To establish a new amino acid structure descriptor that can be applied in peptide quantitative structure activity relationship (QSAR)    Keywords: amino acids, peptides, quantitative structure-activity relationship (QSAR), SVMW descriptor Peptides are essential substance to sustain life  In this paper, SVMW, which derived by principal components analysis of the matrix of 160 MoRSE descriptors and 99 WHIM descriptors of amino acids, were examined through principal component analysis (PCA). Applying SVMW to 58 angiotensin-converting enzyme inhibitors (dipeptide), 55 angiotensin-converting enzyme inhibitors (tri-peptides) and 48 bitter tasting thresholds, satisfying results were obtained from the constructed QSAR models. Experimental part Principle and Methodology Principal component analysis (PCA) Based on quantum chemistry calculation level of density function theory (DFT)  Partial least square Partial least square (PLS)  Stepwise multiple regression (SMR) was carried out for variable selection because it was less time-consuming and easy to implement. PLS was implemented by software of Simca-P 10.0. Matlab 7.0 was used for PCA, and SPSS 10.0 was used for stepwise multiple variable selection. Results and discussions QSAR model for angiotensin-converting enzyme inhibitors (dipeptide) Angiotensin converting enzyme inhibitor (dipeptide) is an inhibitor of angiotensin-converting enzyme (ACE) 

Jia Chang

Jianbo Tong

Mim Bai

Shuling Liu

Xiameng Xu

CiteSeerX

 REV. CHIM. (Bucharest) ♦ 65♦ No.5 ♦ 2014http://www.revistadechimie.ro550
A New Descriptor for Amino Acids and Its
Applications in Peptide QSAR
JIANBO TONG1,2*, JIA CHANG1,2, XIAMENG XU1,2, SHULING LIU1,2, MIM BAI1,2
1College of Chemistry and Chemical Engineering, Shaanxi University of Science & Technology, Xi’an 710021, PR China
2 Key Laboratory of Auxiliary Chemistry & Technology for Chemical Industry, Ministry of Education. Shaanxi University of
Science & Technology, Xi’an 710021, PR China
To establish a new amino acid structure descriptor that can be applied in peptide quantitative structure
activity relationship (QSAR) studies, a new descriptor, named SVMW, was derived by principal components
analysis of the matrix of 160 MoRSE descriptors and 99 WHIM descriptors of amino acids. The scale was then
applied in two panels of peptide QSAR that were molded by partial least square regression. The correlation
coefficients (Rcum
2), cross validation correlation coefficients (QLOO
2) were 0.821 and 0.783 for angiotensin-
converting enzyme inhibitors (dipeptide), 0.991 and 0.969 for angiotensin-converting enzyme inhibitors (tri-
peptides), 0.868 and 0.807 for bitter tasting thresholds, respectively. In addition, the estimation capability
and generalization ability of the models were analyzed by external validation. The correlation coefficients of
predicted values versus experimental ones of external samples (Qext
2) were 0.821, 0.774 and 0.964.
Satisfactory results showed that information related to biological activity could be systemically expressed by
SVWG scales, which may be a useful structural expression methodology for study on peptides QSAR.
Keywords: amino acids, peptides, quantitative structure-activity relationship (QSAR), SVMW descriptor
Peptides are essential substance to sustain life [1,2].
They have high activity, high selectivity and fewer side
effects. The peptide is one of the hot spots of drug research
[3,4]. However, most of experimental methods are
inefficient and expensive. Therefore, the computational
methods such as quantitative structure-activity relationship
(QSAR) have been brought into the spotlight, involving in
not only the key idea of pharmaceutical chemistry and
pharmacology but also the foundation of drug design.
Molecular structural characterization (MSC) is critical to
the success of QSAR. However, in recent years, a good
descriptor should contain as much chemical information
relating to biological activities as possible.
In this paper, SVMW, which derived by principal
components analysis of the matrix of 160 MoRSE
descriptors and 99 WHIM descriptors of amino acids, were
examined through principal component analysis (PCA).
Applying SVMW to 58 angiotensin-converting enzyme
inhibitors (dipeptide), 55 angiotensin-converting enzyme
inhibitors (tri-peptides) and 48 bitter tasting thresholds,
satisfying results were obtained from the constructed QSAR
models.
Experimental part
Principle and Methodology
Principal component analysis (PCA)
Based on quantum chemistry calculation level of density
function theory (DFT) [5, 6], Berny energetic gradient and
generalized gradient approximation (GGA) were employed
to optimize the spatial conformation of 20 coded amino
acids. The descriptor calculation software of Dragon 5.2
was utilized to generate the 160 MoRSE descriptors and 99
WHIM descriptors for each single amino acid [7]. Among
these, the calculation of DFT was achieved by Gaussian
98, and the input file of structures for natural amino acids
was automatically generated by Chemoffice 8.0. Thus, the
matrix of 20×160 dimensions consisting of 160 MoRSE
descriptors, and 20×99 dimensions consisting of 99 WHIM
descriptors was obtained. Primarily, the original variable
matrix X20×160 and X20×99 were subject to autoscaling, then
PCA was applied to generate corresponding scores of
principal components and characterized vector for every
principal component. For original 160 MoRSE descriptors
there generated 7 prominent PCs with eigenvalues > 1,
cumulatively explaining 83.05% variances. For original 99
WHIM descriptors, the former 4 PCs with eigenvalues > 1
cumulatively explained 87.55% variances were selected.
A total of 11 PCs, which MoRSE descriptors and WHIM
descriptors, for 20 coded amino acids were termed as
vector of principal component score (SVMW) (table 1).
Statistics software SPSS 13.0 implemented PCA program.
Partial least square
Partial least square (PLS) [8] regression is a widely
employed modeling method at the present time, which
has advantage of effectively overcoming multicollinearity
issues and especially suits for condition of sample size
smaller than variable number. Even more, PLS has the
desirable property that the precision of the model
parameters is improved with the increasing number of
relevant variables and observations [9, 10].
Stepwise multiple regression (SMR) was carried out for
variable selection because it was less time-consuming and
easy to implement. PLS was implemented by software of
Simca-P 10.0. Matlab 7.0 was used for PCA, and SPSS 10.0
was used for stepwise multiple variable selection.
Results and discussions
QSAR model for angiotensin-converting enzyme inhibitors
(dipeptide)
Angiotensin converting enzyme inhibitor (dipeptide) is
an inhibitor of angiotensin-converting enzyme (ACE)
* email: jianbotong@aliyun.com; jianbotong@yahoo.com.cn
REV. CHIM. (Bucharest) ♦ 65 ♦ No. 5 ♦ 2014 http://www.revistadechimie.ro 551
activity of compounds. Angiotensinogen [11,12], which is
produced by liver, is catalyzed by rennin to disrupt into
inactive angiotensin I which is further catalyzed by
angiotensin converting enzyme to rupture into angiotensin
II. ACE inhibitors combined with the structural
characteristics of angiotensin I to compete with the ACE
and inhibited its effective biological activity, so that to
achieve the purpose of control and lower blood pressure.
Thus, ACE inhibitors have prepared the premedicant [13]
to treat blood pressure, heart disease and diabetes kidney
disease. Data sets of ACE inhibitory with experimentally
determined values were originally from Cushman et al.[14].
Firstly, as a classical sample set in QSAR studies [15-
17], 58 ACE inhibitors (dipeptide) are often utilized to
validate the efficiency of amino acid descriptors. For a set
of peptide analogues, the chemical structure can now be
quantified by describing each varied amino acid position.
So the chemical structure of each dipeptide ACE inhibitor
can be described by 2×11 variables. Not all structural
descriptors are relevant to bioactivities for a QSAR data
set. SMR was employed to delete the irrelevant and
redundant descriptors. QLOO
2
 was shown to achieve the
maximum at the seventh step (ν12, ν17, ν2, ν20, ν11, ν13, ν5).
Ultimately, PLS model in which two PLS components were
enough to account for 90.6% variances of Y variables with
cross-validation achieving 88.5% and RMSE achieving
0.432. There are sequences of ACE inhibitors (dipeptide)
which sorted from low activity to high activity with their
observed and calculated activities in table 2.
Table 1
SVMW DESCRIPTORS FOR AMINO
ACIDS
Table 2
SEQUENCES OF ACE INHIBITORS (DIPEPTIDE)
WITH THEIR OBSERVED AND CALCULATED
ACTIVITIES
 REV. CHIM. (Bucharest) ♦ 65♦ No.5 ♦ 2014http://www.revistadechimie.ro552
Figure 1 presented that most of the samples were falling
into Hotelling’s T2 [18,19] confidence ellipse with 95%
confidence except sample #2, which indicated that SVMW
could provide a satisfied result to the model of ACE inhibitors
(dipeptide). The distance to the PLS model in the X space
was described by the solid line in figure 2 in order to
investigate efficiency on recombination. It can be seen
that the normalized distance to X for most samples were
smaller than the critical value of 1.692 (significance level
= 5%) except sample #49. A 7-variable PLS model was
constructed for the training set with its fitting correlative
coefficient Rcum
2=0.821, and the cross-validation
QLOO
2=0.783. In order to prove the validity and stability of
the model, the whole data set is systematically divided
into two subsets, from table 2 samples were chosen
regularly every four as test set, thus 43 samples were
treated as training set which were utilized to construct
QSAR model and the remaining 15 samples were regarded
as test set (samples in test set are highlighted with “*” in
table 2). The rest 15 samples were utilized to validate the
external prediction power of the model developed.
Consequently, the correlation coefficients of predicted
values versus experimental ones of external samples
Qext
2=0.821. There are plots of calculated versus observed
activities for ACE inhibitors (dipeptide) in figure 3. We, also
compared between QSAR models of ACE inhibitors
(dipeptide) in table 3. The results are similar to or better
than those in the literatures.
QSAR model for angiotensin-converting enzyme inhibitors
(tri-peptides)
Research on angiotensin-converting enzyme inhibitors
(tri-peptides) is an active field in medicine exploitation in
recent years. A series of 55 peptides of ACE inhibitors (tri-
peptides) peptides were taken from the data by Z.H. Lin et
al.[20].
The processes of establish model of ACE inhibitors
(tripeptide) are similar to ACE inhibitors (dipeptide). There
are sequences of ACE inhibitors (tripeptide) which sorted
from low activity to high activity with their observed and
calculated activities in table 4. PLS model in which two
PLS components were enough to account for 99.9%
variances of Y variables with cross-validation achieving
96.5% and RMSE achieving 0.07. Figure 4 presented that
all of the samples were falling into Hotelling’s T2 confidence
ellipse with 95% confidence. Except sample #29, the
distance to the PLS model in the X space was described by
the solid line in figure 5. The normalized distance to X for
Fig. 1.  PLS scores of ACE inhibitors (dipeptide)
Fig. 2.  Distance to PLS model in X space of ACE inhibitors
(dipeptide)
Fig. 3.  Plots of calculated versus observed activities for ACE
inhibitors
Table 3
COMPARISON BETWEEN QSAR MODELS OF
ACE INHIBITORS (DIPEPTIDE)
a principal components; b cumulative multiple correlation coefficient; c cumulative cross-
validated  Rcum
2; d root mean square error; e not determined.
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Table 4
SEQUENCES OF ACE INHIBITORS (TRI-
PEPTIDES) WITH THEIR OBSERVED AND
CALCULATED ACTIVITIES
Fig. 4.  PLS scores of ACE inhibitors (tri-peptides)
Fig. 5.  Distance to PLS model in X space of ACE
inhibitors (tri-peptides)
Fig. 6.  Plots of calculated versus observed activities for 55 ACE
inhibitors (tri-peptides)
Table 5
COMPARISON BETWEEN QSAR MODELS OF ACE INHIBITORS (TRI-PEPTIDES)
 REV. CHIM. (Bucharest) ♦ 65♦ No.5 ♦ 2014http://www.revistadechimie.ro554
most samples was smaller than critical value of 1.584. We
get the PLS model was constructed for the training set
with its fitting correlative coefficient Rcum
2=0.991, cross-
validation QLOO
2=0.969. After that, the whole data set is
systematically divided into two subsets, from table 4
samples were chosen regularly every five as test set, thus
44 samples were treated as training set which were utilized
to construct QSAR model and the remaining 11 samples
were regarded as test set (samples in test set are
Fig. 8.  Distance to PLS model in the X space of BTT
Table 6
SEQUENCES OF BTT INHIBITORS WITH THEIR
OBSERVED AND CALCULATED ACTIVITIES
Fig. 7.  PLS scores of BTT
highlighted with “*” in table 4). As a result, the correlation
coefficients of predicted values versus experimental ones
of external samples Qext
2=0.964. There are plots of
calculated versus observed activities for ACE inhibitors
(tripeptide) in figure 6. Table 5 is compared between QSAR
models of ACE inhibitors (tripeptide). We also get that the
results are similar to or better than those in the literatures.
QSAR model for bitter tasting thresholds
Bitter sensitivity, as one of gustatory sensitivities,
protects humans and organisms from injury by toxic
substances. Studies indicate that conduction of taste signal
in taste receptor cells involves in a series of complicated
processes mediated by G protein-coupled receptors [21].
As a classical sample set in QSAR studies, 48 bitter tasting
thresholds (BTT) reported by Collantes [22], with its activity
expressed by negative logarithm of concentration (pT), are
often utilized to validate the efficiency of amino acid
descriptors. Utilizing the 2×11 SVMW scales to describe
each BBT, the resulting PLS model in which three PLS
components were enough to account for 93.2% variances
of Y variables with cross-validation achieving 89.8% and
RMSE achieving 0.231. From figure 7, except sample #46,
they are smaller than twice the standard deviation.
However, they are all smaller than triple standard deviation.
Fig. 9.  Plots of calculated versus observed activities for 48 BBT
REV. CHIM. (Bucharest) ♦ 65 ♦ No. 5 ♦ 2014 http://www.revistadechimie.ro 555
The distance to the PLS model in the X space was
described by the solid line in  figure 8. The normalized
distance to X for all samples was smaller than critical value
of 1.709. We get that the PLS model was constructed for
the training set with its fitting correlative coefficient
Rcum
2=0.868, cross-validation QLOO
2=0.807.
Moreover, the whole data set is systematically divided
into two subsets, from table 6 samples were chosen
regularly every three as test set, thus 32 samples were
treated as training set which were utilized to construct
QSAR model and the remaining 16 samples were regarded
as test set (samples in test set are highlighted with “*” in
table 6). Consequently, the constructed model was then
utilized to predict test set with the result of Qext
2 =0.774.
There are plots of calculated versus observed activities for
BBT in figure 9. At the same time, we compared between
QSAR models of BBT in table 7. The results are similar to or
better than those in the literatures.
Conclusions
The amino acid descriptors, SVMW, were derived from
principal component analysis of 160 MoRSE descriptors
and 99 WHIM descriptors only by theoretical calculation.
Applying SVMW scales into peptide QSAR studies for three
kinds of classical peptide analogues, the results are similar
to or better than those in the literatures. Thus it is suggested
the SVMW scales have multiple advantages, such as
plentiful structural information, easy to get and good
structural characterization ability. This method can be used
widely in forecasting QSAR studies.
Acknowledgements: The authors appreciate the financial support
from the National Natural Science Foundation of China
(21301113)(21275094), the Scientific Research Planning Program of
the Education Department of Shaanxi Province (2013JK0684), the
Scientific Research Planning Program of Key laboratory of Shaanxi
Province of China (2011SZS007), and the Graduate Innovation Fund of
Shaanxi University of Science and Technology.
Table 7
COMPARISON BETWEEN QSAR MODELS OF BTT
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A New Descriptor for Amino Acids and Its Applications in Peptide QSAR

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A New Descriptor for Amino Acids and Its Applications in Peptide QSAR

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