was trained mostly with pka or pkc kinase family data. it is ideal if we can collect more experimentally verified data for those kinase families; however, in the case that this is not possible, matched sampling methods were shown to be somewhat effective. one of the experiments conducted to compare the performance of siteseek with other existing phosphorylation site predictors reveals several considerable issues. first, the predictive performance of other predictors can be overestimated in that it is almost infeasible to confirm whether their training datasets contains any of amino acids sequences in our testing set. the presence of testing examples in their training dataset not only violates the assumptions needed for learning but also makes evaluation measures unreliable. hence, this issue should be taken into account carefully for more accurate evaluation and comparison of models. it should be noted that as the seven-fold cross validation was also used for the evaluation of siteseek, no redundant data entry for training and testing sets was found. second, the window sizes adopted in this study may not be suitable for kinase family or group based predictions. in the literature, it has been discussed that the major determinants for phosphorylation sites are neighbouring residues for tyrosine and threonine; for serine. several studies using computational methods recently have shown that the optimal window sizes are found to be different depending on types of kinase families. however, no wet-lab research has validated the kinase family based optimal window sizes to date. hence, further research should be carried out so as to provide for a more accurate evaluation and prediction of phosphorylation sites.  this paper identified the effectiveness and utility of our newly proposed machine learning based predictor, siteseek for phosphorylation site prediction. this study addressed two important issues in the computational prediction of protein phosphorylation sites. current encoding schemes like pssm and orthogonal encodings do not provide sufficient information for accurate prediction of phosphorylation sites using existing computational models. our approach uses compact pssm with efficient hydrophobicity scale proves to be more effective in the prediction of phosphorylation sites. next, for a given set of high dimensional protein data, the combination of a parametric local model with a non-parametric global model provided a way of fine-tuning the model by the adjustment of a single smoothing parameter σ as well as providing efficient semi-parametric approximation. this was demonstrated by our above four consecutive experiments. the semi-parametric approach used in adaptive-lekm was shown to be effective by finding an optimal trade-off between parametric and non-parametric models with significantly reduced computations. when tested with the newly built ps-benchmark_ dataset, siteseek which uses the adaptive-lekm and ceh-profile achieved the best prediction accuracy when compared with contemporary phosphorylation site predictors. thus, allowing us to accurately predict phosphorylation sites in proteins so as to better understand their functions in biological systems. 
siteseek is trained using a novel compact evolutionary and hydrophobicity profile to detect possible protein phosphorylation sites for a target sequence. accurate prediction of protein phosphorylation sites is of great importance for the understanding of diverse cellular signalling processes in both the human body and in animals. our newly proposed methods used in siteseek were shown to be useful for the identification of protein phosphorylation sites as it performed much better than widely known predictors on the newly built ps-benchmark_ dataset. post-translational modifications have a substantial influence on the structure and functions of protein. siteseek showed better predictive performance with % accuracy, % sensitivity, % specificity and correlation-coefficient on the four main kinase families. post-translational phosphorylation is one of the most common modification that occur in intracellular proteins. the newly proposed method proves to be more accurate and exhibits a much stable predictive performance than currently existing phosphorylation site predictors. in this study, we propose a new machine learning based protein phosphorylation site predictor, siteseek.
 post-translational modifications have a substantial influence on the structure and functions of protein. post-translational phosphorylation is one of the most common modification that occur in intracellular proteins. accurate prediction of protein phosphorylation sites is of great importance for the understanding of diverse cellular signalling processes in both the human body and in animals. in this study, we propose a new machine learning based protein phosphorylation site predictor, siteseek. siteseek is trained using a novel compact evolutionary and hydrophobicity profile to detect possible protein phosphorylation sites for a target sequence. the newly proposed method proves to be more accurate and exhibits a much stable predictive performance than currently existing phosphorylation site predictors.  the performance of the proposed model was compared to nine existing different machine learning models and four widely known phosphorylation site predictors with the newly proposed ps-benchmark_ dataset to contrast their accuracy, sensitivity, specificity and correlation coefficient. siteseek showed better predictive performance with % accuracy, % sensitivity, % specificity and correlation-coefficient on the four main kinase families.  our newly proposed methods used in siteseek were shown to be useful for the identification of protein phosphorylation sites as it performed much better than widely known predictors on the newly built ps-benchmark_ dataset. 