Genome-wide analysis to predict protein sequence variations that change phosphorylation sites or their
corresponding kinases
Authors
Gil-Mi Ryu
1,3
, Pamela Song
2
, Kyu-Won Kim
3
, Kyung-Soo Oh
1
, and Jong Hun Kim
2
*
*To whom correspondence should be addressed
Affiliation
1
Center for Genome Science, 5 Nokbun-Dong, Eunpyung-Ku, Seoul, 122-701 Korea
2
Department of neurology, Samsung Medical Center, 50 Ilwon-dong, kangnam-Ku, Seoul, 135-710 Korea
3
Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, 599 Gwanak-
ro Gwanak-Ku, Seoul, 151-742, Korea.
Address for correspondence:
Jong Hun Kim, M.D.
Department of Neurology, Samsung Medical Center
Sungkyunkwan University School of Medicine
50 Ilwon-Dong, Kangnam-Ku,
Seoul, 135-710 Korea
Phone: +82-2-3410-1426
Fax: +82-2-3410-1469
E-mail: jh7521@naver.com
Abstract
We define phosphovariants as genetic variations that change phosphorylation sites or their interacting
kinases. Considering the essential role of phosphorylation in protein functions, it is highly likely that
phosphovariants change protein functions and may constitute a proportion of the mechanisms by which genetic
variations cause individual differences or diseases. We categorized phosphovariants into three subtypes and
developed a system that predicts them. Our method can be used to screen important polymorphisms and help to
identify the mechanisms of genetic diseases.
Protein phosphorylation is involved in various important processes: development and learning at the
organism level, and the cell cycle, differentiation, and apoptosis at the cellular level
1,2
. Phosphorylation can
change the subcellular localization of a protein, its lifespan, and its affinity for other proteins or DNA
3
.
Therefore, the addition or deletion of phosphorylation sites through phosphovariants can lead to functional
variations in proteins that can result in phenotypic variations or genetic diseases. By our definition,
phosphovariants are variations that change phosphorylation sites or their interacting kinases. We propose three
subtypes of phosphovariants. First, some variations occur directly at phosphorylation sites, and these sites will be
removed if the phosphoreceptors are replaced with amino acids other than serine, threonine, or tyrosine.
Conversely, replacement of another amino acid with a serine, threonine, or tyrosine may add a new
phosphorylation site. Second, variations adjacent to phosphorylation sites can result in the removal or addition of
phosphorylation sites. Third, variations may change the kinases that recognize phosphorylation sites, without
changing the phosphorylation site itself. We divided phosphovariants into type I, II, and III, respectively,
according to the above descriptions (Fig. 1).
We developed PredPhospho (version 2), a Web-based computer program that predicts phosphorylation
sites, and PhosphoVariant, a database for human phosphovariants. Even the advanced laboratory techniques used
to analyze phosphorylation sites, such as mass spectrometry (MS), cannot analyze all types of proteins
4,5
. For
example, peptides that are either too small or too large in mass can be easily missed. Moreover, membrane
proteins cannot be obtained in sufficient quantities for analysis
5
. Even when proteins can be analyzed with MS, it
is very time consuming and expensive to make thousands of variant proteins and select the phosphovariants.
PredPhospho can predict the phosphorylation sites in kinase-specific ways, using the support vector machines
(SVMs) derived from statistical learning theory proposed by Vapnik and Chervonenkis in 1995
6
. In our study,
we searched for known phosphovariants and tried to predict other possible phosphovariants among human
variations.
Results
Type I phosphovariants
The substitution of phosphoreceptor amino acids with amino acids other than serine, threonine, or
tyrosine causes the elimination of phosphorylation sites and can be classified as type I () phosphovariants
according to our classification. We found 52 type I phosphovariants by matching the locations of the variations
and those of phosphorylation sites registered in the Swiss-Prot database and the Human Protein Resource
Database
7,8
(HPRB, Table 1). Of these phosphovariants, 19 are known to cause Mendelian-inherited diseases
and 18 are associated with cancers. Another 13 phosphovariants are polymorphisms.
Conversely, new phosphorylation sites can be created by variations and we defined these as type I (+)
phosphovariants. We found an example in the study of Nousiainen et al.
9
The cell line that they used had a
Gly766Ser mutation in the probable ATP-dependent RNA helicase DDX27 (Swiss-Prot ID, Q96GQ7), which
was identified as phosphorylated. Similarly, the polymorphisms in Tables 13 are good examples of the
addition of phosphorylation sites by sequence variations. For example, if we postulate that the isoleucine at
amino acid 571 of CTP synthase 1 (Swiss-Prot ID, P17812) is changed to serine, then it also represents a type I
(+) phosphovariant, rather than a type I () phosphovariant (Table 1).
Type II phosphovariants
It is more difficult to find examples of type II phosphovariants than to find type I phosphovariants,
because we cannot definitely say that a phosphorylation site is changed by a substitution near a phosphorylation
site. However, when some kinases (although not all kinases) recognize phosphorylation sites, the specific amino
acids near the phosphoreceptor are important. In Figure 2, we present sequence logos of phosphorylation site
sequences for the CMGC kinase group. The proline residues at position +1 relative to the phosphorylation sites
are important in the phosphorylation site sequences of the CMGC kinase group, especially the CDK kinase
family and the MAPK kinase family. Most (84%) of the phosphorylation sites of the CMGC group registered in
the Swiss-Prot database and the Human Protein Resource Database (91% of the CDK and 87% of the MAPK
kinase families) have +1 proline residues. If the proline is substituted with another amino acid, it is highly
probable that the adjacent phosphorylation site will be abolished. The phosphorylation site at Ser112 of
peroxisome proliferator-activated receptor gamma protein (Swiss-Prot ID, P37231) is eliminated by the
Pro113Gln substitution
10
. We found three other polymorphisms that abolish phosphorylation sites of the CMGC
kinase group, but the removal of these phosphorylation sites has not yet been confirmed (Table 2). The presence
of specific amino acids does not directly affect phosphorylation by kinases other than those of the CMGC kinase
group, but sequences near the phosphorylation site must be considered. Kinases recognize the residues
surrounding the target phosphorylation site, and the amino acids bordering phosphorylation sites are, in turn,
affected by other nearby residues
11
. Hence, when the relevant kinases are not members of the CMGC kinase
group, it is difficult to predict type II phosphovariants simply by database matching, without specific programs
that predict phosphorylation sites.
Type III phosphovariants
Type III phosphovariants are those variations that change only the type of kinase involved, without
affecting the phosphorylation site itself. For example, Ser386 of the tyrosine protein phosphatase, non-receptor
type 1 (PTPN1, Swiss-Prot ID, P18031) is phosphorylated by cell division cycle 2 (CDC2) kinase, a member of
the CMGC kinase group, and by casein kinase 2 (CK2), a member of the "Other" kinase group
12,13
. The
Pro387Leu substitution reduces 75% of the phosphorylation by CDC2 in vitro
14
. However, it has not been
confirmed that Pro387Leu inhibits the recognition of Ser386 by CK2. Only about 5% of the sites phosphorylated
by CK2 that are registered in the Swiss-Prot database or HPRD have a proline residue at position +1 relative to
the phosphorylation site. There is also no known consensus sequence for CK2 that contains proline at that
location
15
. Therefore, we infer that the proline residue is not essential for phosphorylation by CK2 and that
Pro387Leu will have little effect on phosphorylation by CK2. Therefore, we consider Pro387Leu of PTPN1 a
type III phosphovariant because it inhibits the recognition of Ser386 by CDC2 kinase but has little effect on its
phosphorylation by CK2
14,16
.
Kinases that recognize serine and threonine differ from the kinases that recognize tyrosine. The
substitution of phosphorylated serine or threonine for tyrosine, or vice versa, can remove a phosphorylation site
or change the type of kinase that recognizes it. Changes between serine and threonine can also cause changes in
the phosphorylation site and the responsive kinase. Therefore, the phosphovariants in Table 3b are either type I
or type III phosphovariants.
Performance of PredPhospho and Scansite
We developed prediction models for six kinase groups and 18 kinase families. Their accuracy ranged
from 70.80% to 94.67% at the kinase family level and from 71.77% to 91.18% at the kinase group level
(Supplementary Table 2 online). We tested our prediction models using two real laboratory data sets compiled
with MS. For the six kinase group models, the sensitivities were 79.40% with data set I and 75.47% with data set
II, but the specificities were as low as 60.62% for data set I and 61.04% for data set II because of the
accumulation of false negatives by all six kinase group models. When we modified the specificity to > 95% (see
Supplementary Material for the modification of the specificity for each model), the specificities increased to
72.09% and 72.39%, respectively, for each data set, whereas the sensitivities decreased to 73.24% and 65.76%,
respectively (Table 4). At a specificity of > 99%, the specificities changed to 95.79% and 96.62%, respectively,
and the sensitivities to 23.39% and 20.05%, respectively. Scansite is a widely used Web-based prediction
software for phosphorylation sites
17
. We also tested Scansite with the same data sets. When we applied Scansite
with the low-stringency option to the data sets, the specificities were 52.60% and 57.06%, respectively, and the
sensitivities were 84.47% and 83.92%, respectively, whereas with the high-stringency option, the specificities
were 96.77% and 95.71%, respectively, and the sensitivities were 16.39% and 13.60%, respectively. The
performance of PredPhospho with no modification to the specificity is similar to that of Scansite used with the
low-stringency option. The performance of PredPhospho with a specificity of > 99% was similar with that of
Scansite used with the high-stringency option. The data sets were analyzed in the same way with the 18 kinase
family models (Table 4). The family-wise prediction generally had greater sensitivity and lower specificity than
the group-wise prediction.
Phosphovariants predicted with PredPhospho and Scansite
The numbers of phosphovariants predicted with PredPhospho and Scansite are shown in Table 5. In the
supplementary data (online), we present the results for phosphovariants that were predicted with PredPhospho
with the > 99% specificity option. The sensitivity and specificity of the prediction of type I phosphovariants will
be the same as the result shown in Table 4, because we can predict type I phosphovariants simply by the
location of phosphorylation sites, with no knowledge of the kinds of kinases involved. The notions of type II and
type III phosphovariants include the kinds of kinases that recognize the phosphorylation sites. Therefore, the
prediction of type II and III phosphovariants differs from that of type I. Not only the phosphorylation site but
also the type of kinase must be identified, because important amino acid residues flanking the phosphorylation
sites, which guide kinases to the site, may differ according to the kinase involved. Therefore, the general
performance of our prediction of type II and type III phosphovariants will be somewhat different from those
shown in Table 4. Instead, the performance of the each kinase-specific prediction can be judged from the
performances shown in Supplementary Table 2 (online).
The proportion of phosphovariants predicted by PredPhospho is shown in Table 6. These data were
selected with the > 99% specificity option at the kinase family level, and are related to the confirmed
phosphorylation sites in human or orthologous proteins, for which kinase information is not yet available. As
described in the Supplementary Material (online), only phosphorylation sites with available kinase information
were used as training models for PredPhospho. Hence, the phosphorylation sites in Table 6 are predicted ones
with PredPhospho, because the phosphorylation sites were not included in the training data for PredPhospho. If a
specific site is shown to be a phosphorylation site in human or orthologous proteins, then the site is definitely
located on the surface of the protein, and therefore, is accessible to a kinase. The predicted phosphovariants
related to a proven phosphorylation site are more likely to be true than are those related to an unproven
phosphorylation site. Numerous phosphorylation sites in humans or other species are constantly being identified.
The priority for further research among predicted phosphovariants can be decided based on the confirmation of
specific phosphorylation sites.
Discussion
Changes in phosphorylation sites cause various diseases by numerous mechanisms. Some proven
mechanisms of the phosphovariants shown in Tables 13 are related to changes in the protein's affinity for
DNA, inducing hyperphosphorylation and the inhibition of ubiquitinization. For example, microphthalmia-
associated transcription factor (MITF) activates the transcription of the tyrosinase gene. The Ser405Pro change
in MITF eliminates the phosphorylation site at Ser405 and inhibits the binding of MITF to DNA. As a result, the
mutation causes Waardenburg syndrome type IIa, the symptoms of which include depigmentation and
sensorineural hearing loss
18
. Abnormal phosphorylation can also cause disease by increasing phosphorylation at
other sites. The hyperphosphorylation of tau protein induces neurofibrillary tangles and the accumulation of
these tangles can result in Alzheimer's disease and frontotemporal dementia (FTD). Paradoxically, serine
threonine protein kinase N (PKN) interrupts the phosphorylation of other sites by phosphorylating Ser637 and
Ser669 of tau protein
19
. The mutations Ser637Phe and Ser669Leu of tau protein eliminate the recognition sites
for PKN and induce the hyperphosphorylation of tau protein. FTD and the respiratory failure with dementia are
known to be related to Ser637Phe and Ser669Leu, respectively
20,21
. Phosphorylation at Ser32 of NF-
B inhibitor
(Swiss-Prot ID, P25963) causes ubiquitinization and results in the activation of NF-B
22
. The Ser32Ile
substitution of NF-
B inhibitor (Swiss-Prot ID, P25963) eliminates the phosphorylation site at Ser32.
Consequently, the ubiquitinization of NF-
B inhibitor is inhibited by the Ser32Ile variant of NF-B inhibitor
and NF-B cannot be activated. The Ser32Ile variant of NF-B inhibitor causes autosomal dominant
anhydrotic ectodermal dysplasia with immunodeficiency
23
. These are a few examples of phosphovariants.
Considering the numerous functional roles played by phosphorylation in vivo, there must be many mechanisms
by which phosphovariants can cause specific diseases, and these must be identified.
Some phosphovariants do not cause Mendelian-inherited diseases, but change an individual's
susceptibility to disease. The Pro113Gln substitution (dbSNP id, rs1800571) of peroxisome proliferator-
activated receptor gamma (Swiss-Prot ID, P37231), which eliminates the phosphorylation site at Ser112, is
known to cause obesity
10
. The Pro387Leu substitution (dbSNP ID, rs16995309) of tyrosine-protein phosphatase
non-receptor, type 1 (Swiss-Prot ID, P18031) is associated with type II diabetes mellitus
14,16
. We only found
these two polymorphic phosphovariants that are related to disease susceptibility. As shown in Tables 13, 21
phosphovariants are polymorphisms, and the biological significance of 19 of these polymorphisms is not yet
known. In addition, we do not know the biological significance of most of the polymorphic phosphovariants
predicted in our study. Considering the importance of phosphorylation in protein function, polymorphic
phosphovariants may well be involved in specific diseases or phenotypes.
Apart from the characteristics of the three types of phosphovariants already suggested, there are other
fundamental differences between the type I and other phosphovariants. Type I phosphovariants completely add
or remove a phosphorylation site because kinases can only donate a phosphor moiety to an amino acid with a
hydroxyl group. However, type II and III phosphovariants can significantly affect kinase kinetics without
completely changing the kinase's recognition site. For example, the Pro387Leu substitution of PTPN1 removes
only 75% of the phosphorylation of Ser386 by CDC2 kinase in vitro, rather than 100% (Table 3)
14
.
We can explain phenotypic variations and diseases in terms of phosphovariants in more cases than we
have anticipated. Of the human proteins registered in the Swiss-Prot database, 25.5% are phosphoproteins and
60.9% of these phosphoproteins have multiple phosphorylation sites. The protein with the greatest number of
confirmed phosphorylation sites is the serine/arginine repetitive matrix protein 2 (Swiss-Prot ID, Q9UQ35), with
195 phosphorylation sites. Although we could only determine 62 phosphovariants with a database search, many
more phosphovariants must exist. Furthermore, if we count the mutations that add phosphorylation sites and the
type II and III phosphovariants that cannot be found without specific programs, the number of mutations
associated with changes in phosphorylation sites is much greater. We did not consider haplotypes in this study,
for simplicity. However, if two or more nearby variations are frequently linked, a nearby phosphorylation site
can be altered, although each variation individually does not affect the phosphorylation site. Therefore,
phosphovariants may account for a much greater proportion of human variation than we have anticipated.
Several issues must be resolved in future studies. Type II and type III phosphovariants can be
interchanged. For example, removed phosphorylation sites associated with type II () phosphovariants predicted
with PredPhospho may be recognized by kinases that are not included in our prediction models. In such cases,
these variations are type III phosphovariants, not type II (). Conversely, if a phosphorylation site is falsely
classified as a site recognized by multiple kinases, instead of by one true kinase, and if a variation affects only
some of these kinases, this is a type II phosphovariant incorrectly predicted to be a type III phosphovariant.
Other points that must be improved are the low sensitivity achieved with the high-specificity option and the low
specificity achieved with the no-specificity option of PredPhospho (Table 4). Moreover, the number of types of
kinases that we can predict must be increased and haplotypes should be considered in future studies.
Our method can be used in pathophysiological studies of mutations and in the selection of polymorphisms
of clinical and phenotypical importance. Many of the papers that have described the variations, shown in Tables
13, did not mention that the variations could be related to changes in phosphorylation sites. This could be
attributable to the lack of a specific database that connects mutations with phosphorylation sites, or the lack of a
general understanding of the association between phosphorylation and mutation. The type I (+), II, and III
phosphovariants we have defined cannot be identified simply by database analyses. Specific programs are
required to identify these phosphovariants. Accordingly, many nonsense point mutations whose functional
mechanisms are unknown can be reconsidered in terms of phosphovariant. Furthermore, if some mutations are
predicted to be phosphovariants with our system, further research will clarify the cause of the associated disease
or protein function. Our system can be used to select meaningful variations among endless numbers of newly
identified polymorphisms. As sequencing techniques advance, a large number of genetic variations are
emerging. At present, comparison of whole genomes of individuals is possible, because the human genome can
be sequenced in two months
24
. A comparison of phosphovariants between individuals or between species can be
undertaken before amino acid variations or nucleic acid variations are compared in whole genomes. A reverse
genetic approach for unknown protein functions or phenotypic variations is possible with proven
phosphovariants. The screening and prediction of phosphovariants can be a starting point for further research.
Methods
PredPhospho
We created classifiers of various kinases by training SVMs with phosphorylation site sequences and
nonphosphorylated site sequences. In other words, our classifiers determine whether serine, threonine, or
tyrosine residues within a sequence can be phosphorylated or not. "Phosphorylated site sequences" refers to
peptide sequences with a serine, threonine, or tyrosine residue located at the center, and which are
phosphorylated. Conversely, "nonphosphorylated site sequences" are sequences with a serine, threonine, or
tyrosine residue located at the center, which cannot be phosphorylated. We obtained phosphorylated site
sequences from public databases: the Swiss-Prot (release 54.8) and the Human Protein Resource Database
(HPRD, release 7). Nonphosphorylated site sequences were taken from laboratory data confirmed by MS (see
Supplementary Material online).
Manning et al. found 518 human protein kinase genes in the human genome sequence, using the hidden
Markov model (HMM) profile, and confirmed the identities of more than 90% of the identified kinase genes
using cDNA cloning
25
. They also classified the protein kinase superfamily into nine broad groups, and
subdivided the groups into 134 families and 204 subfamilies, using sequence comparisons of the kinase catalytic
domains. We classified the phosphorylated site sequences according to their kinases and created the classifiers in
a kinase-specific manner. Because of the limitations of the phosphorylated sequence data presently available in
public databases, we can make classifiers for only six kinase groups: AGC, CAMK, CK1, CMGC, STE, and TK;
and 18 kinase families: AKT, CAMK2, CAMKL, CDK, CK1, CK2, GSK, IKK, JakA, MAPK, PDGFR, PIKK,
PKA, PKC, RSK, Src, STE20, and Syk (all abbreviations are shown in the footnote to Supplementary Table 1
online). The detailed algorithms and methods were described in the Supplementary Material (online).
Evaluation of the system
The performance status of the prediction for each kinase group and family is shown in Supplementary
Table 2 (online). The performance of the prediction with combinations of all the kinase group models or all the
family group models is not the numerical multiplication of the performance of each model. Therefore, to
evaluate the performances of the predictions at the kinase group level or at the family level, we tested two
proven real data sets, which were compiled with MS experiments. Data set I was created by Olen et al.
5
, who
identified phosphorylation sites in proteins from HeLa cells and classified the phosphopeptides according to their
definition. Four classes are based on their localization probabilities: < 0.25, 0.25
<0.75 without kinase motifs,
0.25
<0.75 with kinase motifs and 0.75. We used only monophosphopeptides that had localization
probabilities of at least 0.75. Data set II was derived from the paper of Beausoleil et al.
4
and we used those of
their phosphopeptides with a localization certainty of > 99.4% (see Supplementary Fig. 2 online). We selected
phosphorylation sites and nonphosphorylated sites from these two data sets. To avoid overestimating the
performance of PredPhospho, we discarded sequences that were more than 70% identical to sequences used for
training PredPhospho. We also avoided using false nonphosphorylated sites by omitting those sites that are listed
as phosphorylation sites in Swiss-Prot or HPRD. We tested the two kinds of data sets not only with our
PredPhospho, but also with Scansite.
Prediction of phosphovariants
We extracted information about human genetic variations from SwissVariant of the Swiss-Prot database.
SwissVariant includes single amino acid polymorphisms and missense mutations
26
. The number of variations
listed in SwissVariant was 33,651. We consulted the Swiss-Prot database and HPRD about their effects, and the
references to these variations and phosphorylation sites. With PredPhospho and Scansite, we predicted the
phosphorylation sites and related kinases for the original sequences and the variant sequences. The
phosphovariants could be identified when the phosphorylation sites or interacting kinases were altered between
the original sequence and the variant sequence. If the phosphorylation site is in the same location as the
variation, it is type I. In type II phosphovariants, the variation is not in the same location as the phosphorylation
site. We added the symbol (+) to types I and II when the phosphovariants added new phosphorylation sites, and
() when the phosphovariants removed phosphorylation sites [e.g., type I (+] or type I ()]. Type III
phosphovariants are caused by changes in the types of kinases involved, rather than in the phosphorylation site
itself, regardless of the locations of the variations. One variation can include more than one class of
phosphovariant, because one variation can affect two or more phosphorylation sites. We predicted
phosphovariants at the kinase group level and the family level. The predictions at the family level are more
sensitive, but less specific, than those at the group level.
To minimize false negatives, we varied the specificity
options (95%, 97%, 98%, or 99%) according to the specificity of each model (Supplementary Table 3 online).
The specificity options are described in the Supplementary Material (online).
Sequence logos
We obtained 562 phosphorylation site sequences recognized by the CMGC kinase group from Swiss-
Prot and HPRD. We trimmed the sequences as 6 symmetric residues centered phospohrylation sites. We aligned
the sequences and obtained a sequence logo using the web program (http://weblogo.berkeley.edu/logo.cgi).
WWW programs
The PredPhospho (version 2) and PhosphoVariant were implemented using the PERL (version 5.8.8)
programming language and MySQL (version 5.0.18). The PhosphoVariant is a database for the definite and
possible human variants changing phosphorylation sites and their interacting kinases. They are available at
http://phosphovariant.ngri.go.kr/seq_input_predphospho2.htm and http://phosphovariant.ngri.go.kr, respectively.
Acknowledgement
This study was supported by Health Fellowship Foundation.
Author contributions
This study was designed by J.H.K. (corresponding author), R.G.M. (first author), K.W.K. and O.K.S.
Programming and web design were performed by J.H.K. and R.G.M. Literature search and data analysis were
done by J.H.K., R.G.M., P.S., K.W.K. and O.K.S. Paper was written by J.H.K., R.G.M., and P.S.
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Table 1 Examples of type I phosphovariants
(a) Type I(-) phosphovariants
Gene
name
SWISS-
PROT ID
Variation site
a
(SWISS-PROT
variant ID)
Phosphor-
ylation site
Effect
b
Reference(s)
for
variation
c
Reference(s)
for phosphor-
ylation site
d
Phosphovariants causing Mendelian inheritant diseases
EDNRB P24530
S305N
(VAR_003472)
S305
Hirschsprung disease type 2
8852659
14636059
FANCA O15360
S858R
(VAR_017498)
S858 Fanconi
anemia
10094191
11091222
17924679
KCNJ1 P48048
S219R
(VAR_019726)
S219
Bartter syndrome type 2
8841184
8621594
L1CAM P32004
S1194L
(VAR_003947)
S1194
Hydrocephalus due to
stenosis of the aqueduct of
Sylvius
Mental retardation, aphasia,
shuffling gait, and adducted
thumbs syndrome
8556302
7881431
17081983
MAPT P10636
S622N
(VAR_010350)
S622
Frontotemporal dementia
and parkinsonism linked
chromosome 17
10208578 7706316
MAPT P10636
S637F
(VAR_019665)
S637 Pick
disease
11891833
11104762
9199504
MAPT P10636
S669L
(VAR_019667)
S669
Fatal respiratory
hypoventilation
14595660 11104762
MITF O75030
S405P
(VAR_010302)
S405
Waardenburg syndrome
type IIa
8589691 10587587
NFKBIA P25963
S32I
(VAR_034871)
S32
Autosomal dominant
anhidrotic ectodermal
dysplasia with
immunodeficiency
14523047
10882136
9721103
8601309
16319058
10723127
9214631
PER2 O15055
S662G
(VAR_029080)
S662
Familial advanced sleep-
phase syndrome
11232563 11232563
PTPN11 Q06124
Y62D
(VAR_015605)
Y62
Patients with Noonan
syndrome 1 manifesting
juvenile myelomonocytic
leukemia
11992261
12325025
12960218
12717436
15951569
15592455
RAF1 P04049
S259F
(VAR_037809)
S259
Noonan syndrome type 5
17603483
8349614
11997508
11971957
10576742
RAF1 P04049
T491R
(VAR_037819)
T491
Noonan syndrome type 5
17603483
11447113
RAF1 P04049
T491I
(VAR_037818)
T491
Noonan syndrome type 5
17603483
11447113
RPS6KA3 P51812
S227A
(VAR_006195)
S227 Coffin-Lowry
syndrome 8955270 17192257
STAT3 P40763
Y657C
(VAR_037381)
Y657
Hyperimmunoglobulin E
recurrent infection
syndrome autosomal
dominant
17881745 15037656
TGFBR2 P37173
Y336N
(VAR_022352)
Y336
Loeys-Dietz aortic
aneurysm syndrome
15731757 9169454
TNNI3 P19429
S166F
(VAR_029454)
S166
Hypertrophic
cardiomyopathy
12974739 11121119
TSC1 Q92574
T417I
(VAR_009403)
T417
Tuberous sclerosis complex,
could be a polymorphism
10570911
10607950
14551205
Phosphovariants found in cancer
CDH1 P12830
S838G
(VAR_001322)
S838 Ovarian
cancer 8075649
10671552
CTNNB1 P35222
S23R
(VAR_017612)
S23
Hepatocellular carcinoma,
no effect
10435629
12027456
12027456
CTNNB1 P35222
S33F
(VAR_017617)
S33
Pilomatrixoma,
medulloblastoma and
hepatocellular carcinoma
10666372
10435629
10192393
12000790
12114015
11818547
CTNNB1 P35222
S33L
(VAR_017618)
S33 Hepatocellular
carcinoma
10435629
12000790
12114015
11818547
CTNNB1 P35222
S37A
(VAR_017624)
S37
Medulloblastoma,
hepatocellular carcinoma
12027456,1
0435629,
10666372
12000790
12114015
11818547
CTNNB1 P35222
S37C
(VAR_017625)
S37
Pilomatrixoma,
hepatoblastoma
9927029,
10192393
12000790
12114015
11818547
CTNNB1 P35222
S37F
(VAR_017626)
S37 Pilomatrixoma
10192393
12000790
12114015
11818547
CTNNB1 P35222
T41A
(VAR_017629)
T41
Hepatoblastoma and
hepatocellular carcinoma,
also in a desmoid tumor
12051714
10398436
9927029
12027456
10655994
10435629
12051714
12114015
11818547
12000790
CTNNB1 P35222
T41I
(VAR_017630)
T41
Pilomatrixoma and
hepatocellular carcinoma
10192393
10435629
12051714
12114015
11818547
12000790
CTNNB1 P35222
S45F
(VAR_017631)
S45 Hepatocellular
carcinoma
10435629
12051714
12000790
11955436
CTNNB1 P35222
S45P
(VAR_017632)
S45 Hepatocellular
carcinoma
10435629
12051714
12000790
11955436
FAM10A4 Q8IZP2
S71L
(VAR_023644)
S71
B-Cell leukemia, multiple
myeloma, and prostate
cancer
12079276 17081983
MET P08581
Y1230C
(VAR_006292)
Y1230
Hereditary papillary renal
carcinoma
9140397 12475979
MET P08581
Y1230H
(VAR_006293)
Y1230
Hereditary papillary renal
carcinoma
9140397 12475979
NME1 P15531
S120G
(VAR_004625)
S120 Neuroblastoma 8047138
8810265
RB1 P06400
S567L
(VAR_005579)
S567 Retinoblastoma
10671068
2594029
10207050
TP53 P04637
T155A
(VAR_005901)
T155 Esophageal
cancer 1868473
12628923
Phosphovariants related with polymorphism
BARD1 Q99728
S186G
S186 polymorphism (rs16852741)
15855157
C10orf11 Q9H2I8
S153F
(VAR_033686)
S153 polymorphism
(rs35349706)
16964243
CTNND1 O60716
Y217C
(VAR_020929)
Y217 polymorphism
(rs11570194)
15592455
16212419
CTPS P17812
S571I
(VAR_027055)
16097034
17081983
HIF1A Q16665
T796A
(VAR_015854)
T796 polymorphism
(rs1802821)
17382325
INSR P06213
Y1361C
(VAR_015933)
KRT36 O76013
T315M
(VAR_020306)
MYH15 Q9Y2K3
T1125A
(VAR_030238)
PDLIM5 Q96HC4
S136F
(VAR_023779)
S136 polymorphism
(rs2452600)
17287340
PNN Q9H307
S671G
(VAR_023368)
S671 polymorphism
(rs13021)
10095061 17287340
SUB1 P53999
S11G
S11 polymorphism (rs17850527)
15489334
17081983
16689930
SRRM2 Q9UQ35
S883C
(VAR_027260)
S883 polymorphism
(rs17136053)
17287340
TP53 P04637
S366A
(VAR_022317)
S366 Polymorphism
9183006
(b) Type I(+) phosphovariants
Gene
name
SWISS-
PROT ID
Variation site
Phosphor-
ylation site
Effect
Reference(s)
for variant
Reference(s)
for phosphor-
ylation site
DDX27 Q96GQ7
G766S
S766
Unknown
16565220 16565220
a
Locations and amino acid changes of the variations in the proteins.
b
The meanings or consequences of the variations. We referred to the feature tables of Swiss-Prot for these
effects. If the polymorphisms are enrolled in dbSNP, the IDs of dbSNP are written in the parentheses.
c
Pubmed ID for the references of the variations
d
Pubmed ID for the references of the phosphorylation sites
Protein names which are abbreviated by their gene names: Catenin
-1, CTNNB1; Epithelial cadherin
[Precursor], CDH1; Probable ATP-dependent RNA helicase DDX27, DDX27; Endothelin B receptor
[Precursor], EDNRB; Protein FAM10A4, FAM10A4; Fanconi anemia group A protein, FANCA; ATP-sensitive
inward rectifier potassium channel 1, KCNJ1; Keratin, type I cuticular Ha6, KRT36; Neural cell adhesion
molecule L1, L1CAM; Microtubule-associated protein tau, MAPT; Hepatocyte growth factor receptor
[Precursor], MET; Microphthalmia-associated transcription factor, MITF; NF--B inhibitor
, NFKBIA;
Nucleoside diphosphate kinase A, NME1; Period circadian protein homolog 2, PER2; Tyrosine-protein
phosphatase non-receptor type 11, PTPN11; RAF proto-oncogene serine/threonine-protein kinase, RAF1;
Retinoblastoma-associated protein, RB1; Ribosomal protein S6 kinase alpha-3, RPS6KA3; Signal transducer
and activator of transcription 3, STAT3; TGF-beta receptor type-2 [Precursor], TGFBR2; Cardiac troponin I,
TNNI3; Cellular tumor antigen p53, TP53; Hamartin, TSC1
Table 2 Examples of type II(-) phosphovariants.
Gene
name
SWISS-
PROT ID
Variation site
(SWISS-PROT
variant ID)
Removed
phosphorylation site
(related kinases)
Effect
Reference(s)
for variant
Reference(s)
for phosphor-
ylation site
DUT P33316
P100S
(VAR_022314)
S99
a
(CDC2)
Polymorphism
17081983
8631817
GJA1 P17302
P283L
(VAR_014101)
S282
a
(ERK1,
ERK2 and MAPK7)
Polymorphism
(rs2228974)
8631994
9535905
PPARG P37231
P113Q
(VAR_010724)
S112
b
(ERK2, JNK1
and MAPK8)
Obesity and
polymorphism
(rs1800571)
9753710 9030579
RXRA P19793
P261L
(VAR_014620)
S260
a
(ERK2 and
MAPK7)
Polymorphism
(rs2234960)
12048211
If the variations substitute the proline residues at position +1 relative to the phosphorylation sites into other
amino acids, the nearby phosphorylation sites recognized by the CMGC kinase group can be eliminated or the
efficiency of phosphorylation in that site is significantly decreased.
a
The removals of the phosphorylation sites by the variation have not been confirmed by experiments. However,
the removals of the phosphorylation sites are highly possible because the nearby phosphorylation sites are
proved to be recognized by the CMGC group.
b
The removal of the phosphorylation site by the variation has been confirmed by a experiment
10
.
Protein names which are abbreviated by their gene names: Deoxyuridine 5'-triphosphate nucleotidohydrolase,
mitochondrial [Precursor], DUT; Gap junction
-1 protein, GJA1; Peroxisome proliferator-activated receptor ,
PPARG; Retinoic acid receptor RXR-
, RXRA
Table 3 Possible examples of type III phosphovariants.
(a) A possible example of type III phosphovariant
Gene
name
SWISS-
PROT ID
Variation site
(SWISS-PROT
variant ID)
Related
phosphorylation site
(kinase recognizing
it)
Effect
Reference(s)
for variant
Reference(s)
for phosphor-
ylation site
PTPN1 P18031
P387L
(VAR_022014)
S386 (CDC2 and
CK2)
Low glucose
tolerance and
polymorphism
(rs16995309)
15919835
9600099
8491187
(b) Possible examples of phosphovariants which can be classified as type I or III.
Gene
name
SWISS-
PROT ID
Variation site
(SWISS-PROT
variant ID)
Related
phosphorylation site
Effect
Reference(s)
for variant
Reference(s)
for phosphor-
ylation site
BRCA1 P38398
S1217Y
(VAR_020695)
S1217
Breast cancer
and breast-
ovarian cancer
14722926 17081983
CASP8 Q14790
S219T
(VAR_025816)
S219
polymorphism
(rs35976359)
17525332
CDK2 P24941
Y15S
(VAR_016157)
Y15
polymorphism
1396589
12912980
12972555
15144186
CTNNB1 P35222
S33Y
(VAR_017619)
S33 Pilomatrixoma
12027456
10192393
12000790
12114015
11818547
CTNNB1 P35222
S37Y
(VAR_017627)
S37
Hepatocellular
carcinoma
10435629
12000790
12114015
11818547
TEK Q02763
Y897S
(VAR_008716)
Y897
Dominantly
inherited venous
malformations
10369874 11080633
XRCC1 P18887
S485Y
(VAR_014779)
S485
polymorphism
15066279
The reasons why these variations are classified as type III are detailed in the text.
Protein names which are abbreviated by their gene names: Breast cancer type 1 susceptibility protein; BRCA1;
Caspase 8, CASP8; Cyclin dependent kinase 2, CDK2; Catenin
-1, CTNNB1; Tyrosine-protein phosphatase
non-receptor type 1, PTPN1; Angiopoietin-1 receptor [Precursor], TEK; DNA repair protein XRCC1, XRCC1
Table 4 The general performance test of the PredPhospho and Scansite for two real data sets
(a) The number of each data set
Data
I
a
Data
II
a
(+)
b
(-)
b
(+) (-)
Ser/Thr
b
1860 926 3017 315
Tyr
b
38 95
359
11
(b) PredPhospho
Prediction at the group level
Prediction at the family level
Data I
Data II
Data I
Data II
Specificity
c
Sn (%)
Sp (%)
Sn (%)
Sp (%)
Specificity
Sn (%)
Sp (%)
Sn (%)
Sp (%)
No
79.40 60.62 75.47 61.04
No
95.36 29.29 93.60 30.98
95%
73.24 72.09 65.76 72.39 95% 92.68 42.80 88.95 46.63
97%
53.37 80.22 48.31 79.45 97% 88.46 56.81 83.50 57.36
98%
43.11 89.23 38.92 89.26 98% 82.03 66.80 75.44 62.88
99%
23.39 95.79 20.05 96.62 99% 73.18 72.67 66.73 72.39
(c) Scansite
Data I
Data II
Stringency
d
Sn (%) Sp (%)
Sn (%) Sp (%)
Low 84.47
52.60
83.92
57.06
Medium 48.63 85.21 43.81 87.73
High 16.39
96.77
13.60
95.71
a
Different data sets compiled with mass spectrometer. See the text for the detail explanation for data set I and II.
b
The types of amino acids located at the center of peptides. We annotated the peptides as (+) if the Ser/Thr or
Tyr at the center of the peptides is phosphorylated. On contrary, we designated the peptide as (-) if the center of
the peptides is not phosphorylated.
c
Options of the specificity. For example, `99%' specificity option mean cutoff value is adjusted for each model
to have 99% specificity, and `No' specificity option means each model has default cutoff value without
adjustment of specificity (See supplementary material online).
d
Scansite has three levels of stringency: high, medium and low. High stringency involves low sensitivity and
high specificity, whereas low stringency involves high sensitivity and low specificity.
Abbreviations: sensitivity, Sn; specificity, Sp
Table 5 The number of the phosphovariants
(a) The number of the phosphovariants predicted with PredPhospho at the kinase group level
Type
I(-)
Type I(+)
Type II(-)
Type II(+)
Type III
Specificity
No 1729 2036 5455 4980 5299
95% 981 1195 1304 1070 986
97% 613 778 694 542 401
98% 314 409 329 213 151
99% 116 150 98 52 21
(b) The number of the phosphovariants predicted with PredPhospho at the kinase family level
Type
I(-)
Type I(+)
Type II(-)
Type II(+)
Type III
Specificity
No 3039 3717 3969 3926 23955
95% 2379 2910 2882 2840 8113
97% 1720 2104 1439 1483 2390
98% 1268 1551 783 862 1213
99% 946 1180 539 548 638
(c) The number of the phosphovariants predicted with Scansite
Type
I(-)
Type I(+)
Type II(-)
Type II(+)
Type III
Stringency
Low 1581 1852 4255 3773 7698
Medium
443 498 487 384 152
High 83 128 35 28 1
Table 6 Predicted
a
phosphovariants whose phosphorylation sites were confirmed in human or orthologous
proteins
(a) Type I(-) phosphovariants
Gene
name
SWISS-
PROT ID
Site
(SWISS-PROT
variant ID)
Related
phosphorylation site
Effect
Reference(s)
for variant
Reference(s)
for phosphor-
ylation site
ACIN1 Q9UKV3
S478F
(VAR_022033)
S478
Polymorphism
(rs3751501)
17242355
(mouse)
b
MECP2 P51608
S229L
(VAR_018200)
S229 Polymorphism
10767337
12872250
17046689
(rat)
a
PAH P00439
S16P
(VAR_000869)
S16 Phenylketonuria
1679029
2246858
1301187
7387651
(rat)
b
(b) Type II(-) phosphovariants
Gene
name
SWISS-
PROT ID
Site
(SWISS-PROT
variant ID)
Related
phosphorylation site
Effect
Reference(s)
for variant
Reference(s)
for phosphor-
ylation site
CHGB P05060
R178Q
(VAR_020287)
S183
Polymorphism
(rs910122)
16807684
GTSE1 Q9NYZ3
R506W
(VAR_024154)
S504
Polymorphism
(rs140054)
10591208 16964243
LIG1 P18858
P52L
(VAR_020194)
S51
Polymorphism
(rs4987181)
16964243
(c) Type III phosphovariant
Gene name
SWISS-
PROT
ID
Site
(SWISS-PROT
variant ID)
Related
phosphorylation
site
1
Effect
Reference(s)
for variant
Reference(s)
for phosphor-
ylation site
ABCB11 O95342
R698H
(VAR_035352)
S701 Polymorphism
16763017
17242355
(mouse)
b
ABL1 P00519
P810L
(VAR_032678)
S809
Polymorphism
17344846
17081983
AQP2 P41181
P262L
(VAR_015255)
S261
Nephrogenic
diabetes
insipidus
9550615
15509592
16641100
(rat)
b
CASP8 Q14790
S219T
(VAR_025816)
S219
Polymorphism
(rs35976359)
17525332
EIF4G3 O43432
P496A
(VAR_034009)
S495
Polymorphism
(rs35176330)
17081983
16964243
MYBPC3 Q14896
G278E
(VAR_019891)
S275
Familial
hypertrophic
12707239
9784245
(chicken)
b
cardiomyopathy
type 4
PKMYT1 Q99640
R140C
(VAR_019928)
S143
Polymorphism
(rs4149796)
17192257
PPP1R12B O60237
R836K
(VAR_024177)
S839
Polymorphism
(rs3881953)
17242355
(mouse)
b
PARK7 Q99497
E64D
(VAR_020493)
Y67
Autosomal
recessive early-
onset Parkinson
disease 7
15365989
14607841
15592455
PNN Q9H307
S671G
(VAR_023368)
S667
Polymorphism
(rs13021)
10095061 17287340
SH3PXD2A Q5TCZ1
R1035Q
(VAR_030782)
S1038
Polymorphism
(rs3781365)
17525332
WDR91 A4D1P6
L257P
(VAR_033358)
S256
Polymorphism
(rs292592)
15489334
14702039
16964243
Eight of the type I(-) phosphovariants (VAR_006195, VAR_023368, VAR_023779, VAR_023644,
VAR_030238, VAR_020306, VAR_033686, and VAR_027260), and a type III phosphovariant (VAR_020695)
were also predicted. However, their detail information are already written in Table 1 and 3.
a
The prediction was done with the 99% specificity option of PredPhospho at the kinase family level.
b
The experiment was done in the proteins of other than human. The names of the species are written in the
parenthesis.
Protein names which are abbreviated by their gene names: Proto-oncogene tyrosine-protein kinase ABL1,
ABL1; ATP-binding cassette sub-family B member 11, ABCB11
the nucleus, ACIN1; Aquaporin-2, AQP2; Caspase-8 [Precursor], CASP8; Secretogranin-1 [precusor], CHGB;
Eukaryotic translation initiation factor 4 gamma 3, EIF4G3; G2 and S phase-expressed protein 1, GTSE1; Zinc
finger protein KIAA1802, KIAA1802; DNA ligase 1, LIG1; Methyl-CpG-binding protein 2, MECP2; Myosin-
binding protein C, cardiac-type, MYBPC3; Phenylalanine-4-hydroxylase, PAH; Parkinson disease protein 7,
PARK7; Membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase, PKMYT1; Pinin, PNN;
Protein phosphatase 1 regulatory subunit 12B, PPP1R12B; Ribosomal protein S6 kinase alpha-3, RPS6KA3;
SH3 and PX domain-containing protein 2A, SH3PXD2A; WD repeat-containing protein 91, WDR91
Figure legends
Figure 1. Schematic illustration of phosphovariants according to their types.
Figure 2. Sequence logos of amino acid sequences near phosphorylation sites recognized by the CMGC
kinase group.
The horizontal axis represents sequential positions relative to the phosphorylation site. The vertical axis
represents decreases in uncertainty. Each letter refers to an amino acid. As the frequency of an amino acid at a
given position increases, its height increases.
