Quantitative Structure Activity Relationship Models for the Antioxidant Activity of Polysaccharides
Quantitative Structure Activity Relationship Models for the Antioxidant Activity of Polysaccharides
Zhiming Li☯ 0 1
Kaiying Nie 0 1
Zhaojing Wang 0 1
Dianhui Luo 0 1
0 Department of Bioengineering and Biotechnology, Huaqiao University , Fujian Xiamen, 361021 , China
1 Editor: Kalimuthusamy Natarajaseenivasan, Bharathidasan University , INDIA
In this study, quantitative structure activity relationship (QSAR) models for the antioxidant activity of polysaccharides were developed with 50% effective concentration (EC50) as the dependent variable. To establish optimum QSAR models, multiple linear regressions (MLR), support vector machines (SVM) and artificial neural networks (ANN) were used, and 11 molecular descriptors were selected. The optimum QSAR model for predicting EC50 of DPPH-scavenging activity consisted of four major descriptors. MLR model gave EC50 = 0.033Ara-0.041GalA-0.03GlcA-0.025PC+0.484, and MLR fitted the training set with R = 0.807. ANN model gave the improvement of training set (R = 0.96, RMSE = 0.018) and test set (R = 0.933, RMSE = 0.055) which indicated that it was more accurately than SVM and MLR models for predicting the DPPH-scavenging activity of polysaccharides. 67 compounds were used for predicting EC50 of the hydroxyl radicals scavenging activity of polysaccharides. MLR model gave EC50 = 0.12PC+0.083Fuc+0.013Rha-0.02UA+0.372. A comparison of results from models indicated that ANN model (R = 0.944, RMSE = 0.119) was also the best one for predicting the hydroxyl radicals scavenging activity of polysaccharides. MLR and ANN models showed that Ara and GalA appeared critical in determining EC50 of DPPH-scavenging activity, and Fuc, Rha, uronic acid and protein content had a great effect on the hydroxyl radicals scavenging activity of polysaccharides. The antioxidant activity of polysaccharide usually was high in MW range of 4000±100000, and the antioxidant activity could be affected simultaneously by other polysaccharide properties, such as uronic acid and Ara.
Data Availability Statement: All relevant data are
within the paper.
Funding: This work was supported by Promotion
Program for Young and Middle-aged Teacher in
Science and Technology Research of Huaqiao
University (ZQN-PY316), National Nature Science
Foundation of China (31201314), and Cultivation
Project Funds for Postgraduates Innovation Ability
of Huaqiao University. The funders had no role in
study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
In our normal metabolism process, oxygen free radicals and non-oxygen free radicals are
continuously produced, and lower concentrations of free radical can play a crucial role in regular
physiological functions [
]. However, many diseases, such as cardiovascular diseases,
diabetes, aging and cancer, can be conducted by unregulated overproduction of free radicals [
Competing Interests: The authors have declared
that no competing interests exist.
Thus, it is essential to develop natural and effective antioxidants [
]. Previously reports
revealed that many natural polysaccharides possess potent scavenging activities of free radicals
and can be used as potential antioxidants [
]. It is always impossible to obtain a large
quantity of experimental data because of a lack of perfect data sites, and so the study on
relationship between bioactivities and the properties of polysaccharides by model forecast
approach was relatively poor .
The quantitative structure-activity relationship (QSAR) model, which use relevant
molecular physico-chemical properties to predict important treatment responses, is considered as an
alternative to the experimental evaluation [
]. It has gained increasingly attention, and a
variety of QSAR methods have been developed for water treatment process selection, membrane
separation and adsorption etc [
To date, QSAR models for predicting the bioactivities of polysaccharides have seldom been
developed. A study reported the relationship between monosaccharide composition ratio and
macrophage stimulatory activity by model forecast approach [
]. To obtain theoretical
supports for applications of polysaccharides from natural products, the main aim of this work was
to establish reliable soft measurement models to predict performance and study the
relationship between polysaccharide properties and antioxidant activities of polysaccharides by QSAR.
In our QSAR studies, multiple linear regression (MLR) method, and the nonlinear methods
including artificial neural network (ANN) and support vector machine (SVM) were used.
Materials and Methods
The present study showed that the antioxidant activity of polysaccharide has related with
many factors, including monosaccharide composition [
], uronic acid (UA), molecular weight
(MW), protein content (PC) and sulfate group content et al [
]. In the data selection, we
chose natural purified polysaccharides without sulfate groups to study QSAR models for
predicting antioxidant activities of polysaccharides. A various set of polysaccharides and their
antioxidant activities were collected from different published papers [
activities of polysaccharides were represented by the 50% effective concentration (EC50). To set
up a more reliable model, we selected 141 compounds. The detailed publication lists with
corresponding antioxidant activities and compounds were given. The normalization process was
adopted in the distribution of the parameters with 2 as the bottom of the log logarithm, and
MW was divided by 10000 in the normalization process.
In models, a training data set was applied to develop the model. A test set, which was never
included during their development, was used to validate the predictive power of model [
]. The training set and test set were chosen by random distribution.
The structure of polysaccharide was complex and could be represented by variety of
descriptors. However, the major composition of polysaccharide was monosaccharide joined together
by glycosidic bonds, which was essential to their bioactivities, so we used monosaccharide
composition as descriptors. The following descriptors of monosaccharide composition were
considered for modeling EC50 values in MLR, ANN and SVM analysis. Descriptors of
monosaccharide composition: rhamnose (Rha), arabinose (Ara), mannose (Man), glucose (Glc),
galactose (Gal), fucose (Fuc), xylose (Xyl), ribose (Rib), glucuronic acid (GlcA) and
galacturonic acid (GalA). Usually, gas chromatography (GC) and high-performance liquid
chromatography (HPLC) were performed for the identification and quantification of monosaccharide
composition. For HPLC analysis, glucuronic acid (GlcA) and galacturonic acid (GalA) could
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not be identified. Thus, total uronic acid (UA) could be determined by other methods, such as
the sulfuric acid carbazole method, and then UA was also used as a descriptor in our models.
The descriptors of PC and MW were also adopted in models. STATISTIC.10 method was used
to establish SVM, MLP and ANN models, and the picture was drawn by using RStudio
(Version 0.99.902–2009–2016 RStudio, Inc.).
Linear model generation
There were primarily two different approaches for choosing a descriptor subset in MLR, and
they were filter and wrapper methods. The procedure of filter method was that setting and
filtering descriptors were supposed to generate the top priority subset before training. However,
the learning algorithm was wrapped into the selection procedure in the wrapper method [
In MLR, we used wrapper as the target learning algorithm. The training data set was applied
only for selecting descriptor. At first, we employed a two-dimensional research method. It was
a combination of forward and backward search. Then we assessed the selected descriptors on
the target learning algorithm. In the learning process, we used 10 fold cross validation method.
In stepwise MLR analysis, we selected training descriptor sets and then established a linear
Artificial neural network and Support vector machines
It was appropriate for artificial neural network (ANN) to model nonlinear relationship. We
can find many reviews about ANN research and its application in QSAR studies [
this study, we employed multi-layer perceptron (MLP) [
] and three layer reverse
Back-Propagation (BP) network. In the back-propagation ANN, we utilized the technique of supervised
learning, and the trained network was trained by minimizing the squared error of the network’s
output. The first step of training model was to confirm the number of layers and neurons in
each player. The second step was to optimize the learning rate as well as momentum
parameters. In the input layer, the architecture of the network was composed of eleven neurons, which
were the eleven relative descriptors chosen. In the output layer, there was one neuron, i.e. EC50
values of the antioxidant activity. In all the layers, logistic function was applied. In the hidden
layer, through changing the number of neurons, we got the lowest RMSE and highest
correlation coefficient. We applied 30% of the training data set for verification. The verification was
employed to hinder from the over fitting. All of optimization process were taken with 10 fold
cross validation [
Support vector machines (SVM) was originally developed for the classification problem,
and SVM has been used to solve nonlinear regression estimation. Nowadays, SVM has
demonstrated much success in QSAR and quantitative structure-property relationship (QSPR) studies
]. We selected support vector machine classifier method (epsilon-SVM) which was most
commonly used in QSPR and QSAR studies to optimize the value of kernel parameter g
Validation techniques and model performance evaluation
We used a 10 fold cross validation technique. This procedure divided the data set into 10 folds
or groups, created the model using 9 of the sets, and tested it on the remaining group. When
the procedure was repeated, each of the 10 groups had served as a test group. The root mean
square error (RMSE) was calculated, averaged, and then used to evaluate the predictive
performance of three models.
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Results and Discussion
Models for the DPPH scavenging activity of polysaccharides
The data was divided into two parts using random classification. One was the training set, the
other was the test set. The entire data set including 74 compounds was divided into two
clusters. The test set of 22 compounds was chosen randomly from this cluster, and the remaining
compounds were used as the train set. Compound number 4, 5, 7, 9, 17, 20, 25, 30, 31, 32, 33,
34, 36, 38, 44, 54, 57, 62, 63, 64, 69,73 were selected as the test set, and the rest of the
compounds were the train set. The test set and train set were given in Table 1. The data distribution
of parameter was shown in Fig 1, the data distribution was uniform, and no other single
variable values was close to EC50 values distribution (-6, 2). The shape of data distribution from
EC50 and Ara was similar, which indicated that there was a certain relation between them. In
addition to MW, other physical quantities were all the components of polysaccharides, so MW
was used to establish the model by itself.
In this study, the training data set of 52 compounds was used. A stepwise linear regression
analysis was used to determine the relationship between the dependent variable of EC50 and
the independent variables of uronic acid (UA), protein content (PC) and monosaccharide
compositions (Rha, Ara, Man, Glc, Gal, Fuc, Xyl, GlcA and GalA). To achieve this goal, regression
analysis was implemented by using the forward stepwise. In stepwise regression procedures,
the first was to choose the most correlated independent variable, and then to select
independent variable which was most correlated with the remaining variance in the dependent variable.
This procedure was to increase the additional independent variable with R-squared (R2) which
was not changing until a significance of at least 80%. Accordingly, the variables of Ara, GalA,
GlcA and PC were included in the regression model. The relationship between the matrix of
parameters and EC50 was shown in Fig 2. One variable data was used as the abscissa, another
variable data was used as ordinate, and all points had been portrayed by the matrix scatter plot.
From the diagonal we can see that the distribution of the data was all similar in shape. Fig 3
showed the correlation between model parameters and EC50, and the proportion of Ara, GalA
and GlcA accounted 0.51, 0.39 and 0.35, respectively, which indicated that they had the most
effect on EC50. In Fig 3, we can see that EC50 had a positive correlation with Ara and PC, and it
has negative correlation with GalA and GlcA, which was consistent with the model given in
equation. The regression Eq 1, which could be obtained through the statistical analysis, was as
follows. Because the effect of UA on EC50 was little, UA was not added to the model equation.
The linear model selected four major relevant descriptors, and gave a stable model with
R = 0.807 and RMSE = 0.423.
In the model, R value was 0.807 (p <0.001), fit indicators of the model were acceptable, the
model was coincided with the data structure, and Ara, GalA, GlcA, PC and EC50 were
significant correlation. The predicted EC50 values of the training and test set by using the MLR
equation were given in the Table 2. Predicted values and experimental values of EC50 in two sets of
data were plotted and shown in Fig 4. Most of the data were distributed from 0 to 1.5, and
there were some predicted and negative values existing in the left lower corner. The
experimental values of these negative values were between 0 and 0.2, which could be accepted.
Experimental values and predicted points were distributed in two sides of the curve fitting, and
most point of test set distributed among the prediction set, which illustrated that the
establishment of training set used for the multiple regression model was very good to predict the
numerical value of test set. The above linear model was applied to predict the 22 test data set,
and these test data were never used in model building. The result showed R = 0.872,
RMSE = 0.361 and p = 1.245E-7, which showed that there was a significant correlation.
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Fig 1. Data distribution of parameter.
Multiple linear regressions (MLR) established the relationship between the dependent variable
of EC50 and the independent variable of polysaccharide properties. The results showed that the
statistics for MLR equation were good, and it also offered some views about the polysaccharide
properties influences on DPPH-scavenging activity of polysaccharides.
Fig 2. Correlation between the matrix of parameters and EC50 value of the DPPH scavenging activity.
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Fig 3. Proportion of the parameters effecting on EC50 value of the DPPH scavenging activity.
Polysaccharide properties were considered as the input layer node in neural networks, and
EC50 values of the DPPH-scavenging activity was the output layer node. Numbers of nodes
had a great influence on the test results. The optimization was done with 10 fold cross
validation, and 30% of test data were used for validation. Selected parameters of the number of
neurons in the hidden layer were optimized by changing from 4 to 14, and it was worthy to
mention that the initial value of 7 selected was optimal. The selected network adopted
Broyden Fletcher Goldfard Shanno (BFGS) algorithm which was still seen as the best
Quasi-Newton algorithm. When the entire training data was trained in the network with the optimized
parameters, it gave R = 0.96 and RMSE = 0.018. The experimental and predicted values of
EC50 for the train data using the ANN model were plotted and shown in Fig 5. The
experimental value was abscissa, the point distribution of the prediction value for the y-coordinate
was on both sides of the curve fitting from 0 to 1.5, and the point distribution was uniform
and closed to each other. According to the view of point, the density of horizontal and
vertical coordinates and the fitting effect were perfect. The predicted values of EC50 for the train
and test data were given in the Table 2. The test set was used for prediction and gave R =
0.933 and RMSE = 0.055.
We selected radial basis function (RBF) kernel for function modeling in SVM, the best
parameter C, g and ε were selected by using 10 fold cross validation, a SVM model was obtained by
training the whole training set, and then the model was used for the test set. By varying the
parameter values in the training set systematically, we optimized SVM parameters, and
calculated RMSE of the model. The parameter value which gave the lowest RMSE was selected. The
regularization parameter C controlled the alternate use between maximizing the margin and
minimizing the training error. If the value of C was too small, then there was not sufficient
stress on fitting the training data. To have a stable learning procedure, a large value of C should
be set up first . To discover an optimal value of C, the RMSE of SVM model with different
C values was calculated. Then, this value C = 9 was selected as the optimal value. We achieved
the selected parameters (g = 0.091, ε = 0.1, C = 9) and the final training running in the whole
training set, and EC50 of the DPPH-scavenging activity was predicted. The predicted EC50 on
the basis of this model was plotted and shown in Fig 6 and Table 2. The statistical parameters
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of this model were R = 0.851 and RMSE = 0.151 for the training set, and the test set was used
for prediction and gave R = 0.865 and RMSE = 0.144.
Comparison of MLR, ANN and SVM models
The statistical parameters obtained from the investigative models for train and test set were
shown in Table 3. The error estimates were applied to model performance evaluation, and
RMSE were lower for nonlinear models (SVM, ANN) generated by the machine learning
methods than that by multiple linear regression. The correlation coefficients (R) given by SVM and
ANN models were also higher than that by multiple linear regression. The above results
indicated that the performances of nonlinear models SVM and ANN were better than that of a
linear MLR model for the prediction of DPPH-scavenging activity of polysaccharides. The
comparison of the nonlinear models demonstrated that ANN model accurately predicted the
relationship between polysaccharide properties and the DPPH-scavenging activity for the train
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Fig 4. A comparison of experimental vs predicted EC50 using MLR method.
data set, and this was obviously evident from a lower RMSE (0.018) and a higher R (0.96)
value. While ANN model was also the best one in the prediction of the test set.
Fig 5. A comparison of experimental vs predicted EC50 using ANN method.
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Fig 6. A comparison of experimental vs predicted EC50 using SVM method.
Effect of MW on the scavenging activity of DPPH radical
Molecular weight was seen as an important indicator of the antioxidant activity of
], so a single study was used to evaluate the relationship of MW and antioxidant
activity of polysaccharides. Due to the relatively large difference in MW of polysaccharide from
2250 to 538500 (Table 4), MW was normalized before the analysis, the size of MW was taken
with a base-8 of log, and the data was shown in Table 4 [
We used EC50 values as the horizontal coordinate and established the correlation between
EC50 and MW. As shown in Fig 7, the value of EC50 decreased with the decrease of MW, which
indicated that the smaller MW could have the stronger DPPH free radical scavenging activity.
This result was in accord with those reported in the literature [
]. In Fig 7, it could also be
found that there were some points which did not conform to the rules, such as TYAP-3 and
BSFP-1. BSFP-1 had the smaller MW and a relatively larger EC50 value , which may be
because BSFP-1 had no UA. TYAP-3 had larger MW, but its EC50 value was smaller. The
reason may be that the content of Ara accounted for 45.82% in TYAP-3 [
]. Fig 7 showed that
when the value of EC50 arranged from 0 to 2, the value of Y axis was 0–5.5, which indicated
that MW was between 4000 and 100000.
According to the above results, we could conclude that the antioxidant activity of
polysaccharide usually was higher in MW range of 4000–100000. However, MW was not the only
factor, and the antioxidant activity could be affected by other polysaccharide properties, such as
UA and Ara.
Fig 7. Correlation scatter plots of EC50 and MW.
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Models for the hydroxyl radicals scavenging activity of polysaccharides
To make relationship models of monosaccharide composition and the hydroxyl radicals
scavenging activity, the entire data set including 67 compounds was divided into two clusters [
]. The test set and the train set were given in Table 5.
We selected five relevant descriptors in MLR model, and a stable model EC50 =
0.12PC+0.083Fuc+0.013Rha-0.02UA+0.372 (R = 0.664, RMSE = 1.149, F = 8.268, p<5.17E-5) was
given. According to the model, PC, Fuc, Rha and UA had significant correlation with EC50 of
the hydroxyl radicals scavenging activity, and the relevant correlation coefficient was shown in
The statistical parameters of MLR, ANN and SVM models for the train set and the test set
were shown in Table 7. According to a lower RMSE and a higher R value, the results indicated
that nonlinear model ANN was better than models obtained from MLR and SVM for the
prediction of hydroxyl radicals scavenging activity of polysaccharides.
Sensitivity analysis from ANN
According to two ANN models, the results of sensitivity analysis were shown in Table 8. The
higher sensitivity coefficient indicated that this descriptor had the more influence upon the
antioxidant activity of polysaccharides. The results indicated that Ara and GalA had a great
effect on DPPH-scavenging activity, and PC, UA and GalA had a great effect on hydroxyl
radicals scavenging activity of polysaccharides, which was consistent with the results from MLR.
To establish quantitative structure-activity relationship (QSAR) models for antioxidant activity
of polysaccharides, MLR, SVM and ANN methods were used, and polysaccharide properties
(UA, PC, monosaccharide compositions, MW) as descriptors were selected. MLR models for
predicting EC50 of DPPH-scavenging activity and hydroxyl radicals scavenging activity of
polysaccharides consisted of four major descriptors, and the models were EC50 =
0.033Ara0.041GalA- 0.03GlcA- 0.025PC +0.484 and EC50 = 0.12PC +0.083Fuc +0.013Rha -0.02UA
+0.372, respectively. A comparison of results from models indicated that the ANN model with
R = 0.96 and RMSE = 0.018 predicted more accurately the DPPH-scavenging activity of
polysaccharides than SVM and MLR models. ANN model (R = 0.933, RMSE = 0.055) was also the
best one for predicting the hydroxyl radicals scavenging activity of polysaccharides. According
to MLR and ANN models, Ara and GalA were most critical in determining the
DPPH-scavenging activity of polysaccharides, and PC, UA and GalA had a great effect on hydroxyl radicals
scavenging activity of polysaccharides. The polysaccharide of MW 4000–100000 usually
owned higher DPPH-scavenging activity, but the antioxidant activity could simultaneously be
affected by other polysaccharide properties. These results may provide some new insights in
the complex study of polysaccharide structure and bioactivities, and we can simply predict the
antioxidant activity of polysaccharide by using the established models after determining the
monosaccharide composition ratios and MW.
It is worth noting that the highly GalA-containing polysaccharide could exhibit significantly
antioxidant activity, which might be because they owned the functional group–COOH. It has
been reported that the functional groups such as–COOH, CH3CO–and–SH were generally
recognized as good electron or hydrogen donors that might be related to the antioxidant activity
of polysaccharides [
]. The antioxidant activity of polysaccharide was also found to correlate to
complex structure such as glycosidic linkages, branch ratios, and microstructure etc,
polysaccharide properties is not enough for fine detailed structure of polysaccharide, and the research
on more precise structure-function relationships remained to be explored.
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This work was supported by Promotion Program for Young and Middle-aged Teacher in
Science and Technology Research of Huaqiao University (ZQN-PY316), National Nature Science
Foundation of China (31201314), and Cultivation Project Funds for Postgraduates Innovation
Ability of Huaqiao University.
Conceptualization: ZW DL.
Data curation: ZW.
Formal analysis: ZL DL.
Funding acquisition: DL.
Project administration: DL.
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Software: ZL KN.
Validation: ZL KN.
Writing – original draft: ZL ZW.
Writing – review & editing: ZW.
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