J. Biomedical Science and Engineering, 2009, 2, 63-69
Published Online February 2009 in SciRes. http://www.scirp.org/journal/jbise JBiSE
1
The pattern of co-existed posttranslational
modifications-A case study
Zheng-Rong Yang
School of Biosciences, University of Exeter, Correspondence should be addressed to Zheng- Rong Yang ([e-mail])
Received August 20th, 2008; revised December 1st, 2008; accepted December 5th, 2008
ABSTRACT
Posttranslational modifications are a class of
important cellular activities in various bio-
chemical processes including signalling trans-
duction, gene/metabolite networks, and disease
development. It has been found that multiple
posttranslational modifications with the same or
different modification residues can co-exist in
the same protein and this co-occurrence is
critical to signalling networks in cells. Although
some biological studies have spotted this phe-
nomenon, little bioinformatics study has been
carried out for understanding its mechanism.
Four data sets were downloaded from NCBI for
the study. The joint probabilities of any two
neighbouring posttranslational modification
sites of different modification residues were
analyzed. The Bayesian probabilistic network
was derived for visualizing the relationship be-
tween a target modification and the contributing
modifications as the predictive factors.
Keywords: posttranslational modification, bio-
informatics, pattern analysis, posttranslational
modification, pattern analysis, probability
model
1. INTRODUCTION
Posttranslational modifications (PTMs) are a chemical
process of modifying a protein’s chemical or structural
properties after the translation of the protein has been
completed. Posttranslational modifications are closely
related with signalling networks of molecules in cells.
The modifications include attaching a chemical, chemi-
cal structural change of amino acids, or protein structural
change. The modification will also alter the functions of
a protein in both ways, adding functions or removing
functions, for instance, phosphorylation and dephos-
phorylation, carboxylation and decarboxylation. Because
of these changes, proteins will carry different signals for
functioning in cells. Posttranslational modifications are
then the focus of many signalling transduction network
studies. For instance, properly folded and posttransla-
tional modified endoplasmic reticulum is related with
stress and will lead to different pathological states [1].
Poly (ADP-ribosyl)ation is related with DNA repair and
cell cycle checkpoint pathways as the unique signal for
protein function modulations [2]. Posttranslational modi-
fications are the mediators for the transporters for multi-
ple functions of human copper-transporting ATPases [3].
In the study of various chronic diseases, it is found that
protein 3-nitrotyrosine (nitration) plays an important role
in pathological conditions [4]. Together mutations and
aberrant mRNA splicing, hyperphosphorylation will lead
to a number of neurodegenerative disorders [5].
S-Nitrosation has been recently found to have similar
function as phosphorylation and acetylation because of
its association with various pathological cell reactions in
signalling networks [6]. In cell-cycle control, differen-
tiation, metabolism, stress response and programmed
cell-death, the FOXO subgroup of forkhead transcription
factors have been found being tightly controlled by
phosphorylation, acetylation and ubiquitination [7].
In biological experiments, it has been found that the
co-occurrence of posttranslational modifications is criti-
cal for many cellular functions in recent a few years. For
instance, it has been found that the necessary condition
for stable transcriptional activity of p53 is the coopera-
tion of multiple posttranslational modifications such as
phosphorylation, acetylation, and ubiquitination [8]. In
studying the complex pattern of posttranslational modi-
fications and its impact on cellular processes, it has been
found that lysine acetylation, arginine/lysine methylation
and serine/threonine phosphorylation will work together
cooperatively for regulating the high mobility group
proteins [9]. In the experiments with human cancer
specimens, it has been found that the extent of acetyla-
tion, formylation and methylation is higher in cultured
cells [10]. It has also been found that proteins with mul-
tiple posttranslational modifications may make contribu-
tion to similar signalling functions [11]. In studying
DNA repair, apoptosis and senescence, it has been found
that the interplay between multiple protein modifications,
including phosphorylation, ubiquitylation, acetylation
and sumoylation is critical for properly propagating
DNA damage signals [12] and the interplay between
methylation and acetylation has been found important
for activating p53 by responding to DNA damage signals
SciRes Copyright © 2009

64 Z. R. Yang et al. / J. Biomedical Science and Engineering 2 (2009) 63-69
SciRes Copyright © 2009 JBiSE
[13]. It is even found that there is crosstalk between dif-
ferent posttranslational modifications [14]. In glycogen
syntheses kinase-3, it has been found that O-GlcNAcy-
lation O-phosphate is interplaying for cellular regulation
[15]. The interplay has been also found in the steroid
receptor coactivators [16]. In the study of transcriptional
programming, it is found that interplay between post-
translational modifications exists in H3 termini [17]. In
histone, it has been found that there are multiple arginine
posttranslational modifications which are critical for
some disease development. Also in histone, the interplay
between sumoylation and either acetylation or ubiquity-
lation has been observed contributing to complex func-
tions of proteins [18]. A recent study has used laboratory
method to identify co-occurrence posttranslational modi-
fications [19]. A computational method was proposed to
predict the interplay between phosphorylation sites and
O-GlcNAc sites based on peptides around modification
residues [20,21]. The analysis was based on the predic-
tion results from various PTM prediction tools and was
based on peptide information only. Moreover, the method
only focused on the competition mechanism between
phosphorylation sites and O-GlcNAc modifications at the
same residues.
The patterns of co-occurrence of posttranslational
modifications are so far unclear or have not yet emerged
through large scale studies. Bioinformatics study will
help revealing those patterns and will benefit many de-
sirable cellular engineering processes, i.e. disease con-
trol and prevention based on handling signalling path-
ways subjectively. This study is aimed to analyse the
patterns of co-occurrence of multiple posttranslational
modifications and visualizing their relationship through
a probabilistic analysis.
Computational approaches, such as structural bioin-
formatics [22], molecular docking [23], molecular pack-
ing [24,25], pharmacophore modelling [26], Mote Carlo
simulated annealing approach [27], diffusion-controlled
reaction simulation [28], bio-macromolecular internal
collective motion simulation [29], QSAR [30,31], pro-
tein subcellular location prediction [32,33], identifica-
tion of membrane proteins and their types [34], identifi-
cation of enzymes and their functional classes [35],
identification of GPCR and their types [36], identifica-
tion of proteases and their types [37], protein cleavage
site prediction [38,39,40], and signal peptide prediction
[41,42], can timely provide very useful information and
insights for both basic research and drug design and are
widely welcome by science community. The present
study is devoted to develop a computational approach
for studying co-occurrence of posttranslational modifi-
cations.
2. APPROACH
The first thing we need to do is to scan all the sequences in
a data set to find all neighbouring modifications. We use
frequency as the joint probability to measure the quantita-
tive property of co-occurrence of modifications first.
Through analyzing the frequency of two modifications, the
likelihood that a pair of modifications occurs can be quan-
tified.
However, the frequency analysis only shows how
likely two modifications can occur simultaneously in the
same sequence. For instance, we may observe that the
probability for hydroxyproline and hydroxylysine to
occur simultaneously in the same sequence is 18.6%.
But this does not indicate how likely hydroxylysine de-
pends on hydroxyproline. In other words, if we have ob-
served a hydroxyproline in a sequence, how likely can we
find a hydroxylysine in the same sequence as the
neighbouring modification? We first define the joint prob-
ability of two different modification residues as the fre-
quency defined as
onsmodificati ngneighbouri ofnumber the
slysimutaneouoccur and t number tha the
),( YX YXP =
(1)
below Here,
X
and Yare two different modification
residues. We then define the marginal probability as the
frequency for one modification residue to occur in a data
set
onsmodificati allofnumber the
onmodificati ofnumber the
)( X XP = (2)
Using the product theory in probability, we have Here
)()|()()|(),( XPXYPYPYXPYXP =
=
(3)
)|( YXP reads out as the conditional probability for
X
to occur given that Y has happened. Based on the
above calculation, we will have two conditional prob-
abilities, either the probability of observing
X
if Y
has been observed or the probability of observing Y if
X
has been observed. Based on the conditional prob-
abilities and the marginal probabilities, we can use the
Bayes rule to determine the posterior probabilities which
are commonly used for decision-making. The Bayes rule is
defined as
∑
=
mmm
ii
iXPXYP
XPXYP
YXP )()|(
)()|(
)|( (4)
Here we treat
Y
as a target modification, for instance a
hyrdoxyproline residue. i X is the ith potential contrib-
uting modification for the target modification Y. The
posterior probability indicates if
Y
has been observed,
what is the probability that it has a neighbour i X. It
quantifies quantitatively how possible i
X is most
probable neighbouring modification of
Y
. Note that
1)|( =
∑
iiYXP . The posterior probabilities are then
used to draw networks to illustrate the relationship be-
tween a target modification residue and other modifica-
tion residues. This probabilistic network here is a simple

Z. R. Yang et al. / J. Biomedical Science and Engineering 2 (2009) 63-69 65
SciRes Copyright © 2009 JBiSE
type of Bayesian networks [43].
3. DATA AND EXPERIMENTAL DESIGN
Two classes of posttranslational modifications are used
for the study, i.e. hydroxylation and methylation. Both
have two most common modification residues. The hy-
droxylation mainly functions at a lysine residue or a
proline residue while methylation mainly functions at a
lysine residue or an arginine residue. Both have ample
experimentally verified data for the study. Four key-
words, hydroxyproline, hydroxylysine, methyllysine
and methylarginine were used to scan NCBI database
to download sequences. All the identical sequences
were removed from the study. All three types of phos-
phorylations were grouped together named as phos-
phorylation. All the amidation activities were also
grouped into one type of modification. Various acety-
lation modifications are grouped together. Because
there are only two poly (methylaminopropyl) lysine
sites, they are treated as methyllysine. Two me-
thy-hydroxylysine residues are treated separated as one
methyllysine and one hydroxylysine.
Table 1 shows the statistics of these four data sets.
There are 10, 17, 10, and 8 different modification resi-
dues in the hydroxylysine, hydroxyproline, methy-
larginine, and methyllysine data sets, respectively.
Here modification residue means a specific posttrans-
lational modification activity at residues in proteins,
for instance a hydroxyproline residue means a proline
which can be hydroxylated and has been confirmed in
experiments. The percentages of sites per sequence are
15.3, 7.1, 7.3, and 5.8 for the hydroxylysine, hy-
droxyproline, methylarginine, and methyllysine data
sets, respectively. The hydroxyproline data set has the
double number of modification residues compared
with other three data sets. The details of multiple
modifications are listed in Table 2. The abbreviations
are seen in Table 3.
Table 4 shows the distribution of neighbouring PTMs
of different modification residues. It can be seen that at
least 25% (and up to 35%) of neighbouring PTMs are of
different modification residues.
Table 1. The statistics of four data sets
Data sets SequencesSites PTM
types Sites per seq
Hydroxylysine38 581 10 15.3
Hydroxyproline199 1405 17 7.1
Methylarginine23 167 10 7.3
Methyllysine 65 376 8 5.8
Table 2. The details of modifications in four data sets
Hydroxylysine Hydroxyproline Methylarginine Methyllysine
hydroxyproline hydroxyproline phosphorylation phosphorylation
hydroxylysine hydroxylysine methylarginine acetylation
Amidation hydroxyphenylalanine methylhistidine citrulline
Allysine allysine methylglutamine methyllysine
phosphorylation amidation thioglycine methylarginine
methyllysine acetylation acetylation amidation
bromotryptophan carboxylation methyllysine methylhistidine
hydroxyphenylalanine hydroxyasparagine citrulline methylalanine
hydroxyarginine bromotryptophan methylcysteine
proteolytic methylcysteine deamidation
phosphorylation
methyllysine
sulfoation
didehydrobutyrine
chlorotryptophan
didehydroalanine
hydroxyvaline
Table 3. The abbreviations
AC acetylation AK allysine
AM amidation BR bromotryptophan
CA carboxylation CH chlorotryptophan
CT citrullination DA didehydroalanine
DM deamidation DP D-phenylalanine
DT D-tryptophan DY didehydrobutyrine
HK hydroxylysine HP hydroxyproline
HR hydroxyarginine HN hydroxyasparagine
HV hydroxyvaline HF hydroxyphenylalanine
MA methylalanine MC methylcysteine
MQ methylglutamine MH methylhistidine
MK methyllysine MR methylarginine
NT nitration PH phosphorylation
PR proteolytic SU sulfoation
TH thioglycine

66 Z. R. Yang et al. / J. Biomedical Science and Engineering 2 (2009) 63-69
SciRes Copyright © 2009 JBiSE
Among these neighbouring PTMs of different modi-
fication residues, 33%, 68%, 78%, 84% have the dis-
tance less than 10 residues for the methylarginine, the
methyllysine, the hydroxylysine, and the hydroxyproline
data sets, respectively as seen in Table 5. Because of this,
two PTMs of different modification residues may likely
share similar structure (at least an overlapped local
structure) for binding. This suggests that the cooperative
activities of PTMs of different modification residues are
critical to cellular signalling/functioning.
Based on the collected sequences, we produce a pro-
gram to search for all the posttranslational modification
residues in four data sets. The sites must have the nota-
tion as </site_type=”modified”>, </experiment=”ex-
perimental evidence, …”>, and </note_type=X>. Here X
can be various types, for instance, 4-hydroxyproline,
3-hydroxyproline, 5-hydroxyproline, etc. For each of
four posttranslational modifications, we find all the in-
volved posttranslational modification sites. The se-
quences in different data sets are analyzed separately
although there are some overlaps among them.
4. RESULTS
Table 6 shows the frequencies as the joint probabilities
of nine types of modifications for the hydroxylysine data
set. Proteolytic is removed as there is only one such
site in the data set. The highest joint probability is 60.2%
for two hydroxyproline residues to be neighbours. How-
ever, the joint probability for two hydroxylysine residues
to be neighbours is only 6.81%. The co-occurrence
probability for these two types of hydroxylation is
18.8%. These two probabilities indicate that for every
hydroxylysine residue, the probability for it to have a
hydroxyproline as the neighbour is three times higher
than the probability for it to have the same hy-
droxylysine residue as the neighbour. The joint prob-
abilities for a hydroxylysine to have an amidation,
allysine, phosphorylation residue, bromotryptophan,
hydroxyphenylalanine, and hydroxyarginine residue as
the neighbour are 0.55%, 0.55%, 0.18%, 0.74%, and
0.18%, respectively. This means that except for the same
type of modification,
Table 4. The distribution of neighbouring PTMs of different modi-
fication residues
sites Percentage over total
Hydroxylysine 147 25%
Hydroxyproline 434 31%
Methylarginine 40 24%
Methyllysine 132 35%
Table 5. The times for different modifications to occur in one
sequence
1-2 3-4 5-6 7-8 9-10 >11 Sum
Hydroxylysine 32 32 33 3 14 33147
Hydroxyproline 175 97 46 18 29 69434
Methylarginine 3 2 2 2 4 2740
Methyllysine 48 24 5 11 2 42132
Table 6. The joint probability as frequency of co-occurred modi-
fications for the hydroxylysine data set
HPHKAMAKPH MK BR HFHR
HP60.218.80 0 0 0 0.18 0 0
HK18.86.810.550.550.18 0 0.18 0.740.18
AM0 0.550 0 0 0 0 0 0
AK0 0.550 0 0 0 0 0 0
PH0 0.180 0 4.05 5.52 0 0 0
MK0 0 0 0 5.52 1.47 0 0 0
BR0.180.180 0 0 0 0 0 0.18
HF0 0.740 0 0 0 0 0.370
HR0 0.180 0 0 0 0.18 0 0
hydroxylysine has a high correlation with allysine, ami-
dation, and hydroxyphenylalanine modifications.
Figure 1 visualises the probabilistic relationship
among different types of modifications in the hy-
droxylysine data set using the posterior probabilities,
where all the posterior probabilities less than 10% are
omitted for simplicity. The network demonstrates that
hydroxylysine only depends on hydroxyproline (24%).
However, hydroxylysine has great impacts on five modi-
fication residues, allysine (100%), amidation (100%),
hydroxyphenylalanine (67%), hydroxyarginine (50%),
and bromotryptophan (33%). Phosphorylation and me-
thyllysine modification residues are independent from
the hydroxylysine block. They are mutually correlated to
each other.
Table 7 shows the joint probabilities for the hy-
droxyproline data set. It can be seen that the probability for
two hydroxyproline residues to be neighbours is 57.7%. The
likelihood for a hydroxyproline to have a hydroxylysine
Figure 1. The probabilistic network as the relationship
among modification residues in the hydroxylysine data set.
The values represent the posterior probabilities. The arcs
mean the directions. For instance, the arc from HK to AM
with a number 100 means P(HK|AM)=100%. In other
words, the probability of observing a hydroxylysine residue
nearby an observed amidation residue is almost certain

Z. R. Yang et al. / J. Biomedical Science and Engineering 2 (2009) 63-69 67
SciRes Copyright © 2009 JBiSE
as the neighbour is 8.46%. However, the likelihood for a
hydroxyproline to have a hydroxyphenylalanine as the
neighbour is 18.2%. This means that a hydroxyproline is
more likely to have a hydroxyphenylalanine to co-occur
rather than hydroxylysine. The other two important
modifications for hydroxyproline are carboxylation and
amidation. The probability for a hydroxyproline residue
and an amidation residue to be neighbours is 4.06% and
the probability for a hydroxyproline residue and a car-
boxylation residue to be neighbours is 1.91%. The other
co-occurred modifications with joint probabilities larger
than 0.2% are acetylation and bromotryptophan.
The probabilistic relationship among different modi-
fications shown in Figure 2 is built for the hy-
droxyproline data set using the posterior probabilities.
All the posterior probabilities less than 10% are not
shown. In the probabilistic network, it can be seen that
the most contributing modifications for hydroxyproline
is hydroxyphenylalanine (20%). However, hydroxyproline
has contributed to 11 other modification residues. For
instance, the posterior probability P(HP|AC) is 100%
meaning that whenever we have found an acetylation
residue, it is certain that there is a hydroxyproline resi-
due nearby. The posterior probability P(HP|HP) is 63%
while P(HK|HP)=9%, P(AK|HP)=1%, P(AM|HP)=4%,
P(AC|HP)=1%, P(CA|HP)=2%, P(BR|HP)=1%.
Table 7. The joint probability (larger than 0.2%) as frequency of
co-occurred modifications for the hydroxyproline data set
HP HK HF AM AC CABR
HP 57.7 8.46 18.2 4.06 0.41 1.910.5
HK 8.46 1.58 0 0.08 0 0 0.08
HF 18.2 0 0.25 0 0 0 0
AM 4.06 0.08 0 0.17 0 0.170.41
AC 0.41 0 0 0 0 0 0
CA 1.91 0 0 0.17 0 2.240.33
BR 0.5 0.08 0 0.41 0 0.33 0
Figure 2. The probabilistic network as the relationship
among modifications in the hydroxyproline data set. The
values represent the posterior probabilities
Table 8 shows the frequencies of modifications in the
methylarginine data set. The likelihood for two methy-
larginine residues to co-occur as neighbours is 40.3%.
The interesting phenomenon is that the co-occurrence
probability between methylarginine and methyllysine
residues (as neighbours) is 0%. The contributing modi-
fications to methylargine residues are phosphorylation
(11.1%), acetylation (2.78%), methylhistidine (1.39%),
and citrulline (1.39%). This is not as expected as it is
thought that both two dominant methylation modifica-
tions should be highly correlated.
Shown in Figure 3 is the probabilistic relationship
among the modifications derived from the methylargin-
ine data set. The network shows that the most contribut-
ing modifications to methylarginine modification is
phosphorylation (19%), i.e. P(PH|MR)=19%. For any
observed methylarginine residue in a protein, the prob-
ability of observing a phosphorylation residue is 19%.
Meanwhile, methylarginine residue can be an important
pre-request for other modification residues. For instance,
the probability of observing a methylarginine residue if a
methylhistidine residue has been observed is 100% and
the probability that a methylarginine residue is standing
near an observed acetylation residue is 33%.100%.
Table 8. The joint probability as frequency of co-occurred modi-
fications for the methylarginine data set
PHMRMHMG TH AC MKCT
PH26.411.10 0 0 2.78 1.390.69
MR11.140.31.390.69 0.69 2.78 0 1.39
MH0 1.390 0 0 0 0 0
MG0 0.690 0 0.69 0 0 0
TH0 0.690 0.69 0 0 0 0
AC2.782.780 0 0 2.08 0.690
MK1.390 0 0 0 0.69 0.690
CT0.691.390 0 0 0 0 2.78
Figure 3. The probabilistic network as the relationship
among modifications in the methylarginine data set. The
values represent the posterior probabilities

68 Z. R. Yang et al. / J. Biomedical Science and Engineering 2 (2009) 63-69
SciRes Copyright © 2009 JBiSE
Table 9 shows the frequencies for the methyllysine
data set. The probability for two methyllysine residues to
be neighbours is 28.3%, which is not dominantly high.
Interestingly, we have found the contributing modifica-
tions for methyllysine are acetylation (15.11%) and
phosphorylation (10.61%). It is again difficult to find the
evidence that two methylation modifications are highly
correlated.
Figure 4 shows the probabilistic network as the rela-
tionship among the modifications in the methyllysine
data set. Here, the most contributing modifications to
methyllysine are phosphorylation (22%) and acetylation
(31%). The methyllysine residue has a high correlation
with methylarginine, methyhistine, and methylalanine.
All have the posterior probabilities as 100%.
5. CONCLUSION
This paper has studied the co-occurrence pattern of two
types of posttranslational modifications with four modi-
fication residues. The study aims to reveal how post-
translational modifications are correlated to each other,
i.e. how one posttranslational modification contributes to
the others. It has been found that the hydroxylysine and
hydroxyproline residues are not the most mutually de-
pendent modification residues, so are the methylarginine
Table 9. The joint probability as frequency of co-occurred modi-
fications for the methyllysine data set
PH AC CT MKMR AM MHMA
PH 8.04 11.58 0 10.61 0 0.32 0 0
AC 11.58 28.3 0.96 15.11 1.93 0 0 0
CT 0 0.96 0 0.960 0 0 0
MK 10.61 15.11 0.96 21.22 0 0 0.320.64
MR 0 1.93 0 0 0 0 0 0
AM 0.32 0 0 0 0 0 0 0
MH 0 0 0 0.320 0 0 0
MA 0 0 0 0.640 0 0 0
Figure 4. The probabilistic network as the relationship
among modifications in the methyllysine data set. The
values represent the posterior probabilities
and methyllysine residues. We have found that the hy-
droxyllysine residues depend on the hydroxyproline resi-
dues with a posterior probability 24% and the hy-
droxyproline residues are the unique major contributing
modification residue for the hydroxylysine residues.
However, we have found the hydroxyproline residues
nearly do not depend on the hydroxylysine residues.
Instead, the hydroxyphenylalanine residues are the con-
tributing modification residue to the the hydroxyproline
residues with a posterior probability 20%. Among the
methylarginine residues and the methyllysine residues,
we have found that the phosphorylation residues are the
main player for both of these two modification residues.
In addition, the acetylation residues are needed for the
methyllysine residues as well. Surprisingly, two different
methylation residues also do not rely on each other. Al-
though the study is limited to two modification classes
with four modification residues, it is expected that this
method can be generalized to a wide range of multiple
posttranslational modification pattern discovery.
REFERENCES
[1] E. Lai, T. Teodoro, and A. Volchuk, (2007) Endoplasmic reticu-
lum stress: signaling the unfolded protein response. Physiology
(Bethesda), 22, 193-201.
[2] J. F. Haince, S. Kozlov, V. L. Dawson, T. M. Dawson, M. J.
Hendzel, M.F. Lavin, and G. G. Poirier, (2007) Ataxia telangiec-
tasia mutated (ATM) signaling network is modulated by a novel
poly (ADP-ribose)-dependent pathway in the early response to
DNA- damaging agents. J Biol Chem., 282, 16441-16453.
[3] S. Lutsenko, A. Gupta, , J. L. Burkhead, and V. Zuzel, (2008)
Cellular multitasking: The dual role of human Cu-ATPases in
cofactor delivery and intracellular copper balance. Arch Bio-
chem Biophys., (in press).
[4] J. M. Souza, G. Peluffo, and R. Radi, (2008) Protein tyrosine
nitration-Functional alteration or just a biomarker? Free Radic
Biol Med., (in press).
[5] J. Z. Wang, and F. Liu, (2008) Microtubule-associated protein
tau in development, degeneration and protection of neurons.
Prog Neurobiol., 85, 148-175.
[6] V. V. Sumbayev and I. M. Yasinska, (2008) Protein S-nitrosation
in signal transduction: assays for specific qualitative and quanti-
tative analysis. Methods Enzymol, 440, 209-219.
[7] T. Obsil, and V. Obsilova, (2008) Structure/function relation-
ships underlying regulation of FOXO transcription factors. On-
cogene, 27, 2263-2275.
[8] A. Scoumanne and X. Chen, (2008) Protein methylation: a new
mechanism of p53 tumor suppressor regulation. Histol Histopa-
thol., 23, 1143-1149.
[9] Q. Zhang, and Y. Wang, (2008) High mobility group proteins
and their post-translational modifications. Biochim Biophys
Acta., (in press).
[10] J. R. Wisniewski, A. Zougman, S. Kruger, P. Ziołkowski, M.
Pudełko, M. Bębenek and M. Mann, (2008) Constitutive and
dynamic phosphorylation and acetylation sites on NUCKS, a
hypermodified nuclear protein, studied by quantitative pro-
teomics. Proteins, (in press).
[11] C. A. Galea, A. Nourse, Y. Wang, S. G. Sivakolundu, W. T.
Heller and R. W. Kriwacki, (2008) Role of intrinsic flexibility in
signal transduction mediated by the cell cycle regulator, p27
Kip1. J Mol Biol. 376, 827-838.
[12] M. S. Huen and J. Chen, (2008) The DNA damage response
pathways: at the crossroad of protein modifications. Cell Res.,
18, 8-16.
[13] M. S. Huen and J. Chen, (2008) The DNA damage response
pathways: at the crossroad of protein modifications. Cell Res.,

Z. R. Yang et al. / J. Biomedical Science and Engineering 2 (2009) 63-69 69
SciRes Copyright © 2009 JBiSE
18, 8-16.
[14] T. Hunter, (2007) The age of crosstalk: phosphorylation, ubiq-
uitination, and beyond. Mol Cell., 28, 730-738.
[15] Z. Wang, A. Pandey and G. W. Hart, (2007) Dynamic interplay
between O-linked N-acetylglucosaminylation and glycogen syn-
thase kinase-3-dependent phosphorylation. Mol Cell Proteomics,
6, 1365-1379.
[16] S. Li and Y. Shang (2007) Regulation of SRC family coactivators
by post-translational modifications. Cell Signal, 19, 1101- 1112.
[17] P. R. Thompson and W. Fast, (2006) Histone citrullination by
protein arginine deiminase: is arginine methylation a green light
or a roadblock? ACS Chem Biol., 21, 433-41.
[18] D. Nathan, K. Ingvarsdottir, D. E. Sterner, G. R. Bylebyl, M.
Dokmanovic, J. A. Dorsey, K. A. Whelan, M. Krsmanovic, W. S.
Lane, P. B. Meluh, E. S. Johnson, and S. L.Berger, (2006) His-
tone sumoylation is a negative regulator in Saccharomyces cere-
visiae and shows dynamic interplay with positive-acting histone
modifications. Genes Dev., 20, 966-976.
[19] J. Seo, J. Jeong, Y. M. Kim, N. Hwang, E. Paek and K. J. Lee,
(2008) Strategy for comprehensive identification of post-trans-
lational modifications in cellular proteins, including low abun-
dant modifications: application to glyceraldehyde-3-phosphate
dehydrogenase. J Proteome Res., 7, 587-602.
[20] A. Ahmad, T. S. Khan, D. C. Hoessli, E. Walker-Nasir, A. Ka-
leem, A. R. Shakoori and S. Nasir-ud-Din, (2008) In silico
modulation of HMGN-1 binding to histones and gene expres-
sion by interplay of phosphorylation and O-GlcNAc modifica-
tion. Protein Pept Lett., 15, 193-199.
[21] A. Kaleem, D. C. Hoessli, I. Ahmad, E. Walker-Nasir, A. Nasim,
A. R. Shakoori and S. Nasir-ud-Din, (2008) Immediate-early
gene regulation by interplay between different post-translational
modifications on human histone H3. J Cell Biochem., 103,
835-851.
[22] K. C. Chou, (2004) Review: Structural bioinformatics and its
impact to biomedical science. Current Medicinal Chemistry, 11,
2105-2134.
[23] K. C. Chou, D. Q. Wei and W. Z. Zhong (2003) Binding mecha-
nism of coronavirus main proteinase with ligands and its impli-
cation to drug design against SARS. (Erratum: ibid., 2003,
Vol.310, 675). Biochem Biophys Res Comm, 308, 148-151.
[24] K. C. Chou, G. Nemethy and H. A. Scheraga, (1984) Energetic
approach to packing of a-helices: 2. General treatment of non-
equivalent and nonregular helices. Journal of American Chemi-
cal Society, 106, 3161-3170.
[25] K. C. Chou, G. M. Maggiora, G. Nemethy and H. A. Scheraga,
(1988) Energetics of the structure of the four-alpha-helix bundle
in proteins. Proceedings of National Academy of Sciences, USA,
85, 4295-4299.
[26] S. Sirois, D. Q. Wei, Q. S. Du and K. C. Chou, (2004) Virtual-
Screening for SARS-CoV Protease Based on KZ7088 Pharma-
cophore Points. J Chem Inf Comput Sci, 44, 1111-1122.
[27] K. C. Chou, (1992) Energy-optimized structure of antifreeze
protein and its binding mechanism. Journal of Molecular Biol-
ogy, 223, 509-517.
[28] K. C. Chou and G. P. Zhou, (1982) Role of the protein outside
active site on the diffusion-controlled reaction of enzyme. Jour-
nal of American Chemical Society, 104, 1409-1413.
[29] K. C. Chou, (1988) Review: Low-frequency collective motion in
biomacromolecules and its biological functions. Biophysical
Chemistry, 30, 3-48.
[30] Q. S. Du, R. B. Huang, Y. T. Wei, L. Q. Du and K. C. Chou,
(2008) Multiple Field Three Dimensional Quantitative Struc-
ture- Activity Relationship (MF-3D-QSAR). Journal of Compu-
tational Chemistry, 29, 211-219.
[31] H. Gonzalez-Díaz, Y. Gonzalez-Díaz, L. Santana, F. M. Ubeira
and E. Uriarte, (2008) Proteomics, networks, and connectivity
indices. Proteomics, 8, 750–778.
[32] K. C. Chou and H. B. Shen, (2007) Review: Recent progresses
in protein subcellular location prediction. Analytical Biochemis-
try, 370, 1-16.
[33] K. C. Chou and H. B. Shen, (2008) Cell-PLoc: A package of
web-servers for predicting subcellular localization of proteins in
various organisms. Nature Protocols, 3, 153-162.
[34] K. C. Chou and H. B. Shen, (2007) MemType-2L: A Web server
for predicting membrane proteins and their types by incorporat-
ing evolution information through Pse-PSSM. Biochem Biophys
Res Comm, 360, 339-345.
[35] H. B. Shen, and K. C. Chou, (2007) EzyPred: A top-down ap-
proach for predicting enzyme functional classes and subclasses.
Biochem Biophys Res Comm, 364, 53-59.
[36] K. C. Chou, (2005) Prediction of G-protein-coupled receptor
classes. Journal of Proteome Research, 4, 1413-1418.
[37] K. C. Chou and H. B. Shen, (2008) ProtIdent: A web server for
identifying proteases and their types by fusing functional do-
main and sequential evolution information. Biochem Biophys
Res Comm, 376, 321-325.
[38] K. C. Chou, (1993) A vectorized sequence-coupling model for
predicting HIV protease cleavage sites in proteins. J Biol Chem,
268, 16938-16948.
[39] K. C. Chou, (1996) Review: Prediction of HIV protease cleav-
age sites in proteins. Analytical Biochemistry, 233, 1-14.
[40] H. B. Shen and K. C. Chou, (2008) HIVcleave: a web-server for
predicting HIV protease cleavage sites in proteins. Analytical
Biochemistry, 375, 388-390.
[41] K. C. Chou and H. B. Shen, (2007) Signal-CF: a subsite-coupled
and window-fusing approach for predicting signal peptides. Bio-
chem Biophys Res Comm, 357, 633-640.
[42] H. B. Shen and K. C. Chou, (2007) Signal-3L: a 3-layer ap-
proach for predicting signal peptide. Biochem Biophys Res
Comm, 363, 297-303.
[43] P. Pourret and B. Naim, (2008) Marcot, Bayesian Networks: A
Practical Guide to Applications. Chichester, UK: Wiley.