Identification is the covalent addition of a

Identification of protein S-Nitrosylation sites using composition of amino acid
frequency

Abstract

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S-nitrosylation is the covalent addition of a nitrogen monoxide group to the thiol adjacent chain of
cysteine which is a form of post-translational protein modification, which can associate with stroke,
cancer and a number of chronic degenerative diseases, neurodegeneration, including Parkinson’s and
Alzheimer’s disease and Amyotrophic Lateral Sclerosis. Identification of S-nitrosylation site is important
for understanding the function of S-nitrosylation. However, the S-nitrosylation site is mainly difficult to
detect because the experimental approaches are often time-consuming and labor intensive. Thus, an
accurate computational method for identifying S-nitrosylation sites may help researchers to design their
experiments and to understand the molecular mechanism of S-nitrosylation. In this study, a computational
method has been developed to predict protein S-nitrosylation sites by including CKSAAP and binary
encodings .Then, a random forest classifier was trained with these features. Our proposed method
achieves an AUC score of 0.782 in the 10-fold cross-validation set for 1:1 ratio of positive and negative
samples and performs significantly improve in both cross-validation and independent test. Thus, the
proposed method would be helpful computational resource for predicting S-nitrosylation sites.
Keywords: S-nitrosylation, CKSAAP encoding, Binary encoding, Random forest, Feature selection.

Introduction
Nitric oxide (NO) and related nitrogen species which are considered reactive can mediate various
post translational modifications (PTMs), for example metal nitrosylation, tyrosine nitration, and
cysteine S-nitrosylation. Cysteine S-nitrosylation term is used to describe the covalent binding of
an NO group to a protein cysteine (Cys) residue. S-Nitrosylation is a form of post-translational
protein modification with similarities to phosphorylation. This PTM is important molecular
mechanisms by which NO regulates protein functions and has been shown to alter protein
activities, protein-protein interactions, and subcellular localization under both normal and
pathological conditions 1–4. MS-based proteomics approaches have been developed for the

identification of S-nitrosylated proteins and their modification sites from complex biological
samples 5, 6. By trans-nitrosylation this molecule can transfer its NO moiety to protein cysteine
residues. GSNO is often used to generate S-nitrosylated proteins in removes for the subsequent
isolation and identification of S-nitrosylated proteins 7–11. The identification of redox-
sensitive cysteine residues is the most important to understad the regulatory functions of NO.
Latest reports have suggested that S-nitrosylation can modulate protein stability 12 and
trafficking 13,14, and play an important role in a variety of biological processes, including
transcriptional regulation 12, apoptosis 15, and chromatin remodeling 16. Moreover,
aberrant S-nitrosylation has been implicated in numerous diseases 15,17,18. S-Nitrosylation
meets the criteria for validation as a cell signaling mechanism in that it is stimulus
evoked19, precisely targeted20, reversible21, spatiotemporally restricted2223 and
necessary for specific cell responses24. The first protein whose activity was shown to be
regulated by S-nitrosylation in this fashion was the NMDA-type glutamate receptor in the
brain2526. S-Nitrosylation has since been shown to be ubiquitous in biology, having been
demonstrated to occur in all phylogenetic Kingdoms2728 and has been described as the
prototypic redox-based signalling mechanism29, hypothesized to have evolved on primordial
Earth30. The reverse process of S-nitrosylation is termed denitrosylation, which in addition to
occurring spontaneously in the presence of metal ions and under conditions of photolysis31,
has recently been demonstrated to be an enzymically controlled process. S-Nitrosoglutathione
reductase (GSNOR), which accelerates the decomposition of S-nitrosoglutathione (GSNO) and
other SNO-proteins, is an alcohol dehydrogenase class III isoenzyme which has been shown to
be conserved from bacteria to humans32. Similarly, the thioredoxin/thioredoxin reductase
system catalyzes the denitrosylation of a number of S-nitrosoproteins333435. Aberrant or
dysregulated denitrosylation or S-nitrosylation has been associated with stroke (cerebral
ischemia)36 and a number of chronic degenerative diseases, including Parkinson’s and
Alzheimer’s disease37-40 and Amyotrophic Lateral Sclerosis (ALS)41. There is an emerging
role of S-nitrosylation in cancer biology42. S-Nitrosylation of EGFR and Src activates an
oncogenic signaling network in human basal-like breast cancer 43. Some research groups have
attempted to define consensus motifs for S-nitrosylation by relating the amino acid sequences
around identified target cysteine residues. Such analyses have shown that the target cysteine
residues often lie within an acid-base or hydrophobic motif 45. In difference, other studies have
discovered that the primary sequence of the surrounding amino acid residues has no significant
effect on the reactivity of cysteines to S-nitrosylation at the peptide level 46. Recently, 70
known S-nitrosylated sites have been used to identify general structures associated with S-
nitrosylation. The results obtained shown that proximal acid– base motif, Cys pKa, sulfur atom
exposure, and Cys conservation or hydrophobicity in the vicinity of the modified cysteine do not
predict S-nitrosylation specificity. In its place, this analysis identified a revised acid-base motif
that is located farther from the cysteine and in which the charged groups are exposed 47. By the
way, experimental identification of S-nitrosylated proteins together with their sites would serve
as a foundation to understand the molecular mechanisms and regulatory roles of S-nitrosylation.
Currently, computational studies of post-translational modifications (PTMs) are attracting
considerable attention. In difference with time-consuming and expensive experimental methods,
certain of the accurate and convenient computational approaches have been shown to be able to
rapidly generate helpful information for further experimental verification.

Materials and Methods
Data collection
First, 1356 amino acid sequences were downloaded from the most recent version of the Mus
Musculus information resource Data Base of cysteine s-nytrosylation
(http://140.138.144.145/~dbSNO/download.php) In this collected data have 2641 experimentally
verified S-nitrosylation sites in 1356 proteins. The number of S-nytrosylated proteins 3351 and
number of cysteine S-nytrosylation instances 3259 in this data.
Data preparation
In this study ,the experimentally validated s-nytrosylation sites (Cysteine residues) were
considered as positive samples, while all the remaining cysteine residues that have not been
verified as s-nytrosylation sites in these proteins were considered as negative samples (i.e. non-
s-nytrosylation sites). Each site was represented as a sequence fragment with cysteine in the
center, which includes 1356 s-nytrosylation proteins covering 2641 positive and 10394 negative
sites. We are taken training data as 80% data and test data as 20% . The numbers of positive and
negative samples are highly imbalanced in the original training dataset. Therefore, a relatively
balanced dataset with a 1:1 ratio of positives to negatives (i.e. 2641 positive sites and 2641
randomly selected negative sites) was compiled to train our predictor. It can be noted that for the
independent test set, the corresponding proteins with all positive and negative samples were
used. However, for the data set, only a 1: 1 ratio of the positive vs. negative samples was
assessed.

Fig. 1: The s-nytrosylation predictor pipeline. The datasets were collected from several published
database.At first total data set divided by two set between training and independent data set. For
the remaining proteins, a 1:1, 1:2, 1:3 ratio of s-nytrosylation vs. non- s-nytrosylation sites was
selected to construct the dataset. Then we select window and feature. After encoding two types
of features, the RF was utilized to build the classifier. Then, the best model was built after
parameter optimization and performance evaluation.

DATABASE Downloaded dataset
Positive vs. negative
Window size selection Feature selection
Encoding Encoding
CKSAAP
Binary

Random forest
Cross-validation Parameter optimization
Performance (Sn, Sp, Ac,
Mcc and AUC)
Statistical learning
model
Independent data set Training dataset

Performance assessment
To investigate the performance of the proposed s-nytrosylation predictor, we used four
measurements, including sensitivity (Sn), specificity (Sp), accuracy (Ac), and Mathew
Correlation Coefficient (MCC) to evaluate the prediction performance of GPS-SNO. The four
measurements were defined as below:
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���+���

���=��
��+����

����=���+��
���+����+��+���

�����=(������)?(������)
?(���+���)×(���+���)×(���+���)×(���+���)

where TP represents the number of correctly predicted s-nytrosylation windows, TN is the
number of correctly predicted non s-nytrosylation windows, FP is the number of incorrectly
predicted s-nytrosylation windows and FN is the number of incorrectly predicted non- s-
nytrosylation windows. The values of all of these measurements lie between 0 and 1, and a
higher value represents a better prediction. In addition, we also used the receiver operating
characteristics (ROC) score for calculating the performance of the proposed prediction 49, 50.
To evaluate the performance of the proposed predictor using the performance measures (Sn, Sp,
Ac and MCC) as early mentioned earlier, a five-fold cross validation (CV) was used to
investigate the performance of the proposed method based on the two encoding methods
(CKSAAP and Binary). In addition, at last we used 5-fold, 10-fold and 15-fold CV to
investigate the performance of the proposed method based on the feature selection and without
feature selection. For the 5-fold CV, the original datasets were randomly and equally divided into
5 subgroups. Among the 5 subgroups, one subgroup was singled out as the test dataset and the
remaining 4 subgroups were considered as the training dataset. Then, we computed all the
performance measures for each predictor. We replicated this procedure 5 times by changing
datasets from the 5 subgroups. Finally, we computed the average value for each performance
measure for each predictor.

CKSAAP encoding
CKSAAP is one of the most classical encoding methods and was initially developed by Chen et
al51. It has been widely used in several bioinformatics tasks 52–58. The procedure of
CKSAAP is briefly described as follows. If a sequence fragment was composed of a window size
2r + 1 and 21 types of amino acids (including the gap (O)), it may contain (21× 21) = 441 types
of amino acid pairs (i.e. AA, AC, AD,. . ., OO) for every single k (k denotes the space between
two amino acids). For example, ”AXXA”, whose k-space number is equal to 2. In this study,
the optimal kmax was set at 5, indicating that 21× (kmax + 1) × 21 = 2646 different amino acid

pairs were collected to calculate the corresponding feature vector for each sequence. The feature
vector was then calculated using the following
equation :
(
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�����������
,
����
�����������
,
����
�����������
, … ,
�����
�����������
)441
Where Ntotal is the length of the total composition residues (for example, if the fragment residue
with a length L is 29 and k = 0, 1, 2, 3, 4, 5 then Ntotal = L – k -1 will be 28, 27, 26, 25, 24 and 23,
respectively). �����, �����,. . ., ��� represent the frequency of the amino acid pair within the
fragment.
Binary encoding
In order to make a robust predictor, binary amino acid encoding was considered to calculate the
positional information from the corresponding sequence fragments. In this study, 21 (including
gap (O)) amino acids were transformed into numeric vectors by adopting a binary vector. The 21
types of residues were ordered as ACDEFGHIKLMNPQRSTVWYO. For adopting binary
vector, in query proteins, A was represented as 1000000000000000000000 and C as
01000000000000000000, and so on. The selected window size of surrounding s-nytrosylation
sites was 27. For the query proteins of s-nytrosylation sites, the center position was always C.
Thus, it was not considered to be taken into account. Finally, the feature vectors with a
dimensionality (21×26) = 546 were obtained from the binary encoding
Random forest classifier
RF classifier is a collection of decision tree classifiers, where in each tree is trained with a
randomly selected subset of samples. The decision tree is grown as follows. Suppose N samples
are randomly selected with replacement from the F features, then the best split node is selected
from F features. Finally, the decision tree is grown as large as possible without cutting. In the
construction of the forest, it is generalized based on most votes given by all the individual trees;
within the post for the error estimate it does not produce bias. It is relatively robust to noise and
outliers 59. As a supervised learning algorithm, it has been widely used in protein
bioinformatics 60–62. The predicted result of the RF was decided by voting among the number
of trees, which contains two classes, either positive samples (s-nytrosylation sites) or negative
samples (non- s-nytrosylation sites). In this study, the RF algorithm was implemented using the
‘Random Forest’ R package.
Feature encoding
In statistics, feature selection, also known as variable selection, attribute selection or variable
subset selection, is the process of selecting a subset of relevant features (variables, predictors) for
use in model construction. In order to build an effective prediction model, we encoded each
sequence fragment into a numeric vector, which was the crucial step to present the classifier and
group architecture. Thus, a high-quality sequence encoding method for keeping the generated
code compact in dimensionality was necessary. Instead of employing a simple binary
representation, three types of amino acid feature encodings were adopted, including CKSAAP

and binary . As mentioned in ”Feature encoding”, each investigated s-nytrosylation or non s-
nytrosylation fragment was encoded into as a high dimensional vector. Therefore, they may not
equally contribute to determine the surrounding s-nytrosylation or non s-nytrosylation sites.
Results and discussion
Prediction capabilities of the different encoding features with a RF
classifier
In nature, the s-nytrosylation and non- s-nytrosylation datasets are highly unbalanced. It has been
established that statistical learning algorithms become computationally intractable and the
accuracy is strongly affected due to the nature of the unbalanced datasets. To address this issue,
many PTM site prediction studies employ a relatively balanced ratio between the positive and
negative samples during the training of the classifiers (e.g. the ratio of positives versus negatives
is controlled at 1:1 or 1: 2) 53, 58, 61. After evaluating the different ratios of s-nytrosylation
and non- s-nytrosylation peptides, a comparatively balanced training dataset (1:1 ratio of positive
vs. negative) was used to develop the RF-based predictor of s-nytrosylation. In order to evaluate
the performance capability of the different encoding features, two single feature encoding-based
(CKSAAP and Binary) table1. The performance of these sensitivity (Sn), specificity (Sp),
accuracy (Ac), and Mathew Correlation Coefficient (MCC) was assessed using a 5-fold cross-
validation. To obtain the sensitivity (Sn), specificity (Sp), accuracy (Ac), and Mathew
Correlation Coefficient (MCC) score we use 21, 27, 31 window size for the CKSAAP and binary
encodings. The AUC performance values on the datasets are given in Table 1.

Table 1: Performance of the ratio and CKSAAP and binary encodings on the training dataset

CKSAAP
Window size ratio fold Sn Sp Ac Mcc AUC
21 1:1 5 0.6435397 0.677635 0.6587776 0.3193598 0.726
21 1:2 5 0.6821918 0.6873276 0.6870404 0.1800967 0.708
21 1:3 5 0.7288136 0.7633444 0.7624081 0.1845999 0.690
27 1:1 5 0.6548745 0.6959917 0.6730239 0.3484487 0.743
27 1:2 5 0.6971279 0.6893409 0.6897978 0.1926642 0.724
27 1:3 5 0.6766917 0.7634511 0.7607996 0.1749583 0.710
31 1:1 5 0.6605617 0.7081794 0.681296 0.3656536 0.753
31 1:2 5 0.6958525 0.6924844 0.6927083 0.2052253 0.736
31 1:3 5 0.6654412 0.7634013 0.7603401 0.172317 0.720
BINARY
Window size ratio Fold Sn Sp Ac Mcc AUC
21 1:1 5 0.5031915 0.5008792 0.5013787 0.003350293 0.500
27 1:1 5 0.5826087 0.5022422 0.5043658 0.02721921 0.503
31 1:1 5 0.6148148 0.5039127 0.5073529 0.04116815 0.503

In this table indicate that the highest score of the AUC
0.726(Sn=0.6435397,Sp=0.677635,Ac=0.6587776,Mcc=0.3193598) for 21 window size,
0.743(Sn=0.6548745,Sp=0.6959917,Ac=0.6730239,Mcc=0.3484487) for 27 window size and

0.753 (Sn=0.6605617,Sp=0.7081794,Ac=0.681296,Mcc=0.3656536) for 31 window size in
CKSAAP encoding . So when we are taken 1:1 ratio then the AUC score is better than the ratio
1:2 and 1:3.So the 1:1 ratio provides most accurate predictions of s-nytrosylation sites. And
another issue is the encoding, in above result we see that the AUC score for CKSAAP encoding
is better than binary encoding. Therefore, CKSAAP encoding provides most accurate predictions
of s-nytrosylation sites. Another issue is the optimal size of the sequence windows flanking the s-
nytrosylation and non- s-nytrosylation sites.

Table2: Performance of the window size on the training dataset
Window size ratio fold Sn Sp Ac Mcc AUC
19 1:1 5 0.6402789 0.6786834 0.6571691 0.3166418 0.724
21 1:1 5 0.6435397 0.677635 0.6587776 0.3193598 0.726
23 1:1 5 0.6433306 0.6785346 0.6590074 0.3199341 0.732
25 1:1 5 0.6552873 0.6912821 0.6714154 0.344695 0.740
27 1:1 5 0.6548745 0.6959917 0.6730239 0.3484487 0.743
29 1:1 5 0.6670797 0.709477 0.6858915 0.3741623 0.754
31 1:1 5 0.6605617 0.7081794 0.681296 0.3656536 0.753
The optimal window size was decided based on the analysis table2. AUC value of the
consecutive CKSAAP encoding methods mentioned above. The performance of the different
window size is shown (table 2) .We observed that the model with a window size of 29 was
optimal to discriminate s-nytrosylation and non- s-nytrosylation sites.
Sequence specificity of S-nitrosylation site
The proposed s-nytrosylation combines the positional amino acid encoding properties. For the
dataset, the amino acid propensities of surrounding s-nytrosylation sites compared to the non- s-
nytrosylation sites were displayed by Two Sample Logos software64 (Fig. 1). Briefly, in the
two sample logo, only over- or under-represented residues at each position are plotted above and
under the X-axis, respectively. The height of the letter was in proportion to the percentage of
positive (if over-represented) or negative samples (if under-represented) harboring the
corresponding residue. The Y-axis reports the cumulative percentage of these over-/ under-
represented residues. We can see that some amino acids are over-/underrepresented in the
specific positions (Fig.1), which indicates that the positional amino acid encoding was an
efficient method to identify the s-nytrosylation sites. But, positional based encoding is not
enough to accurately predict the s-nytrosylation Sites, as demonstrated by the performance of
binary encoding in s-nytrosylation .
.

Fig. The amino acid propensities of surrounding s-nytrosylation sites compared to non- s-
nytrosylation sites, as displayed with the Two Sample Logos software. It also shows that the
position between the compositional amino acids of the s-nytrosylation and non- s-nytrosylation
peptides had a wide difference, especially those located in the positions from -13 to -1 and +1 to
+13.
Feature optimization
The feature selection method was performed to analyze the most informative features of the
surrounding s-nytrosylation substrates. Although the feature selection method did not result in an
improvement in performance, it allowed us to collect the most important features from the
corresponding encoding methods according to importance.

Table 3 (a): Performance of the gain and correlation feature selection method for 1:1 ratio and 29 window
size on the training data set.

Gain Feature selection
Features
sizes
ratio fold Sn Sp Ac Mcc AUC
100 1:1 5 0.6349745 0.6593186 0.6461397 0.2932846 0.707
200 1:1 5 0.6677686 0.7101449 0.6865809 0.3755301 0.759
300 1:1 5 0.6845804 0.7309881 0.705193 0.4129691 0.777
400 1:1 5 0.6800821 0.7287428 0.7015165 0.4059187 0.776
500 1:1 5 0.6799678 0.7386424 0.705193 0.4144777 0.778
600 1:1 5 0.6845528 0.7399577 0.7086397 0.4208795 0.772
700 1:1 5 0.6811594 0.7258647 0.701057 0.4045616 0.767
800 1:1 5 0.6770665 0.7319533 0.7008272 0.4053203 0.775
900 1:1 5 0.6706587 0.7314564 0.6964614 0.3974924 0.771
1000 1:1 5 0.6782219 0.73 0.7008272 0.4049248 0.770

Correlation Feature selection
Features
sizes
ratio fold Sn Sp Ac Mcc AUC
100 1:1 5 0.672089 0.6994048 0.6847426 0.3704882 0.749
200 1:1 5 0.6814664 0.7348445 0.7047335 0.4128747 0.771
300 1:1 5 0.6857143 0.7392219 0.7090993 0.4215539 0.780
400 1:1 5 0.6821327 0.7361478 0.7056526 0.4147781 0.777
500 1:1 5 0.6810943 0.7330531 0.7038143 0.4108751 0.778
600 1:1 5 0.6837782 0.7334377 0.7056526 0.41425 0.778
700 1:1 5 0.688317 0.7421218 0.7118566 0.4270628 0.776
800 1:1 5 0.676045 0.7361634 0.7017705 0.407902 0.772
900 1:1 5 0.6818923 0.7347368 0.7049632 0.4132642 0.782
1000 1:1 5 0.6696574 0.7189474 0.6911765 0.3854662 0.761

From above tables we see that all features are very important to show better performance.
But when we take top 500 features in this study, we get better result(AUC score =0.778,
Sn=0.6799678, Sp=0.7386424, Ac=0.705193, Mcc=0.4144777) than other number of features
in gain feature selection method for 29 window size and when we take top 900 features in
this study, we get better result(AUC score = 0.782, Sn=0.6818923,Sp=0.7347368,
Ac=0.7049632, Mcc=0.4132642) than other number of features in correlation feature selection
method for 29 window size. So , we get from the result that the AUC score is improved by
feature selection and correlation feature selection method for 1:1 ratio ,29 window size and 5
fold cross validation is important to identify s-nytrosylation sites.
Table 4 : Performance of the 5-fold, 10-fold, 15-fold cross validation in 1:1 ratio and 29 window size for
before feature selection and after feature selection on the training data set

From above tables we see that without feature selection in 15–fold (AUC score =0.767,
Sn=0.6766763, Sp=0.7235815, Ac=0.6973805, Mcc=0.3974999) are very important to show
better performance and after feature selection in 10–fold (AUC score =0.782 , Sn=0.6871921,
Sp=0.7379958, Ac=0.7095588, Mcc=0.4221419) are very important to show better
performance.

feature ratio windo
w
fold Sn Sp Ac Mcc AUC
Before feature 1:1 29 5 0.6670797 0.709477 0.6858915 0.3741623 0.754
Before feature 1:1 29 10 0.6672151 0.7116085 0.6868107 0.3762135 0.762
Before feature 1:1 29 15 0.6766763 0.7235815 0.6973805 0.3974999 0.767
After feature 1:1 29 5 0.6818923 0.7347368 0.7049632 0.4132642 0.782
After feature 1:1 29 10 0.6871921 0.7379958 0.7095588 0.4221419 0.782
After feature 1:1 29 15 0.6894156 0.7433071 0.7130055 0.4293537 0.780

Table 5: Performance of the 15-fold cross validation in 1:1 ratio and 29 window size for before feature
selection and 10-fold cross validation in 1:1 ratio and 29 window size after feature selection on the test
data set
feature ratio windo
w
fold Sn Sp Ac Mcc AUC
Before feature 1:1 29 15 0.7222222 0.7629108 0.7408602 0.4834237 0.834
After feature 1:1 29 10 0.7698413 0.8192488 0.7924731 0.5870145 0.870

From above tables we see that without feature selection in 15–fold (AUC score =0.834,
Sn=0.7222222, Sp=0.7629108, Ac=0.7408602, Mcc=0.4834237) CV are very important to
show better performance for both test and training dataset and after feature selection in 10–
fold (AUC score =0.870, Sn=0.7698413, Sp=0.8192488, Ac=0.7924731, Mcc=0.5870145)
are very important to show better performance for both test and training dataset.
Conclusions
In this article, we designed a simple and efficient predictor s-nytrosylation for identifying s-
nytrosylation sites. For class prediction in the independent dataset, we observed that our
proposed predictor performed better than the existing predictors. Moreover, a feature selection
was carried out to identify the significant features, which helps to better understand the important
rules that underlie the s-nytrosylation proteins. The data analysis results demonstrated that the
proposed method might be helpful to understand s-nytrosylation as well as the mechanisms of
protein s-nytrosylation. In addition, we also provide a new s-nytrosylation sites database that
contains 1356 experimentally verified s-nytrosylation proteins with 2641 cysteine s-nytrosylation
sites. If we take correlation feature selection method, 1:1 ratio, 29 window size and 10-fold
cross validation then we get optimal performance to identify s-nytrosylation sites.

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