BACKGROUND
reactive oxygen species  are toxic oxygen-derived molecules generated during various cellular processes  <cit> .
accumulation of ros may result in the damage of different cellular components including proteins, nucleic acids, lipids and metal cofactors.
it has been indicated that many diseases, including type ii diabetes, cancer, neurodegenerative diseases and cardiovascular disease, are associated with oxidative stress  <cit> .
thus, ros has traditionally been regarded as unwanted by-products of aerobic metabolism  <cit> .
however, under normal conditions, ros can modify the structure and function of proteins in defined ways .
ros may also act as important signaling molecules in gene transcription and translation, stress protection, apoptosis, metabolism and other processes .
reactive nitrogen species , a family of antimicrobial molecules derived from nitrite oxide  and superoxide  produced via the enzymatic activity of inducible nitric oxide synthase  <dig> and nadph oxidase respectively, also play similar roles as ros does  <cit> .
cysteine is of the least abundance among  <dig> common amino acids.
however, cysteine residues usually are more conserved and tend to play critical roles  <cit> .
cysteine residues bear a thiol group that represents the most reduced state of sulfur in proteins.
these thiol groups can be oxidized to disulfide , sulfenic acid , sulfinic acid , sulfonic acid , s-nitrosothiol  or s-glutathione .
sulfenic acids are usually the intermediate of thiol-modification, which can react with other thiols or be further oxidized.
sulfinic acid and sulfonic acid represent the irreversibly oxidized products.
alternatively, sulfenic acids can be oxidized to disulfide bonds or s-nitrosothiols, which can be reduced back to thiols by thioredoxin, glutaredoxin, or glutathione  <cit> .
redox-sensitive cysteines undergo reversible thiol modifications in response to ros or rns, thereby modulate protein function, activity or localization, and serve as a regulatory switch for proteins in response to cellular redox state .
traditionally, redox-sensitive cysteines are identified by biochemical characterization of proteins accompanied by site-directed mutagenesis experiment  <cit> .
over the past decade, tremendous progress in the field of redox proteomics has been made and different gel electrophoresis  and isotope coded affinity tag  strategies have been used to measure cysteine oxidation  <cit> .
however, proteomics techniques are insensitive to proteins with low abundance including most transcription factors.
for this reason, many proteins identified by proteomics techniques to date are components of redox homeostasis systems or highly abundant target proteins such as ribosomal proteins or enzymes  <cit> .
moreover, proteomics approach is costly.
computational approaches which are not limited by protein abundance can therefore provide an important alternative to proteomics based approaches.
cysteine could be functionally categorized as structural disulfide bonded cys, metal-cys, catalytic cys and regulatory cys, with some cysteines belong to multiple groups  <cit> .
most identified redox-sensitive cysteines are known to function as catalytic or regulatory cys.
despite the fact that various in silico methods have been developed for the prediction of structural disulfide bonded cys  and metal-cys , computational approaches for predicting redox-sensitive cysteines are limited.
thiol oxidoreductases, which usually bear a dicysteine active site motif , are the most extensively studied proteins with catalytic redox-active cysteines.
based on the observation that sec  usually locate at the active sites of redox proteins, fomenko et al.
developed a procedure for high-throughput identification of catalytic redox-active cys by searching for sec/cys pairs in sequence databases  <cit> .
in another study, marino et al.
analyzed general features of catalytic redox-active cysteines in thiol oxidoreductases at the structural level, and designed a structure-based method for predicting thiol oxidoreductases and their catalytic redox-active cysteine residues  <cit> .
these approaches are efficient for detecting catalytic redox-active cysteines in thiol oxidoreductase.
however, they cannot be used for detecting redox-active cysteines in other protein types.
apart from catalytic redox-active cysteines, some regulatory cysteines may affect protein activity when oxidized or reduced.
such regulatory redox-sensitive cysteines have been found in transcription factors, kinases, phosphatases, chaperone and other proteins  <cit> .
compared to catalytic redox-sensitive cysteines, regulatory cysteines are much more difficult to predict.
computational tool that can accurately predict cysteines with regulatory roles is of great importance for our understanding of cysteine thiol oxidation  <cit> .
sanchez et al.
studied various protein structure features and found three features useful for the prediction of redox-sensitive cysteines: distance to the nearest cysteine sulfur atom, solvent accessibility and pka  <cit> .
using these features, a decision-tree based classifier cysteine oxidation prediction algorithm  was developed for predicting redox-susceptible cysteines  <cit> .
this study provided valuable information about the determinants of cysteine redox-sensitivity.
however, the application of copa is highly limited due to its dependence on protein structure data, which are not available for most proteins in the proteomes.
in another study, fan et al.
scanned the protein data bank for potential redox-active cysteine pairs by looking for proteins with alternate redox states  <cit>  and recovered  <dig>  unique redox pairs of proteins, many of which exhibit conformational differences between alternate redox states.
again, the structural data for both oxidized and reduced form of protein are required for this method  <cit> .
such simple and straightforward procedure is useful for scanning the entire protein data bank database; however, it can hardly be used for de novo prediction.
computational methods independent of protein structure data is therefore in great need for a better understanding of redox-sensitive cysteines.
in this study, a dataset of experimentally validated redox-sensitive cysteines  was collected and various features possibly related to cysteine redox-sensitivity were critically analyzed.
among them, three types of features that are efficient for redox-sensitive cysteine prediction were identified.
after further feature selection using svm-rfe, a corresponding svm classifier namely rscp was developed.
using 10-fold cross-validation on rsc <dig>  the model achieved accuracy of  <dig> , sensitivity of  <dig> , specificity of  <dig> , mcc of  <dig>  and auc of  <dig> .
when evaluated using 10-fold cross-validation with balosctdb dataset which has structure information, the model’s performance was comparable to current structure-based method.
the robustness of rscp was further validated using an independent dataset.
RESULTS
performance using different combinations of features on rsc <dig> dataset
using the rsc <dig> dataset, we first optimized the parameters for feature extraction, including: 1) the number of nearby cysteines for which the sequential distance is considered; 2) the window size for position-specific scoring matrix profile , predicted secondary structure , predicted solvent accessibility  and physical-chemical property .
the 1) and 2) parameters were optimized separately.
we first extracted the sequential distances to the 1st to 10th nearest cysteines, and then svm classifiers were trained.
the performance for different classifiers was compared according to the acc, mcc and auc values from using 10-fold cross-validation .
the best result was achieved when sequential distance for the 1st to 6th nearby cysteines were considered.
similarly, features including pssm, ss, sa and pcp were extracted using sliding windows with window sizes between  <dig> and  <dig>  and the performances from 10-fold cross-validation were compared .
the best performance was achieved when using window size of  <dig>  thus, sequential distance to the 6th nearby cysteines, and pssm, ss, sa and pcp features extracted with a window size of  <dig> were used in the following study.fig.
<dig> optimization of the parameters for feature extraction.
a performance with different numbers of nearby cysteines.
the numbers of nearby cysteines are optimized between  <dig> and  <dig>  b performance with different window sizes.
the window sizes are optimized between  <dig> and  <dig>  the gray bars indicated the finally selected parameters
the performance using different combinations of features was further compared .
when each single type of feature was tested, sequential distance to nearby cysteines  and pssm were found to be the most efficient features.
specifically, when the model was trained using sequential distance features only, an auc value of  <dig>  was achieved.
an auc value of  <dig>  was achieved when using d + pssm + ss feature set.
further integration of sa and pcp features only slightly improved the auc, but not the acc .
thus, the sa and pcp feature sets were excluded from further analysis.
by a grid search using 10-fold cross-validation, the regularization parameter c and the kernel parameter γ for svm classifier were optimized as  <dig>  and  <dig> , respectively.
the model trained using the full d + pssm + ss feature set could achieve the performance with acc of  <dig> , sn of  <dig> , sp of  <dig> , mcc of  <dig>  and auc of  <dig> .table  <dig> 10-fold cross-validation of different combinations of features on rsc758
d + pssm + ss
<dig>
<dig>
<dig>
<dig>
<dig>
the results are sorted by auc value.
the feature set in bold was selected as the optimal
d sequential distance to adjacent cysteines, pssm pssm profile, ss predicted secondary structure, sa predicted solvent accessibility, pcp physical-chemical property
fig.
<dig> the roc curves of svm classifiers using rsc <dig> dataset.
the average values of true positive rate and false positive rate from 10-fold cross-validation are used
feature selection using svm-rfe on rsc <dig> dataset
to further improve the performance, we applied svm-based recursive feature elimination  to the d + pssm + ss feature set.
initially, the full d + pssm + ss feature set has 213-dimentional vector.
as evaluated by acc, mcc and auc estimated from 10-fold cross-validation on rsc <dig> dataset, the best performance was achieved when utilizing the forty top-ranked features .
by a grid search using 10-fold cross-validation, the regularization parameter c and the kernel parameter γ for svm classifier were optimized as  <dig>  and  <dig> , respectively.
the corresponding model achieved acc of  <dig> , sn of  <dig> , sp of  <dig> , mcc of  <dig>  and auc of  <dig> , respectively.
further inspection showed that three features for d,  <dig> for pssm and  <dig> for ss, are among these selected features .fig.
<dig> performance using different number of features selected by svm-rfe for rsc <dig> dataset.
the x-axis indicated the number of selected features.
y-axis represents the acc, mcc and auc estimated from 10-fold cross-validation
performance of different machine learning techniques on rsc <dig> dataset
in addition to svm, we also compared the performance of three other widely used machine learning techniques, including naive bayes, artificial neural network and random forest.
similarly, the major parameters for these models were tuned by grid searching.
then, the performance was evaluated by 10-fold cross-validation using the forty selected features on rsc <dig> dataset.
the result showed that svm outperforms other approaches regarding acc, mcc and auc .
random forest was the second best one, with acc of  <dig> , sn of  <dig> , sp of  <dig> , mcc of  <dig>  and auc of  <dig> .
thus, the svm classifier trained using the forty selected features was used as the final model.table  <dig> 10-fold cross-validation with forty selected features using different machine learning methods on rsc758
the results are sorted by auc value
fig.
<dig> the roc curves of different machine learning techniques using the forty selected features for rsc <dig> dataset.
the average values of true positive rate and false positive rate from 10-fold cross validation are used
evaluation of the most efficient features
our study revealed that three features, including sequential distance to nearby cysteines, pssm profile and predicted secondary structure, are efficient for redox-sensitive cysteine prediction.
we next investigated if redox-sensitive cysteines show distinct patterns when considering these features.
identification of such patterns is of particular importance for our understanding of the determinants of cysteine redox-sensitivity.
sequential distance to the nearby cysteines  has been previously used to predict structural disulfide  <cit> , another cysteine oxidative state different from reversible oxidation.
in this study, we this feature to be the most efficient for predicting redox-sensitive cysteines, indicating that it may be associated with cysteine redox-sensitivity.
we found that the sequential distance to nearby cysteines seems to be longer for redox-sensitive cysteines compared with redox-insensitive ones .
from the osctdb dataset which has quite different gene family composition, we observed similar pattern except for the sequential distance to the most nearest cysteine .
this is probably due to the fact that more than one third of the proteins  in osctdb are thiol oxidoreductases, which usually bear two redox-sensitive cysteines within the typical cxxc motif.fig.
<dig> comparison of sequential distance to nearby cysteines between redox-sensitive and redox-insensitive cysteines.
this result is derived from the rsc <dig> dataset.
the x-axis indicated the index of nearby cysteines .
y-axis represents the log10-scaled sequential distance.
the error bars represent the standard deviation
pssm profile represents the probability of occurrence for each type of amino acid residues, thus it can be considered as a measure of residue conservation in a given location.
using the whole rsc <dig> dataset, we examined the average pssm scores of the flanking region for redox-sensitive and redox-insensitive cysteines.
we found that several types of amino acids  showed significantly different pssm scores surrounding redox-sensitive and redox-insensitive cysteines  .
the predicted secondary structure for residues in the flanking region of redox-sensitive and -insensitive cysteines was summarized in additional file 2: figure s <dig>  the result indicated an over-represented coil and relatively depleted helix surrounding redox-sensitive cysteines compared with redox-insensitive ones.
comparison with structure-based method
the performance of rscp was compared with cysteine oxidation prediction algorithm , which is a decision-tree based classifier using protein structural features  <cit> .
because rsc <dig> dataset does not contain structural information, the comparison was conducted using balosctdb dataset by 10-fold cross-validation.
weka package  <cit>  was used for decision-tree implement, and m value was set to  <dig> as suggested in the original paper of copa  <cit> .
the sequential distance, pssm and ss features were extracted using the same parameters as aforementioned.
the regularization parameter c and the kernel parameter γ for svm classifier were optimized as  <dig>  and  <dig> , respectively.
we first evaluated the performance on balosctdb dataset using the forty features selected according to rsc <dig> dataset.
it achieved an acc of  <dig> , sn of  <dig> , sp of  <dig> , mcc of  <dig>  and auc of  <dig> , which is quite similar to that evaluated on rsc <dig> dataset .
because the protein family composition in balosctdb is quite different from rsc <dig>  we further examined the performance using features selected based on balosctdb dataset itself .
the result showed that with the twenty top-ranked features by svm-rfe, it could achieve the best performance with acc of  <dig> , sn of  <dig> , sp of  <dig> , mcc of  <dig>  and auc of  <dig>  .
among these twenty features, three are for d, four for pssm and thirteen for ss .table  <dig> performance comparison between rscp and copa using balosctdb by 10-fold cross-validation
in summary, the result showed that even without structure data, the performance of rscp could still be comparable to copa .
this also indicated that although cysteine redox-sensitivity is mainly affected by protein structural environment as revealed by some previous studies , redox-sensitive cysteines can still be inferred using only sequence features with moderate accuracy.
sequence-based prediction is much more advantageous in that it is not limited by structure data thus can be widely used for prediction as long as the protein sequence data is available.
performance evaluation using osctdb
we further evaluated the performance of rscp trained from rsc <dig> using osctdb as independent dataset.
osctdb consists of a few types of enzymes together with some non-enzyme proteins, and catalytic redox-active cysteines from oxidoreductase make up about one third of the dataset.
however, when collecting rsc <dig>  we tried not to be biased towards any gene families by including various types of enzymes and a large number of non-enzyme proteins.
rscp achieved acc of  <dig> , sn of  <dig> , sp of  <dig>  and mcc of  <dig>  , using this independent dataset.
even though the testing dataset has very different protein family composition to that of training dataset, the prediction accuracy on this independent testing dataset is similar to the cross-validation result, indicating rscp is robust.table  <dig> performance evaluation using osctdb by gene families
only gene families with at least ten redox-sensitive cysteines were shown
the testing dataset also contain different gene family composition compared to the training dataset.
the prediction accuracy on different gene families was summarized in table  <dig>  the result indicates that rscp is robust on predicting redox-sensitive cysteines in most gene families except for transferases.
rscp achieved the highest accuracy of  <dig>  for hydrolases.
redox-sensitive cysteines have been identified mainly from enzymes, especially oxidoreductases.
a variety of non-enzyme proteins are also regulated via redox processes.
although regulatory cysteines are thought to be much difficult to predict  <cit> , rscp achieved high accuracy of  <dig>  for cysteines in non-enzyme proteins.
this model is therefore particularly useful for the analysis of regulatory redox-sensitive cysteines.
discussion
thiol-based redox regulation and signaling has become one of the important research focuses in recent years.
in this study, we identified three important sequence-based features that are efficient for the prediction of redox-sensitive cysteines.
after feature selection using svm-rfe, we further developed a sequence-based svm classifier for predicting redox-sensitive cysteines.
when evaluated with balosctdb dataset which has structure information, the model achieved performance comparable to current structure-based method.
the major advantage of this sequence-based classifier lays in its independence of protein structure data, which is not readily available for a large portion of the proteomes.
the high reactivity and chemical plasticity of cysteine, mainly due to its sulfur-based functional group, has been well known  <cit> .
for redox-sensitive cysteine, those could form reversible disulfides, are most well studied .
unlike structural disulfides which cannot be easily opened once formed, reversible disulfides could be reversibly oxidized and reduced under different conditions thus function as regulatory switches.
in this study, cysteines forming reversible disulfides were considered as redox-sensitive ones, while those forming structural disulfides were included in the the negative training dataset.
by compiling the training dataset in this way, we expected the trained model could also have potential ability to distinguish these two types of disulfide-bonded cysteines.
apart from redox sensitivity as we focused in this study, cysteines could also function via binding different metal irons  <cit>  such as fe2+/3+ and zn2+.
a number of previous studies as review in  <cit>  suggested that some well known zinc-factor binding cysteines could also undergo redox modification.
when generating the training dataset, redox-sensitive cysteines with metal-binding function were not excluded.
we neither tried to distinguish redox-sensitive cysteines with or without metal-binding potential for analysis.
however, in the future, it would be interesting to investigate how the determinants of redox-sensitivity and metal-binding potential for cysteine are related.
two datasets were used in this study: balosctdb is a dataset adopted from previous studies which is smaller but with structural information, and rsc <dig> is a newly generated dataset of larger size and relatively unbiased.
while the model optimized and evaluated using balosctdb could achieve good performance comparable to current structure-based method, the model trained using rsc <dig> dataset only achieved moderate accuracy.
one possibility is that apart from the redox-sensitivity, cysteines under different types of redox modifications also have their distinct properties which are not well represented by the identified features.
in this study, we aimed at examine the common features underlying redox-sensitivity, and develop a general purpose predictor of redox-sensitive cysteines.
but with the accumulation of validated redox-sensitive cysteines, it would be interesting to perform comparative analysis among different types of redox modification to reveal their unique features.
it is also highly desirable to develop computational tools which could not only predict the redox sensitivity but also the exact type of redox modification.
CONCLUSIONS
in this study, we identified three important sequence-based features for redox-sensitive cysteines, and further developed a svm classifier for predicting redox-sensitive cysteines.
we expect the accurate prediction of redox-sensitive cysteines could not only enhance our understanding about the redox sensitivity of cysteine, but also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

 reactive oxygen species are toxic oxygen-derived molecules generated during various cellular processes. accumulation of ros may result in the damage of different cellular components including proteins, nucleic acids, lipids and metal cofactors. it has been indicated that many diseases, including type ii diabetes, cancer, neurodegenerative diseases and cardiovascular disease, are associated with oxidative stress. thus, ros has traditionally been regarded as unwanted by-products of aerobic metabolism. however, under normal conditions, ros can modify the structure and function of proteins in defined ways. ros may also act as important signaling molecules in gene transcription and translation, stress protection, apoptosis, metabolism and other processes. reactive nitrogen species, a family of antimicrobial molecules derived from nitrite oxide and superoxide produced via the enzymatic activity of inducible nitric oxide synthase and nadph oxidase respectively, also play similar roles as ros does. cysteine is of the least abundance among common amino acids. however, cysteine residues usually are more conserved and tend to play critical roles. cysteine residues bear a thiol group that represents the most reduced state of sulfur in proteins. these thiol groups can be oxidized to disulfide, sulfenic acid, sulfinic acid, sulfonic acid, s-nitrosothiol or s-glutathione. sulfenic acids are usually the intermediate of thiol-modification, which can react with other thiols or be further oxidized. sulfinic acid and sulfonic acid represent the irreversibly oxidized products. alternatively, sulfenic acids can be oxidized to disulfide bonds or s-nitrosothiols, which can be reduced back to thiols by thioredoxin, glutaredoxin, or glutathione. redox-sensitive cysteines undergo reversible thiol modifications in response to ros or rns, thereby modulate protein function, activity or localization, and serve as a regulatory switch for proteins in response to cellular redox state. traditionally, redox-sensitive cysteines are identified by biochemical characterization of proteins accompanied by site-directed mutagenesis experiment. over the past decade, tremendous progress in the field of redox proteomics has been made and different gel electrophoresis and isotope coded affinity tag strategies have been used to measure cysteine oxidation. however, proteomics techniques are insensitive to proteins with low abundance including most transcription factors. for this reason, many proteins identified by proteomics techniques to date are components of redox homeostasis systems or highly abundant target proteins such as ribosomal proteins or enzymes. moreover, proteomics approach is costly. computational approaches which are not limited by protein abundance can therefore provide an important alternative to proteomics based approaches. cysteine could be functionally categorized as structural disulfide bonded cys, metal-cys, catalytic cys and regulatory cys, with some cysteines belong to multiple groups. most identified redox-sensitive cysteines are known to function as catalytic or regulatory cys. despite the fact that various in silico methods have been developed for the prediction of structural disulfide bonded cys and metal-cys, computational approaches for predicting redox-sensitive cysteines are limited. thiol oxidoreductases, which usually bear a dicysteine active site motif, are the most extensively studied proteins with catalytic redox-active cysteines. based on the observation that sec usually locate at the active sites of redox proteins, fomenko et al. developed a procedure for high-throughput identification of catalytic redox-active cys by searching for sec/cys pairs in sequence databases. in another study, marino et al. analyzed general features of catalytic redox-active cysteines in thiol oxidoreductases at the structural level, and designed a structure-based method for predicting thiol oxidoreductases and their catalytic redox-active cysteine residues. these approaches are efficient for detecting catalytic redox-active cysteines in thiol oxidoreductase. however, they cannot be used for detecting redox-active cysteines in other protein types. apart from catalytic redox-active cysteines, some regulatory cysteines may affect protein activity when oxidized or reduced. such regulatory redox-sensitive cysteines have been found in transcription factors, kinases, phosphatases, chaperone and other proteins. compared to catalytic redox-sensitive cysteines, regulatory cysteines are much more difficult to predict. computational tool that can accurately predict cysteines with regulatory roles is of great importance for our understanding of cysteine thiol oxidation. sanchez et al. studied various protein structure features and found three features useful for the prediction of redox-sensitive cysteines: distance to the nearest cysteine sulfur atom, solvent accessibility and pka. using these features, a decision-tree based classifier cysteine oxidation prediction algorithm was developed for predicting redox-susceptible cysteines. this study provided valuable information about the determinants of cysteine redox-sensitivity. however, the application of copa is highly limited due to its dependence on protein structure data, which are not available for most proteins in the proteomes. in another study, fan et al. scanned the protein data bank for potential redox-active cysteine pairs by looking for proteins with alternate redox states and recovered unique redox pairs of proteins, many of which exhibit conformational differences between alternate redox states. again, the structural data for both oxidized and reduced form of protein are required for this method. such simple and straightforward procedure is useful for scanning the entire protein data bank database; however, it can hardly be used for de novo prediction. computational methods independent of protein structure data is therefore in great need for a better understanding of redox-sensitive cysteines. in this study, a dataset of experimentally validated redox-sensitive cysteines was collected and various features possibly related to cysteine redox-sensitivity were critically analyzed. among them, three types of features that are efficient for redox-sensitive cysteine prediction were identified. after further feature selection using svm-rfe, a corresponding svm classifier namely rscp was developed. using 10-fold cross-validation on rsc the model achieved accuracy of, sensitivity of, specificity of, mcc of and auc of. when evaluated using 10-fold cross-validation with balosctdb dataset which has structure information, the model’s performance was comparable to current structure-based method. the robustness of rscp was further validated using an independent dataset.  performance using different combinations of features on rsc dataset using the rsc dataset, we first optimized the parameters for feature extraction, including: 1) the number of nearby cysteines for which the sequential distance is considered; 2) the window size for position-specific scoring matrix profile, predicted secondary structure, predicted solvent accessibility and physical-chemical property. the 1) and 2) parameters were optimized separately. we first extracted the sequential distances to the 1st to 10th nearest cysteines, and then svm classifiers were trained. the performance for different classifiers was compared according to the acc, mcc and auc values from using 10-fold cross-validation. the best result was achieved when sequential distance for the 1st to 6th nearby cysteines were considered. similarly, features including pssm, ss, sa and pcp were extracted using sliding windows with window sizes between and and the performances from 10-fold cross-validation were compared. the best performance was achieved when using window size of thus, sequential distance to the 6th nearby cysteines, and pssm, ss, sa and pcp features extracted with a window size of were used in the following study.fig. optimization of the parameters for feature extraction. a performance with different numbers of nearby cysteines. the numbers of nearby cysteines are optimized between and b performance with different window sizes. the window sizes are optimized between and the gray bars indicated the finally selected parameters the performance using different combinations of features was further compared. when each single type of feature was tested, sequential distance to nearby cysteines and pssm were found to be the most efficient features. specifically, when the model was trained using sequential distance features only, an auc value of was achieved. an auc value of was achieved when using d + pssm + ss feature set. further integration of sa and pcp features only slightly improved the auc, but not the acc. thus, the sa and pcp feature sets were excluded from further analysis. by a grid search using 10-fold cross-validation, the regularization parameter c and the kernel parameter γ for svm classifier were optimized as and, respectively. the model trained using the full d + pssm + ss feature set could achieve the performance with acc of, sn of, sp of, mcc of and auc of.table 10-fold cross-validation of different combinations of features on rsc758 d + pssm + ss the results are sorted by auc value. the feature set in bold was selected as the optimal d sequential distance to adjacent cysteines, pssm pssm profile, ss predicted secondary structure, sa predicted solvent accessibility, pcp physical-chemical property fig. the roc curves of svm classifiers using rsc dataset. the average values of true positive rate and false positive rate from 10-fold cross-validation are used feature selection using svm-rfe on rsc dataset to further improve the performance, we applied svm-based recursive feature elimination to the d + pssm + ss feature set. initially, the full d + pssm + ss feature set has 213-dimentional vector. as evaluated by acc, mcc and auc estimated from 10-fold cross-validation on rsc dataset, the best performance was achieved when utilizing the forty top-ranked features. by a grid search using 10-fold cross-validation, the regularization parameter c and the kernel parameter γ for svm classifier were optimized as and, respectively. the corresponding model achieved acc of, sn of, sp of, mcc of and auc of, respectively. further inspection showed that three features for d, for pssm and for ss, are among these selected features.fig. performance using different number of features selected by svm-rfe for rsc dataset. the x-axis indicated the number of selected features. y-axis represents the acc, mcc and auc estimated from 10-fold cross-validation performance of different machine learning techniques on rsc dataset in addition to svm, we also compared the performance of three other widely used machine learning techniques, including naive bayes, artificial neural network and random forest. similarly, the major parameters for these models were tuned by grid searching. then, the performance was evaluated by 10-fold cross-validation using the forty selected features on rsc dataset. the result showed that svm outperforms other approaches regarding acc, mcc and auc. random forest was the second best one, with acc of, sn of, sp of, mcc of and auc of. thus, the svm classifier trained using the forty selected features was used as the final model.table 10-fold cross-validation with forty selected features using different machine learning methods on rsc758 the results are sorted by auc value fig. the roc curves of different machine learning techniques using the forty selected features for rsc dataset. the average values of true positive rate and false positive rate from 10-fold cross validation are used evaluation of the most efficient features our study revealed that three features, including sequential distance to nearby cysteines, pssm profile and predicted secondary structure, are efficient for redox-sensitive cysteine prediction. we next investigated if redox-sensitive cysteines show distinct patterns when considering these features. identification of such patterns is of particular importance for our understanding of the determinants of cysteine redox-sensitivity. sequential distance to the nearby cysteines has been previously used to predict structural disulfide, another cysteine oxidative state different from reversible oxidation. in this study, we this feature to be the most efficient for predicting redox-sensitive cysteines, indicating that it may be associated with cysteine redox-sensitivity. we found that the sequential distance to nearby cysteines seems to be longer for redox-sensitive cysteines compared with redox-insensitive ones. from the osctdb dataset which has quite different gene family composition, we observed similar pattern except for the sequential distance to the most nearest cysteine. this is probably due to the fact that more than one third of the proteins in osctdb are thiol oxidoreductases, which usually bear two redox-sensitive cysteines within the typical cxxc motif.fig. comparison of sequential distance to nearby cysteines between redox-sensitive and redox-insensitive cysteines. this result is derived from the rsc dataset. the x-axis indicated the index of nearby cysteines. y-axis represents the log10-scaled sequential distance. the error bars represent the standard deviation pssm profile represents the probability of occurrence for each type of amino acid residues, thus it can be considered as a measure of residue conservation in a given location. using the whole rsc dataset, we examined the average pssm scores of the flanking region for redox-sensitive and redox-insensitive cysteines. we found that several types of amino acids showed significantly different pssm scores surrounding redox-sensitive and redox-insensitive cysteines. the predicted secondary structure for residues in the flanking region of redox-sensitive and -insensitive cysteines was summarized in additional file 2: figure s the result indicated an over-represented coil and relatively depleted helix surrounding redox-sensitive cysteines compared with redox-insensitive ones. comparison with structure-based method the performance of rscp was compared with cysteine oxidation prediction algorithm, which is a decision-tree based classifier using protein structural features. because rsc dataset does not contain structural information, the comparison was conducted using balosctdb dataset by 10-fold cross-validation. weka package was used for decision-tree implement, and m value was set to as suggested in the original paper of copa. the sequential distance, pssm and ss features were extracted using the same parameters as aforementioned. the regularization parameter c and the kernel parameter γ for svm classifier were optimized as and, respectively. we first evaluated the performance on balosctdb dataset using the forty features selected according to rsc dataset. it achieved an acc of, sn of, sp of, mcc of and auc of, which is quite similar to that evaluated on rsc dataset. because the protein family composition in balosctdb is quite different from rsc we further examined the performance using features selected based on balosctdb dataset itself. the result showed that with the twenty top-ranked features by svm-rfe, it could achieve the best performance with acc of, sn of, sp of, mcc of and auc of. among these twenty features, three are for d, four for pssm and thirteen for ss.table performance comparison between rscp and copa using balosctdb by 10-fold cross-validation in summary, the result showed that even without structure data, the performance of rscp could still be comparable to copa. this also indicated that although cysteine redox-sensitivity is mainly affected by protein structural environment as revealed by some previous studies, redox-sensitive cysteines can still be inferred using only sequence features with moderate accuracy. sequence-based prediction is much more advantageous in that it is not limited by structure data thus can be widely used for prediction as long as the protein sequence data is available. performance evaluation using osctdb we further evaluated the performance of rscp trained from rsc using osctdb as independent dataset. osctdb consists of a few types of enzymes together with some non-enzyme proteins, and catalytic redox-active cysteines from oxidoreductase make up about one third of the dataset. however, when collecting rsc we tried not to be biased towards any gene families by including various types of enzymes and a large number of non-enzyme proteins. rscp achieved acc of, sn of, sp of and mcc of, using this independent dataset. even though the testing dataset has very different protein family composition to that of training dataset, the prediction accuracy on this independent testing dataset is similar to the cross-validation result, indicating rscp is robust.table performance evaluation using osctdb by gene families only gene families with at least ten redox-sensitive cysteines were shown the testing dataset also contain different gene family composition compared to the training dataset. the prediction accuracy on different gene families was summarized in table  the result indicates that rscp is robust on predicting redox-sensitive cysteines in most gene families except for transferases. rscp achieved the highest accuracy of for hydrolases. redox-sensitive cysteines have been identified mainly from enzymes, especially oxidoreductases. a variety of non-enzyme proteins are also regulated via redox processes. although regulatory cysteines are thought to be much difficult to predict, rscp achieved high accuracy of for cysteines in non-enzyme proteins. this model is therefore particularly useful for the analysis of regulatory redox-sensitive cysteines. discussion thiol-based redox regulation and signaling has become one of the important research focuses in recent years. in this study, we identified three important sequence-based features that are efficient for the prediction of redox-sensitive cysteines. after feature selection using svm-rfe, we further developed a sequence-based svm classifier for predicting redox-sensitive cysteines. when evaluated with balosctdb dataset which has structure information, the model achieved performance comparable to current structure-based method. the major advantage of this sequence-based classifier lays in its independence of protein structure data, which is not readily available for a large portion of the proteomes. the high reactivity and chemical plasticity of cysteine, mainly due to its sulfur-based functional group, has been well known. for redox-sensitive cysteine, those could form reversible disulfides, are most well studied. unlike structural disulfides which cannot be easily opened once formed, reversible disulfides could be reversibly oxidized and reduced under different conditions thus function as regulatory switches. in this study, cysteines forming reversible disulfides were considered as redox-sensitive ones, while those forming structural disulfides were included in the the negative training dataset. by compiling the training dataset in this way, we expected the trained model could also have potential ability to distinguish these two types of disulfide-bonded cysteines. apart from redox sensitivity as we focused in this study, cysteines could also function via binding different metal irons such as fe2+/3+ and zn2+. a number of previous studies as review in suggested that some well known zinc-factor binding cysteines could also undergo redox modification. when generating the training dataset, redox-sensitive cysteines with metal-binding function were not excluded. we neither tried to distinguish redox-sensitive cysteines with or without metal-binding potential for analysis. however, in the future, it would be interesting to investigate how the determinants of redox-sensitivity and metal-binding potential for cysteine are related. two datasets were used in this study: balosctdb is a dataset adopted from previous studies which is smaller but with structural information, and rsc is a newly generated dataset of larger size and relatively unbiased. while the model optimized and evaluated using balosctdb could achieve good performance comparable to current structure-based method, the model trained using rsc dataset only achieved moderate accuracy. one possibility is that apart from the redox-sensitivity, cysteines under different types of redox modifications also have their distinct properties which are not well represented by the identified features. in this study, we aimed at examine the common features underlying redox-sensitivity, and develop a general purpose predictor of redox-sensitive cysteines. but with the accumulation of validated redox-sensitive cysteines, it would be interesting to perform comparative analysis among different types of redox modification to reveal their unique features. it is also highly desirable to develop computational tools which could not only predict the redox sensitivity but also the exact type of redox modification.  in this study, we identified three important sequence-based features for redox-sensitive cysteines, and further developed a svm classifier for predicting redox-sensitive cysteines. we expect the accurate prediction of redox-sensitive cysteines could not only enhance our understanding about the redox sensitivity of cysteine, but also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines. 
accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines. using 10-fold cross-validation on rsc dataset, the accuracy, sensitivity, specificity, mcc and auc were estimated as,,, and, respectively. when evaluated using 10-fold cross-validation with balosctdb dataset which has structure information, the model achieved performance comparable to current structure-based method. recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. after further feature selection using svm-rfe, we developed redox-sensitive cysteine predictor, a svm based classifier for redox-sensitive cysteine prediction using primary sequence only. in this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. in this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. these features are: sequential distance to the nearby cysteines, pssm profile and predicted secondary structure of flanking residues. the major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. reactive oxygen speciesredox-sensitive cysteinepost-translational modificationsupport vector machinesvm-based recursive feature eliminationhong kong university grants committeehkbu1/crf/10guo dianjing issue-copyright-statement© the author 2016
 reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. cysteine thiol groups of proteins are particularly susceptible to oxidation. meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used.  in this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. these features are: sequential distance to the nearby cysteines, pssm profile and predicted secondary structure of flanking residues. after further feature selection using svm-rfe, we developed redox-sensitive cysteine predictor, a svm based classifier for redox-sensitive cysteine prediction using primary sequence only. using 10-fold cross-validation on rsc dataset, the accuracy, sensitivity, specificity, mcc and auc were estimated as,,, and, respectively. when evaluated using 10-fold cross-validation with balosctdb dataset which has structure information, the model achieved performance comparable to current structure-based method. further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins.  in this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. the major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords reactive oxygen speciesredox-sensitive cysteinepost-translational modificationsupport vector machinesvm-based recursive feature eliminationhong kong university grants committeehkbu1/crf/10guo dianjing issue-copyright-statement© the author 2016 