BACKGROUND
tyrosine sulfation is a posttranslational modification , which introduces a sulfate group to a tyrosine residue in a protein  <cit> .
during the modification process, sulfation is catalysed by tyrosylprotein sulfotransferase  <cit> .
a targeted tyrosine for sulfation is normally required to be exposed on a protein surface  <cit> .
previous studies have indicated that sulfation is an important anticipator for extracellular protein-protein interactions  <cit> .
studies have shown that sulfation is related to various diseases when a malfunction of a cellular activity occurs.
for instance, sulfotyrosine can alter the affinity in some chemokine receptors leading to a downstream signalling cascade which affects the cells involved in acute and chronic events of cellular immunity  <cit> .
disease-related alterations at the non-reducing termini of chondroitin and dermatan sulfate have been found useful for monitoring proteoglycan metabolism  <cit> .
in biochemistry, sulfation has been recognised as an important contributor to detoxication for endogenous compounds  <cit> .
sulfation activity has been investigated in various cancer studies such as breast cancer  <cit> , lung cancer  <cit> , prostate cancer  <cit> , and pancreatic cancer  <cit> .
because of the relevance to various disease, tyrosine sulfation has been the target for drug design for over a decade  <cit> .
in silico prediction of posttranslational modification sites is a significant activity in bioinformatics.
for instance, in expasy  various ptm site predictors have been implemented.
specifically, a predictor named as sulfinator  for sulfotyrosine site prediction has been successfully implemented using hidden markov models   <cit> .
the predictor was able to obtain a sensitivity  of 98% and total prediction accuracy of 98%.
when the predictor is used on newly sequenced proteins, it is found that the predictor has a particularly low sensitivity although the specificity  is high.
in this study, a new approach is therefore developed aiming to improve the sensitivity while maintaining the specificity.
there is another predictor developed only for tyrosine sulfation sites in animal viruses using position-specific-scoring-matrix   <cit> .
this approach is very similar to the so-called h-function proposed by poorman  <cit>   <dig> years ago.
because only positive peptides are used for scoring, such an approach suffers low specificity when used for making prediction on unseen data  <cit> .
<dig> jackknife simulations were conducted for only positive data.
although it claimed prediction accuracy of  <dig> %, the model was actually trained with a carefully selected threshold.
the claimed accuracy was observed after tuning the threshold, which is therefore likely over-estimated.
meanwhile, there is no public available tool for the comparison.
in a review paper, some most common features describing the patterns of the residues flanking a tyrosine sulfation site were given  <cit> .
the patterns are found from the residues which flank the experimentally verified tyrosine sulfation sites using a regular expression pattern match approach.
this is commonly used in various posttranslational modification pattern analysis projects.
the web tool called weblogos  is such an application  <cit> .
the reviewer discussed some motif patterns summarised from an earlier study, for instance, glu and asp commonly occur between - <dig> and  <dig> of a tyrosine sulfation site.
however, the regular expression approach suffers two theoretical limitations.
first, such an approach assumes that motif positions are mutually independent with a uniform background distribution which may not be true in most applications  <cit> .
second, the motifs generated this way are sensitive to experimental errors  <cit> .
machine learning models, on the other had, are more error-tolerate and have been recognised being capable of generalising well on unseen data.
in the common practice of peptide classification, the input for site prediction is normally a symmetrical peptide of consecutive amino acid resides that flank the potentially modified tyrosine.
in this study three peptide sizes have been evaluated and the amino acids have been encoded using a hydrophobicity scale  <cit> .
the encoded numerical data of peptides are then treated as inputs for building prediction models using various machine learning algorithms.
the reason of using a hydrophobicity scale is due to its traditional role in analysing the impact of amino acid hydrophobicity on protein structure and potential sites for protein-protein interactions  <cit> .
hydrophobic amino acids are generally located in the protein interior whereas hydrophilic amino acids are generally located on the protein surface as targets for binding with other molecules.
a protein whose surface is composed of mainly negatively charged amino acids such as glutamate and aspartate will bind to a protein with mainly positively-charged molecules such as lysine and arginine  <cit> .
this means that the hydrophobicity scale can be one candidate for encoding amino acids for constructing a predictive model.
this study has used the cornette scale  <cit> .
RESULTS
there are  <dig> experimentally verified sulfotyrosine sites and  <dig> unconfirmed sulfotyrosine sites in  <dig> blind test sequences.
two inferred sulfotyrosine sites were not used for the evaluation.
table  <dig> shows the prediction result for these sequences using the sulfinator.
in the table, "actual" means the experimentally verified sulfotyrosine sites while "predicted" means the predicted sulfotyrosine sites.
"accession" is the accession number from ncbi database.
numbers in bold face are the sulfotyrosine sites missed from the sulfinator.
numbers in italic are the false sulfotyrosine sites.
the total prediction accuracy is 82% with a specificity of 94% and a sensitivity of 61%.
the sensitivity is 33% lower than the specificity.
the chi-square test which has been used in bioinformatics  <cit>  is used to evaluate the significance of the improvement of the sensitivity.
the test value is  <dig>  .
this represents that the new predictor is able to increase the sensitivity significantly compared with the previous one.
figures  <dig> and  <dig> show the roc curves of the rf and svm models, respectively.
it can be seen that three rf models are consistent while the svm model built on the 20-mer peptide outperforms the other two svm models, i.e.
the curve is more close to the top-left corner.
discussion
in order to investigate the consistency among the rf models, correlation analysis is conducted.
figure  <dig> shows the correlation analysis of the predictions generated from the 10-mer rf model and 20-mer rf model.
the correlation is  <dig> .
figure  <dig> shows the correlation analysis of the predictions generated from the 10-mer rf model and 30-mer rf model.
the correlation is  <dig> .
figure  <dig> shows the correlation analysis of the predictions generated from the 20-mer rf model and 30-mer rf model.
the correlation is  <dig> .
the high correlation indicates that three rf models are very consistent in prediction demonstrating a high robustness of the algorithm.
the major differences between the sulfinator and the predictors constructed in this paper are the use of different algorithms as well as the different presentation approaches of amino acids to a machine learning model.
hmm does not need an encoding process while the predictors constructed in this study use a hydrophobicity scale to encode the amino acids.
it is known that the random forest algorithm and the support vector machine algorithm have been well-known in improving the generalisation capability of a model.
the significant improvement in the prediction accuracy in blind data these models can result from the use of rf and svm algorithms and the use of hydrophobicity scale.
finally, a single rf predictor is built using whole training data coded using 20-mer peptides .
the rf predictor, a c program which is used to extract 20-mer peptides from a query sequence and encoding peptides using the cornette scale, the whole training sequences and the blind sequences are available in the web site  for free use, where a web tool is also available.
the rf predictor is then used to make prediction on the  <dig> blind sequences.
its performance is the same as that obtained from cross-validation models.
for instance, protein q9pu <dig> is a cholecystokinin with  <dig> residues.
it is related to the release of pancreatic enzymes in the gut  <cit> .
a high degree of identity was found between the sequence from chicken and chinchilla which inferred a homologous function  <cit> .
the sulfotyrosine is the first residue of the peptide cholecystokinin- <dig> .
the residue has been accurately predicted by both cross-validation rf predictions and the single rf predictor, as well as using sulfinator.
protein a2zbg <dig> is a phytosulfokines  <dig>  two sulfotyrosine sites have been found in the peptide phytosulfokine-beta .
this protein is associated with plant cell differentiation, organogenesis, somatic embryogenesis and cell proliferation.
the sulfated tyrosine is for binding to a putative membrane receptor  <cit> .
in this case both sites have been accurately predicted using the rf model.
however, sulfinator failed to predict both.
the rf predictor failed to predict three sulfotyrosine sites in extracellular proteins  while sulfinator failed to predict one of these three sites .
details of the analysis of all the predictions can be seen in table  <dig>  in the table, "site" represents the experimentally verified sulfotyrosine sites as well as those tyrosine residues which are not experimentally verified sulfotyrosine sites.
if the value in the "ture" column is  <dig>  it represents that the site is an experimentally verified one.
sulfinator represents the predictions of the sulfinator tool, where "y" represents predicted sulfotyrosine sites including false positives.
the sites  <dig> and  <dig> of q <dig> m3v <dig> are missed experimentally verified sulfotyrosine sites.
blanks represent predicted non- sulfotyrosine sites.
"rf1" represents the prediction  of cross-validation predictions while "rf2" represents the predictions  made by a single rf predictor.
"peptide" represents the segments in which the sulfotyrosine sites sit.
"region" represents the protein in which the sulfotyrosine sites are.
it is also important to see how confident we trust the predictions made by a model and whether this new approach is making a significant contribution to prediction accuracy compared with old models.
for this we investigate the properties of the negative and positive predictive powers  <cit> .
the negative predictive power measures how likely a negative prediction is true.
in other words, it measures the probability that a prediction of unconfirmed sulfotyrosine is a true unconfirmed sulfotyrosine.
it is calculated by the fraction of correctly identified unconfirmed sulfotyrosine sites over the total predicted unconfirmed sulfotyrosine sites.
the positive predictive power then measures the probability that a sultyrosine prediction is a true sulfotyrosine.
this is calculated by the fraction of correctly identified true sulfotyrosine sites over the total predicted sulfotyrosine sites.
given the confusion matrix made by testing the blind sequences as in table  <dig>  we can work out these two measurements.
in the table, "negative" represents unconfirmed sulfotyrosine and "positive" represents experimentally verified sulfotyrosine.
the left panel is the result obtained when using sulfinator while the right panel is the result generated by the rf model based on the 20-mer data.
when using sulfinator, the negative and the positive predictive powers are 82% and 85%, respectively.
however they are 91% and 94%, respectively, when using the rf models.
it can be seen that the confidence of trusting an unconfirmed sulfotyrosine site has increased by 9% using the rf models and the confidence of trusting a sulfotyrosine site has improved by 9% as well.
CONCLUSIONS
this paper has presented a new predictor for sulfotyrosine sites in protein sequences.
the sequences annotated after  <dig> are used as the blind test data for comparing the models constructed using various other machine learning algorithms in this study and sulfinator, a prediction tool established in  <dig>  through evaluation, it has been found that the predictors constructed using the random forest algorithm and the support vector machine algorithm show significantly improved prediction accuracy compared with sulfinator.
the random forest models demonstrate consistently better performance.
using the rf models, the sensitivity is increased by 22%, the specificity is increased by 3%, and the total prediction accuracy is increased by 10% compared with the sulfinator.
both negative and positive predictive powers have been increased by 9% using the rf models.
the 20-mer rf model is the method of choice for implementing a predictor because it has the highest aur.

 tyrosine sulfation is a posttranslational modification, which introduces a sulfate group to a tyrosine residue in a protein. during the modification process, sulfation is catalysed by tyrosylprotein sulfotransferase. a targeted tyrosine for sulfation is normally required to be exposed on a protein surface. previous studies have indicated that sulfation is an important anticipator for extracellular protein-protein interactions. studies have shown that sulfation is related to various diseases when a malfunction of a cellular activity occurs. for instance, sulfotyrosine can alter the affinity in some chemokine receptors leading to a downstream signalling cascade which affects the cells involved in acute and chronic events of cellular immunity. disease-related alterations at the non-reducing termini of chondroitin and dermatan sulfate have been found useful for monitoring proteoglycan metabolism. in biochemistry, sulfation has been recognised as an important contributor to detoxication for endogenous compounds. sulfation activity has been investigated in various cancer studies such as breast cancer, lung cancer, prostate cancer, and pancreatic cancer. because of the relevance to various disease, tyrosine sulfation has been the target for drug design for over a decade. in silico prediction of posttranslational modification sites is a significant activity in bioinformatics. for instance, in expasy various ptm site predictors have been implemented. specifically, a predictor named as sulfinator for sulfotyrosine site prediction has been successfully implemented using hidden markov models. the predictor was able to obtain a sensitivity of 98% and total prediction accuracy of 98%. when the predictor is used on newly sequenced proteins, it is found that the predictor has a particularly low sensitivity although the specificity is high. in this study, a new approach is therefore developed aiming to improve the sensitivity while maintaining the specificity. there is another predictor developed only for tyrosine sulfation sites in animal viruses using position-specific-scoring-matrix. this approach is very similar to the so-called h-function proposed by poorman years ago. because only positive peptides are used for scoring, such an approach suffers low specificity when used for making prediction on unseen data. jackknife simulations were conducted for only positive data. although it claimed prediction accuracy of %, the model was actually trained with a carefully selected threshold. the claimed accuracy was observed after tuning the threshold, which is therefore likely over-estimated. meanwhile, there is no public available tool for the comparison. in a review paper, some most common features describing the patterns of the residues flanking a tyrosine sulfation site were given. the patterns are found from the residues which flank the experimentally verified tyrosine sulfation sites using a regular expression pattern match approach. this is commonly used in various posttranslational modification pattern analysis projects. the web tool called weblogos is such an application. the reviewer discussed some motif patterns summarised from an earlier study, for instance, glu and asp commonly occur between - and of a tyrosine sulfation site. however, the regular expression approach suffers two theoretical limitations. first, such an approach assumes that motif positions are mutually independent with a uniform background distribution which may not be true in most applications. second, the motifs generated this way are sensitive to experimental errors. machine learning models, on the other had, are more error-tolerate and have been recognised being capable of generalising well on unseen data. in the common practice of peptide classification, the input for site prediction is normally a symmetrical peptide of consecutive amino acid resides that flank the potentially modified tyrosine. in this study three peptide sizes have been evaluated and the amino acids have been encoded using a hydrophobicity scale. the encoded numerical data of peptides are then treated as inputs for building prediction models using various machine learning algorithms. the reason of using a hydrophobicity scale is due to its traditional role in analysing the impact of amino acid hydrophobicity on protein structure and potential sites for protein-protein interactions. hydrophobic amino acids are generally located in the protein interior whereas hydrophilic amino acids are generally located on the protein surface as targets for binding with other molecules. a protein whose surface is composed of mainly negatively charged amino acids such as glutamate and aspartate will bind to a protein with mainly positively-charged molecules such as lysine and arginine. this means that the hydrophobicity scale can be one candidate for encoding amino acids for constructing a predictive model. this study has used the cornette scale.  there are experimentally verified sulfotyrosine sites and unconfirmed sulfotyrosine sites in blind test sequences. two inferred sulfotyrosine sites were not used for the evaluation. table shows the prediction result for these sequences using the sulfinator. in the table, "actual" means the experimentally verified sulfotyrosine sites while "predicted" means the predicted sulfotyrosine sites. "accession" is the accession number from ncbi database. numbers in bold face are the sulfotyrosine sites missed from the sulfinator. numbers in italic are the false sulfotyrosine sites. the total prediction accuracy is 82% with a specificity of 94% and a sensitivity of 61%. the sensitivity is 33% lower than the specificity. the chi-square test which has been used in bioinformatics is used to evaluate the significance of the improvement of the sensitivity. the test value is. this represents that the new predictor is able to increase the sensitivity significantly compared with the previous one. figures and show the roc curves of the rf and svm models, respectively. it can be seen that three rf models are consistent while the svm model built on the 20-mer peptide outperforms the other two svm models, i.e. the curve is more close to the top-left corner. discussion in order to investigate the consistency among the rf models, correlation analysis is conducted. figure shows the correlation analysis of the predictions generated from the 10-mer rf model and 20-mer rf model. the correlation is. figure shows the correlation analysis of the predictions generated from the 10-mer rf model and 30-mer rf model. the correlation is. figure shows the correlation analysis of the predictions generated from the 20-mer rf model and 30-mer rf model. the correlation is. the high correlation indicates that three rf models are very consistent in prediction demonstrating a high robustness of the algorithm. the major differences between the sulfinator and the predictors constructed in this paper are the use of different algorithms as well as the different presentation approaches of amino acids to a machine learning model. hmm does not need an encoding process while the predictors constructed in this study use a hydrophobicity scale to encode the amino acids. it is known that the random forest algorithm and the support vector machine algorithm have been well-known in improving the generalisation capability of a model. the significant improvement in the prediction accuracy in blind data these models can result from the use of rf and svm algorithms and the use of hydrophobicity scale. finally, a single rf predictor is built using whole training data coded using 20-mer peptides. the rf predictor, a c program which is used to extract 20-mer peptides from a query sequence and encoding peptides using the cornette scale, the whole training sequences and the blind sequences are available in the web site for free use, where a web tool is also available. the rf predictor is then used to make prediction on the blind sequences. its performance is the same as that obtained from cross-validation models. for instance, protein q9pu is a cholecystokinin with residues. it is related to the release of pancreatic enzymes in the gut. a high degree of identity was found between the sequence from chicken and chinchilla which inferred a homologous function. the sulfotyrosine is the first residue of the peptide cholecystokinin-. the residue has been accurately predicted by both cross-validation rf predictions and the single rf predictor, as well as using sulfinator. protein a2zbg is a phytosulfokines two sulfotyrosine sites have been found in the peptide phytosulfokine-beta. this protein is associated with plant cell differentiation, organogenesis, somatic embryogenesis and cell proliferation. the sulfated tyrosine is for binding to a putative membrane receptor. in this case both sites have been accurately predicted using the rf model. however, sulfinator failed to predict both. the rf predictor failed to predict three sulfotyrosine sites in extracellular proteins while sulfinator failed to predict one of these three sites. details of the analysis of all the predictions can be seen in table in the table, "site" represents the experimentally verified sulfotyrosine sites as well as those tyrosine residues which are not experimentally verified sulfotyrosine sites. if the value in the "ture" column is it represents that the site is an experimentally verified one. sulfinator represents the predictions of the sulfinator tool, where "y" represents predicted sulfotyrosine sites including false positives. the sites and of q m3v are missed experimentally verified sulfotyrosine sites. blanks represent predicted non- sulfotyrosine sites. "rf1" represents the prediction of cross-validation predictions while "rf2" represents the predictions made by a single rf predictor. "peptide" represents the segments in which the sulfotyrosine sites sit. "region" represents the protein in which the sulfotyrosine sites are. it is also important to see how confident we trust the predictions made by a model and whether this new approach is making a significant contribution to prediction accuracy compared with old models. for this we investigate the properties of the negative and positive predictive powers. the negative predictive power measures how likely a negative prediction is true. in other words, it measures the probability that a prediction of unconfirmed sulfotyrosine is a true unconfirmed sulfotyrosine. it is calculated by the fraction of correctly identified unconfirmed sulfotyrosine sites over the total predicted unconfirmed sulfotyrosine sites. the positive predictive power then measures the probability that a sultyrosine prediction is a true sulfotyrosine. this is calculated by the fraction of correctly identified true sulfotyrosine sites over the total predicted sulfotyrosine sites. given the confusion matrix made by testing the blind sequences as in table we can work out these two measurements. in the table, "negative" represents unconfirmed sulfotyrosine and "positive" represents experimentally verified sulfotyrosine. the left panel is the result obtained when using sulfinator while the right panel is the result generated by the rf model based on the 20-mer data. when using sulfinator, the negative and the positive predictive powers are 82% and 85%, respectively. however they are 91% and 94%, respectively, when using the rf models. it can be seen that the confidence of trusting an unconfirmed sulfotyrosine site has increased by 9% using the rf models and the confidence of trusting a sulfotyrosine site has improved by 9% as well.  this paper has presented a new predictor for sulfotyrosine sites in protein sequences. the sequences annotated after are used as the blind test data for comparing the models constructed using various other machine learning algorithms in this study and sulfinator, a prediction tool established in through evaluation, it has been found that the predictors constructed using the random forest algorithm and the support vector machine algorithm show significantly improved prediction accuracy compared with sulfinator. the random forest models demonstrate consistently better performance. using the rf models, the sensitivity is increased by 22%, the specificity is increased by 3%, and the total prediction accuracy is increased by 10% compared with the sulfinator. both negative and positive predictive powers have been increased by 9% using the rf models. the 20-mer rf model is the method of choice for implementing a predictor because it has the highest aur. 
a predictor published seven years ago has been very successful with claimed prediction accuracy of 98%. this new approach has increased the sensitivity by 22%, the specificity by 3%, and the total prediction accuracy by 10% compared with the previous predictor using the same blind data. a new approach has been developed for predicting sulfotyrosine sites using the random forest algorithm after a careful evaluation of seven machine learning algorithms. the random forest algorithm is able to deliver a better model compared with the hidden markov model, the support vector machine, artificial neural networks, and others for predicting sulfotyrosine sites. the success shows that the random forest algorithm together with an amino acid hydrophobicity scale encoding can be a good candidate for peptide classification. a web tool has been implemented at for public use. tyrosine sulfation is one of the most important posttranslational modifications. due to its relevance to various disease developments, tyrosine sulfation has become the target for drug design. in addition, the random forest model has an excellent feature for ranking the residues flanking tyrosine sites, hence providing more information for further investigating the tyrosine sulfation mechanism. meanwhile, both negative and positive predictive powers have been increased by 9%. in order to facilitate efficient drug design, accurate prediction of sulfotyrosine sites is desirable. however, it has a particularly low sensitivity when predicting sulfotyrosine sites in some newly sequenced proteins.
 tyrosine sulfation is one of the most important posttranslational modifications. due to its relevance to various disease developments, tyrosine sulfation has become the target for drug design. in order to facilitate efficient drug design, accurate prediction of sulfotyrosine sites is desirable. a predictor published seven years ago has been very successful with claimed prediction accuracy of 98%. however, it has a particularly low sensitivity when predicting sulfotyrosine sites in some newly sequenced proteins.  a new approach has been developed for predicting sulfotyrosine sites using the random forest algorithm after a careful evaluation of seven machine learning algorithms. peptides are formed by consecutive residues symmetrically flanking tyrosine sites. they are then encoded using an amino acid hydrophobicity scale. this new approach has increased the sensitivity by 22%, the specificity by 3%, and the total prediction accuracy by 10% compared with the previous predictor using the same blind data. meanwhile, both negative and positive predictive powers have been increased by 9%. in addition, the random forest model has an excellent feature for ranking the residues flanking tyrosine sites, hence providing more information for further investigating the tyrosine sulfation mechanism. a web tool has been implemented at for public use.  the random forest algorithm is able to deliver a better model compared with the hidden markov model, the support vector machine, artificial neural networks, and others for predicting sulfotyrosine sites. the success shows that the random forest algorithm together with an amino acid hydrophobicity scale encoding can be a good candidate for peptide classification. 