Supplemental Material can be found at:
http://www.mcponline.org/cgi/content/full/M800466-MCP200/DC1

Research

In Silico Analysis of Phosphoproteome Data
Suggests a Rich-get-richer Process of
Phosphosite Accumulation over Evolution*□
S

Nozomu Yachie‡§, Rintaro Saito‡¶储, Junichi Sugahara‡§, Masaru Tomita‡§¶,and Yasushi Ishihama‡**

From the ‡Institute for Advanced Biosciences, Keio University,
403-1, Daihoji, Tsuruoka, Yamagata 997-0017, Japan, §Systems Biology Program, Graduate School of Media and Governance and¶Faculty of Environment and Information Studies, Keio University,
Endo 5322, Fujisawa, Kanagawa 252-8520, Japan, and **Precursory
Research for Embryonic Science and Technology (PRESTO), Japan
Science and Technology Agency, Sanbancho Building, Sanbancho 5,
Chiyodaku, Tokyo 102-0075, Japan
Received, October 9, 2008, and in revised form, January 7, 2009
Published, MCP Papers in Press, January 9, 2009, DOI 10.1074/
mcp.M800466-MCP200© 2009 by The American Society for Biochemistry and Molecular Biology, Inc.
This paper is available on line at http://www.mcponline.org

Protein phosphorylation is an important and ubiquitous
post-translational modification that regulates a variety of biological processes in various organisms (1– 4). Reversible
phosphorylations of serine, threonine, and tyrosine residues
are critical steps in the control of signal transduction pathways (1– 4). Recent advances in MS-based technologies andphosphopeptide enrichment methods have allowed highthroughput and large scale in vivo phosphosite mapping for awide variety of organisms such as human (5– 8), mouse (9),yeast (10 –12), fly (13, 14), bacteria (15, 16), and plants (17–19). Moreover information on several hundred to several thousand phosphosites from each study has been gathered inpublic databases such as Phospho.ELM (20), PhosphoSitePlus, PHOSIDA (21), and UniProt (22). However, the total
number of phosphosites and most of their biological functions
are still unknown. Similarly only about 10% of the estimated
500 – 600 human kinases target known phosphosite consensus sequences within their substrate proteins (23). Although
the tyrosine phosphoproteome in Arabidopsis was recently
published (24), the corresponding tyrosine kinases have notbeen identified because of the lack of known consensus
sequences activated by tyrosine kinases.
Computational data-mining approaches have been required to extract information from the large amount of accumulated phosphosite data obtained from experimental approaches. These approaches have also been used to add
more meaningful information about each of the phosphosites
to understand the proteome-wide protein phosphorylation invarious organisms. One of the most useful strategies of computational data mining is to identify phosphorylated sequencemotifs by extracting consensus sequences from the sets ofamino acid sequences clustered around phosphorylated residues (25). A number of kinases and their corresponding recognition substrate motifs have been successfully identified by
the experimental approach of incubating each target kinasewith a combinatorial substrate peptide library and ATP, andthese data are registered in various databases, including the
Human Protein Reference Database (HPRD)1 (26). With thisknowledge of documented kinases and their related sequence motifs, we can use computational biology techniques
1

The abbreviations used are: HPRD, Human Protein Reference
Database; IPI, International Protein Index; AA, amino acid(s).

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Recent phosphoproteome analyses using mass spectrometry-based technologies have provided new insights
into the extensive presence of protein phosphorylation invarious species and have raised the interesting questionof how this protein modification was gained evolutionarily
on such a large scale. We investigated this issue by using
human and mouse phosphoproteome data. We initiallyfound that phosphoproteins followed a power-law distribution with regard to their number of phosphosites: most of
the proteins included only a few phosphosites, but someincluded dozens of phosphosites. The power-law distribution, unlike more commonly observed distributions such as
normal and log-normal distributions, is considered by thefield of complex systems science to be produced by aspecific rich-get-richer process called preferential attachment growth. Therefore, we explored the factors that may
have promoted the rich-get-richer process during phosphosite evolution. We conducted a bioinformatics analysis
to evaluate the relationship of amino acid sequences ofphosphoproteins with the positions of phosphosites andfound an overconcentration of phosphosites in specific regions of protein surfaces and implications that in many
phosphoproteins these clusters of phosphosites are activated simultaneously. Multiple phosphosites concentrated
in limited spaces on phosphoprotein surfaces may therefore function biologically as cooperative modules that areresistant to selective pressures during phosphoprotein evolution. We therefore proposed a hypothetical model bywhich the modularization of multiple phosphosites has
been resistant to natural selection and has driven the richget-richer process of the evolutionary growth of phosphosite numbers. Molecular & Cellular Proteomics 8:1061–1071, 2009.

On the Evolutionary Increase in Phosphosites

EXPERIMENTAL PROCEDURES

Phosphosite Data—We initially obtained the phosphosite data andphosphoproteome sequences from the UniProt database (22), which
incorporates large scale data from many high quality phosphoproteomics studies. The complete list of UniProtKB/Swiss-Prot (Release12.8) protein entries involving at least one phosphosite identified inhigh throughput phosphoproteomics studies was downloaded via theProtein Knowledgebase (UniProtKB) (33) in Extensible Markup Language (XML) format by querying the term scope: “PHOSPHORYLATION [LARGE SCALE ANALYSIS] AT.” Within each entry, Swiss-Prot
accession IDs, protein name, positions of phosphosites, amino acidsequence of the protein, and functional annotations were included.
When multiple Swiss-Prot IDs were included in an entry, throughout
this study we used the first one.
The downloaded phosphoproteome data were obtained mainly
from human (Homo sapiens), mouse (Mus musculus), and yeast

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Molecular & Cellular Proteomics 8.5(Saccharomyces cerevisiae). In human, among a total of 10,463
phosphosites in 3,290 phosphoproteins, there were 8,043 Ser(P)sites (in a total of 2,769 proteins), 1,496 Thr(P) sites (in 1,005
proteins), and 892 Tyr(P) sites (in 620 proteins). In mouse, within atotal of 4,894 phosphosites (in 2,089 proteins), there were 3,466
Ser(P) sites (in 1,648 proteins), 744 Thr(P) sites (in 573 proteins),
and 668 Tyr(P) sites (in 462 proteins). In yeast, within a total of 4,982
phosphosites (in 1,656 proteins), there were 4,069 Ser(P) sites(1,528 proteins), 887 Thr(P) sites (616 proteins), and 25 Tyr(P) sites(25 proteins).
Proteome Sequences—The human proteome sequences were
downloaded from the International Protein Index (IPI) of the European
Molecular Biology Laboratory-European Bioinformatics Institute(EMBL-EBI) database (34), which provides minimally redundant but
maximally complete sets of proteins for registered species. We obtained a set of 69,731 amino acid sequences.
Phosphoserine-based Motifs—From the HPRD (26), we obtained atotal of 171 Ser(P)-based human sequence motifs. Among thesemotifs, 158 were kinase and phosphatase motifs, and the others were
protein-binding motifs.
Distribution of Phosphosite Numbers among Proteins—We
counted the numbers of phosphosites in each protein by using prepared sets of phosphoproteins that included any type of phosphosite(i.e. all phosphoproteins), Ser(P) sites, Thr(P) sites, or Tyr(P) sites inhuman, mouse, and yeast. Then the cumulative probability distributions of phosphoproteins in each set were calculated with regard to
the numbers of all types of phosphosites, “only Ser(P) sites,” “onlyThr(P) sites,” and “only Tyr(P) sites”; in each distribution, the numbersof phosphosites were denoted on the horizontal axis (X), and the
proportion having more than X phosphosites of all the phosphoproteins in the set was indicated on the vertical axis (P⬎(X)). Each distribution was then approximated by a power-law function. When aprobability distribution density follows a power law, the probability ofvariable X (i.e. p(X)) is inversely proportional to the power of thevariable X with a power-law exponent ⫺␮, and its cumulative probability distribution density P⬎(X) also follows a power law with thepower-law exponent ⫺␮ ⫹ 1.

p共X兲 ⬀ X⫺␮ N P⬎共X兲 ⬀ X⫺␮⫹1(Eq. 1)

Therefore, to ascertain whether a distribution followed a power law,
we analyzed its cumulative probability distribution because the cumulative probability distribution can normalize the effect of noise where thevariable X is large with low p(X) arising from a small number of samples;
this region is called the long tail region of the power-law distribution.
Characteristics of Phosphoprotein Sequences—To investigate
whether the characteristics of phosphoproteins affect their distribution with regard to the number of phosphosites they contain, we
observed the lengths and compositions of the amino acid sequencesof phosphoproteins. In both human and mouse, we calculated thePearson’s correlation coefficients between the number of Ser(P) sites
and protein lengths and between the number of Ser(P) sites andnumber of Ser residues.
Probabilistic Logic-based Reannotation of Phosphosite Numbers—To control for possible redundant annotations in the UniProt
data, we newly estimated the number of phosphosites in the proteome
sequences. We adopted a probabilistic logic model of the proteomewide mapping of phosphopeptides and stochastically estimated thenumber of phosphosites within each protein where at least one of its
peptide sources matched multiple other protein sequences. We obtained human phosphopeptide data from the PHOSIDA database (21)and downloaded the human proteome sequences from IPI. Of all phosphosites within the phosphopeptides in the PHOSIDA database, we
selected only the Ser(P) sites that scored highly reliable experimental
post-translational modification probabilities (p ⬎ 0.75) (6).

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to discover additional phosphorylated motifs in the numerous
substrates shown in phosphoproteomics studies to be biologically phosphorylated. This has allowed us to reconstruct
the kinome on a large scale (27–29).
A comparative study of phosphoproteome data in multiple
species has revealed that a wide range of phosphoproteins
are relatively well conserved relative to non-phosphoproteins,and similarly many phosphoserine (Ser(P)), phosphothreonine(Thr(P)), and phosphotyrosine (Tyr(P)) phosphosites are wellconserved compared with non-phosphorylated sites (21). Under natural selection, the emergence of phosphoproteins andthe gain and loss of phosphosites should have changed theregulation of many intracellular systems, such as kinetic pathways, subcellular protein localization, and protein interactions
and stabilization. This triggered our interest in the evolution ofphosphoproteins and their phosphosites.
In this study, we combined statistical physics and computational biology to investigate the role of selective pressure in
the evolution of phosphoproteins and to create a model of theevolutionary gain of phosphosites. First using the human andmouse phosphoproteome data registered in public databases, we discovered that the number of phosphosites ineach phosphoprotein follows a power-law distribution, which
has been shown in complex systems science and statisticalphysics to emerge through a specific rich-get-richer process
called preferential attachment growth (30 –32). We therefore
hypothesized that phosphoproteins may have evolved
through a rich-get-richer process, gaining new phosphosites
according to a probability density proportional to their current
number of phosphosites. Starting from this hypothesis, wethen explored how this particular evolutionary pattern may
have arisen during natural selection and suggested that setsof phosphosites localized in limited spaces on protein surfaces may function as cooperative modules that are resistant
to selective pressures. Therefore, to explain phosphosite evolution, we proposed a model in which the evolutionary gain ofphosphosites follows a rich-get-richer process and evolution
is promoted by the development of cooperative functional
modules on protein surfaces.

On the Evolutionary Increase in Phosphosites

occurrence ratio of a at position i can be represented as pi,a. The
information entropy of a relative position i is defined as Hi (bit) and is
calculated as follows.

Hi ⫽ log2共20兲 ⫺冘pi,alog2共 pi,a兲(Eq. 2)a僆A

Finally we defined the mean information entropy Hmean (bit) for each
phosphoprotein by dividing the total information entropies of all the
relative positions between ⫺6 and ⫹6 by 13. Larger values of the
Hmean score express a higher consensus of sequences surrounding
Ser(P) sites in a protein; the maximum score is ⬃4.32, derived fromlog2(20), and a score of 0 means no consensus.
We also analyzed whether multiple phosphosites in a single protein
matched the same previously documented phosphosite-based motif.
By using the human Ser(P)-based motifs obtained from the HPRD, wesearched for the motifs that most commonly matched the Ser(P) sites
within the phosphoproteins. For each human phosphoprotein having
more than 10 Ser(P) sites, we calculated the percentage of Ser(P)sites that matched the most common motif in the protein. The negative control for each phosphoprotein was prepared by randomly
selecting the same number of phosphosites from the total pool ofhuman Ser(P) sites in UniProt. Then the occurrence percentage of themost common motif among the randomly selected set was calculated. This random procedure was repeated 1,000 times, and its mean
and S.D. were computed.
RESULTSPower-law Rule in Phosphoproteins—We analyzed the distribution of phosphoproteins in human and mouse with regard
to their number of Ser(P) sites using phosphoprotein dataobtained from the UniProt database. The cumulative probability density of each human phosphoprotein was approximated by a power-law exponent of ⫺1.92, and the r2 value of
Pearson’s correlation coefficient was 0.98 (Fig. 1A). The cumulative probability density of mouse proteins was approximated with a power-law exponent of ⫺2.00, and the r2 value
was 0.98 (Fig. 1B). Human and mouse phosphoproteins also
followed power-law distributions with high correlations withregard to their number of Ser(P)/Thr(P)/Tyr(P) phosphosites,
only Thr(P) sites, and only Tyr(P) sites (see supplemental Figs.
S1–S3) as did yeast phosphoproteins for all types of phosphosites (data not shown).
Next to investigate whether the power-law distributions
were potentially affected by the characteristics of the phosphoprotein sequences, we examined the correlations between the numbers of Ser(P) sites and the lengths of thephosphoproteins in human and mouse. The r2 values calculated from the linear approximation were 0.02 in human and0.05 in mouse (supplemental Fig. S4, A and B), indicating that
there was no correlation between the Ser(P) site numbers andthe lengths of the phosphoproteins. Similarly the number ofSer(P) sites and the number of Ser residues in the human andmouse phosphoproteins had r2 values of 0.05 and 0.10, respectively (supplemental Fig. S4, C and D). In fact, the probability distributions of human and mouse phosphoproteins
with regard to the number of Ser residues were log-normal
distributions (Fig. 2). Therefore, the power-law rule linking the

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According to the experimental procedure of PHOSIDA in which
trypsin is used to convert the protein mixture into a more analyzable
population of peptides (21), we integrated the redundancy of phosphopeptides at the sequence level and matched them to proteome
sequences on the basis of the following rules: the exclusive cleavageof a C-terminal Arg or Lys and the possible cleavage of the weakest
peptide bond between Asp (on the N-terminal side) and Pro (on the
C-terminal side) were induced in the trypsin digestion in the MSbased proteome analysis (35). When a phosphopeptide fragment
harboring k Ser(P) sites was redundantly matched to n protein sequences, we counted the expectation value of k divided by n (i.e. k/n)
for each of n proteins. Using this procedure, we created a probabilistic logic-based reannotated set of the number of Ser(P) sites in each
phosphoprotein. Then we analyzed the cumulative probability density
distribution of all phosphoproteins included in the reannotated set
according to their estimated number of Ser(P) sites.
Finally by downloading the cross-reference file of the protein IDs ofmultiple databases from the IPI, we integrated the information on thestochastically estimated number of Ser(P) sites with data from eachcorresponding phosphoprotein obtained from UniProt. Then using
the phosphoproteins present in both the UniProt set and the reannotated set, we demonstrated the respective distributions of phosphoproteins according to the Ser(P) site numbers in the UniProt data andthose in the reannotated set. Within the intersection set, the Pearson’s correlation coefficient between the number of Ser(P) sites ofrespective proteins annotated in UniProt and those in the reannotated
set was also calculated.
Localization of Phosphosites on Protein Surfaces—We evaluatedthe locations of phosphosites on the protein surfaces in the humanand mouse phosphoproteome data from UniProt. For each set of allamino acid sequences of phosphoproteins in human and mouse, weused a sliding window of a certain size (see below) with a 1 ⫺ aminoacid (AA) displacement and counted all the possible distances between two Ser(P) sites within the window when more than two Ser(P)sites were included in the window. Negative controls were generated
by a similar procedure; when multiple Ser(P) sites were observed
within the sliding window, we moved them to serine residues randomly selected from all the serine residues, including the actual Ser(P)sites, within the window and counted all the possible distances between two Ser(P) sites set randomly in silico.
The SURFACE database of protein surfaces (36) contains the distribution of residue lengths of amino acid segments appearing onprotein surfaces. According to the distribution, 85% of the patches
exposed on protein surfaces are composed of between 10 AA and 40
AA residues, and the peak of the distribution is around 20 AA. Therefore, to estimate the concentration of multiple phosphosites on theprotein surface, we adopted sliding window sizes of 40, 20, and 10 AA
using human and mouse phosphoproteomics data, and we compared
the probability distributions of all the possible distances between
pairs of Ser(P) sites with those of the negative controls. For each of
the analyses, the window size and object species were varied, thenegative control analyses were repeated 1,000 times, and the averageand the 95% confidence intervals of the probability distributions of
the negative controls were calculated.
Sequence Consensus around Phosphosites—We used the data setof Ser(P) sites obtained from UniProt to evaluate the sequence consensus of the amino acids surrounding the phosphosites within each
phosphoprotein of human and mouse. For each Ser(P) site of each
phosphoprotein, we identified the amino acids between the relative
positions of ⫺6 and ⫹6. Then by using each set of 13 AA including thesurrounding sequences, the information entropy of every relative
position was first calculated on the basis of information theory, suchas used in the sequence logo program (37). Let A be a class of 20
amino acids, and a is a given amino acid included within A. The

On the Evolutionary Increase in Phosphosites

FIG. 2. Probability distribution of
number of Ser(P) sites and Ser residues within phosphoproteins obtained from UniProt. Probability distributions of the number of Ser(P) sites (“#of pS”) within the human (A) and mouse(B) phosphoproteins obtained from the
UniProt database are shown; each pointshows the proportion of phosphoproteins with the number of Ser(P) sites indicated on the horizontal axis. Using thesame phosphoprotein set, the probability distributions of the number of Ser
residues (“# of S”) within each human (C)and mouse (D) phosphoprotein were calculated. In each figure, both axes are log
scale.

phosphosite numbers to protein probabilities was not causedby the characteristics of the primary sequences of aminoacids within the phosphoproteins.
More than one protein may have identical stretches ofamino acids, an issue that is not addressed in the UniProt
database, and we were concerned about the possible effectof redundant or duplicative annotation on the distribution

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pattern of phosphosite numbers of individual proteins. Therefore, we used PHOSIDA phosphopeptide data obtained with
an MS-based analysis to conduct a probabilistic logic-based
estimation of the numbers of phosphosites for each protein
within the IPI database, which consists of minimally redundant but maximally complete sets of documented proteins.
From this, we generated a stochastically reannotated set of

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FIG. 1. Cumulative probability distribution of number of Ser(P) sites within phosphoproteins obtained from UniProt. Distributions of
cumulative probabilities of human (A) and mouse (B) phosphoproteins obtained from the UniProt database are shown according to their
number of Ser(P) sites (“# of pS”); each plot shows the proportion of phosphoproteins having more Ser(P) sites than the corresponding variableindicated on the horizontal axis (see “Experimental Procedures”). In each figure, both axes are on log scales, and the optimal approximate lineof the power-law distribution is shown as a gray dotted line with the r2 value of the Pearson’s correlation coefficient. The approximate formulaof each distribution is shown in the dotted box; “C” means constant.

On the Evolutionary Increase in Phosphosites

phosphoproteins along with their estimated number of Ser(P)sites (see “Experimental Procedures”). The distribution of thereannotated phosphoproteins with regard to their number ofSer(P) sites was also proportional to a power law with an
exponent of ⫺1.73 and an r2 value of 0.99 (Fig. 3A). When we
focused only on phosphoproteins included in both UniProt
and the reannotated data, a power-law distribution of phosphoproteins with regard to the number of Ser(P) sites annotated in UniProt was observed with an exponent of ⫺2.00 andan r2 value of 0.98 (Fig. 3B). In this population, a power-low
distribution was also observed according to the number ofSer(P) sites estimated in the reannotated set with a power-law
exponent of ⫺1.76 and an r2 value of 0.97 (Fig. 3C). Moreoverthe number of Ser(P) sites estimated in the reannotated set
was correlated with the number in the UniProt database withan r2 value of 0.51 (Fig. 3D). Thus, the power-law rule in thenumber of phosphosites was not likely to arise from redundant proteome-wide mapping of phosphopeptides.
Furthermore we demonstrated that the observed powerlaw distribution was independent of protein abundance. We
were concerned that the phosphosites of abundant proteinsmight be more likely to be identified by MS, thereby resulting
in the power-law distribution. Therefore, we estimated theabsolute abundances of proteins in a lysate of HeLa cells at
the proteome level by calculating the exponentially modified
protein abundance index (emPAI (38)) in an LC-MS/MS ex-

periment. We also used phosphoproteome data we obtained
from the same cell lysate in our previous studies (5, 39, 40) tocount the numbers of phosphosites of individual proteins. We
found no correlation between the number of phosphosites
within respective proteins and their copy number (for details,
see supplemental Fig. S5). In fact, in this analysis, proteins
containing many phosphosites seemed to be less abundant
than other proteins (supplemental Fig. S5).
Localization of Phosphosites on Protein Surfaces—We hypothesized that the power-law distribution of phosphoproteins emerged during evolution according to the preferential
attachment growth principle whereby a phosphoprotein gains
new phosphosites according to a probability that is proportional to its current phosphosite number. To investigate whatmight have promoted the rich-get-richer growth of phosphosites during evolution, we conducted a bioinformatics
analysis using human and mouse data from UniProt to estimate phosphosite localization on the protein surface. By sliding a window of a given size along the phosphoprotein sequences, we were able to measure the distances between all
possible pairs of Ser(P) sites within each sliding window.
According to the SURFACE database, most stretches ofamino acids appearing on protein surfaces range from 10 to
40 AA with a peak at 20 AA. Therefore, we used these threevalues as the window sizes to predict the localization ofphosphosites on protein surfaces. Negative controls were

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FIG. 3. Cumulative probability distribution of number of Ser(P) sites within
phosphoproteins of the reannotatedset. The distribution of cumulative probabilities of phosphoproteins according
to their estimated number of Ser(P) sites(“# of pS”) in the human phosphoproteins reannotated by using PHOSIDA
and IPI data (A) is shown. The distribution of phosphoproteins in the intersection between UniProt and the reannotated set according to the number ofSer(P) sites annotated in the UniProt
data (B) and in the reannotated set (C) isshown. For a detailed explanation of the
cumulative probability distribution, see
the legend to Fig. 1. Within the intersection set, the correlation between thenumber of Ser(P) sites annotated in UniProt and those estimated in the reannotated set was calculated with the r2 valueof the Pearson’s correlation coefficient(D). The dotted gray line indicates its
straight line approximation.

On the Evolutionary Increase in Phosphosites

generated by randomly placing Ser(P) sites within the sliding
windows onto any serine residues within the respective windows (see “Experimental Procedures”).
In all the results obtained by displacing sliding windows of 40,
20, and 10 AA by 1 AA on the sequences of human and mouse
phosphoproteins, the distances between pairs of Ser(P) sites
within the windows were significantly shorter than those generated by negative control trials (Fig. 4). For each window size ineach species, we compared the probability distribution of distances between two actual Ser(P) sites with the higher confidence bound of the probability distribution estimated by 1,000
repetitions of the negative controls. When the sliding window

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size of 40 AA was used on the human data, the probabilities of
distances of two Ser(P) sites under 5 were significantly higher
than those estimated for the negative controls in human (p ⬍0.05) (Fig. 4A), and the probabilities of distances under 4 weresignificantly higher than those of the negative controls in mouse(p ⬍ 0.05) (Fig. 4D). Similarly the probabilities of distances under
3 were significantly higher than those of the negative controls
when the sliding window of 20 AA was used in both human andmouse (p ⬍ 0.05) (Fig. 4, B and E), and the probabilities of
distances under 2 were significantly higher than those of thenegative controls when the sliding window of 10 AA was used inboth species (p ⬍ 0.05) (Fig. 4, C and F). Although some

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FIG. 4. Distribution of distances between two Ser(P) sites within limited spaces of phosphoprotein sequences. Using sliding windowsof 40, 20, and 10 AA on the phosphoproteome sequences with a 1-AA displacement, the probability density distributions of distances between
all possible pairs of Ser(P) (pS) sites included within the sliding windows are shown as the black-filled graphs in A–C for human and D–F formouse. Sequence distance (d) between two Ser(P) sites is shown by the number of amino acid residues between the two sites; thus, d ⫽ 0means the two sites are next to each other. In each figure, the gray solid line indicates the average of distributions calculated from 1,000
random trials as negative controls, and both sides of the 95% confidence intervals estimated by the negative trials are indicated by gray dottedlines (see “Experimental Procedures”).

On the Evolutionary Increase in Phosphosites

TABLE IThe 20 human phosphoserine proteins containing the highest numbers of Ser(P) sites
Proteins are sorted by number of Ser(P) sites annotated in the UniProt database; the top 20 are listed (for the full list, see the supplemental
Table S1). The column “Name” indicates the protein names obtained from the UniProt database. Numbers of Ser(P) sites in each protein are
shown under “No. of Ser(P)⬙; the column “UniProt” indicates those annotated in UniProt, and “Reannotated” indicates those estimated using
the PHOSIDA and IPI databases. In the column “Length,” the number of amino acid residues in each protein is shown, and the number of Ser
residues is in parentheses. Each value in the column “Hmean” is the mean information entropy calculated by using all the sequences surrounding
Ser(P) sites within a given protein. The occurrence percentage of the most common motif of each protein is represented in subcolumn “P” ofthe column “Percentage of the most common motif.” The subcolumn “N” denotes the mean and S.D. calculated from 1,000 trials of thenegative control. Selected annotations from UniProt are listed in the column “Comments.”
Name

No. of Ser(P)LengthReannotated

SRRM2
SRRM1
TCOF1
TP53BP1
IWS1
BCLAF1
MAP1B
ACIN1

142
60
59
49
44
39
33
33

45.78
31.96
8.94
11.45
14.00
5.62
50.00
8.17

2752 (642)904 (151)
1488 (203)1972 (246)819 (112)920 (151)
2468 (295)1341 (152)

MKI67
HIRIP3KIAA1802
PRPF4B

31
28
27
26

4.00
11.00
21.00
27.00

NUMA1
THRAP3
MDC1

26
25
25

TNKS1BP1
MAP4
ZC3H13INCENP
AHNAK

Percentage of themost common motif

Comments

P

N

1.27
1.72
1.44
0.99
1.81
1.14
1.26
1.33

36.62
48.33
27.12
26.53
38.64
30.77
24.24
54.55

21.52 ⫾ 3.15
22.31 ⫾ 4.24
22.47 ⫾ 4.61
22.94 ⫾ 4.87
22.72 ⫾ 5.01
23.15 ⫾ 5.27
23.22 ⫾ 5.39
23.22 ⫾ 5.39

3256 (280)556 (70)812 (113)
1007 (117)

1.47
1.67
1.62
1.49

41.94
25.00
14.81
50.00

23.40 ⫾ 5.57
23.70 ⫾ 6.00
24.17 ⫾ 6.08
24.20 ⫾ 6.34

1.75
28.67
2.50

2115 (162)955 (156)
2089 (201)

1.32
1.33
1.30

23.08
40.00
12.00

24.20 ⫾ 6.34
24.22 ⫾ 6.20
24.22 ⫾ 6.20

23
23
22
22
21

3.50
1.58
10.67
2.67
13.50

1729 (217)1152 (114)
1668 (195)923 (74)
5890 (323)

1.37
1.30
1.44
1.43
1.55

26.09
34.78
31.82
13.64
14.29

24.81 ⫾ 6.73
24.81 ⫾ 6.73
25.15 ⫾ 6.67
25.15 ⫾ 6.67
24.67 ⫾ 6.70

LEO1
SFRS2IP

21
21

10.50
6.33

666 (78)
1148 (164)

1.90
1.42

23.81
23.81

24.67 ⫾ 6.70
24.67 ⫾ 6.70

NUCKS121

4.80

243 (32)

1.58

38.10

24.67 ⫾ 6.70

probability differences between the positive and negative controls are hard to observe in the figures when a 10-AA sliding
window was used in either species, especially at the distance of
1 AA in mouse, all of the probabilities for the positive controls
were significantly higher than those for the negative controls(detailed data not shown). These results suggest that multiple
phosphosites within a phosphoprotein tend to localize within
the sequences located on the protein surfaces.
Sequence Consensus around Phosphosites with Each Protein—Next we analyzed the sequence consensus aroundphosphosites in human proteins. Using information theory, we
calculated the mean information entropy of Hmean for theamino acids between the positions of ⫺6 and ⫹6 relative toeach Ser(P) within the same phosphoprotein. Within the 20
phosphoproteins with the largest number of Ser(P) sites, mostof the Hmean scores were above 1, indicating that sequences
surrounding Ser(P) sites in the respective phosphoproteins
are strongly biased toward specific patterns (Table I). More-

Serine/arginine repetitive matrix protein 2
Serine/arginine repetitive matrix protein 1
Treacle protein; Treacher Collins syndrome protein
Tumor suppressor p53-binding protein 1
IWS1 homolog
Bcl-2-associated transcription factor 1
Microtubule-associated protein 1B
Apoptotic chromatin condensation inducer in the
nucleus
Antigen KI-67
HIRA-interacting protein 3
Zinc finger protein KIAA1802
Serine/threonine-protein kinase PRP4 homolog;
PRP4 kinase
Nuclear mitotic apparatus protein 1
Thyroid hormone receptor-associated protein 3
Mediator of DNA damage checkpoint protein 1;
nuclear factor with BRCT domains 1
182-kDa tankyrase 1-binding protein
Microtubule-associated protein 4; MAP 4
Zinc finger CCCH domain-containing protein 13
Inner centromere protein
Neuroblast differentiation-associated protein;
AHNAKRNA polymerase-associated protein LEO1
SFRS2-interacting protein; splicing factor,
arginine/serine-rich 2-interacting protein;
SC35-interacting protein 1
Nuclear ubiquitous casein and cyclin-dependent
kinases substrateover in sequence logo representations of the sequences surrounding each Ser(P) site within some example phosphoproteins, we also observed remarkable sequence biases aroundthe Ser(P) sites (Fig. 5). However, we could not rule out thepossibility that this result was caused by bias in the occurrence of amino acid residues within each protein rather thanby the relationship of these residues to phosphosites.
On the other hand, many phosphorylation sequence motifs
have been documented in previous studies, and substrate
proteins including phosphorylation motifs are thought to be
the targets of specific kinases (26). We therefore next examined whether multiple phosphosites in a single protein tended
to be targeted by some of the common kinases. We obtained the documented Ser(P)-based motifs in human proteins from the HPRD, and for each phosphoprotein that
included more than 10 Ser(P) sites we extracted the motifthat most commonly matched the Ser(P) sites and calculated the percentage of Ser(P) sites represented by the mostMolecular & Cellular Proteomics 8.5

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UniProt

Hmean

On the Evolutionary Increase in Phosphosites

common motif. For each phosphoprotein, we calculated theoccurrence percentage of the most common motif and themean and S.D. calculated from 1,000 repetitions of the negative control trials (Table I and Fig. 6). The occurrence percentages of the most common motifs were much higher thanthose of the negative controls, especially in proteins withlarger numbers of Ser(P) sites (Fig. 6). Although it is possiblethat the specific sequence pattern biases around Ser(P) sites
within the respective phosphoproteins are caused by patternbiases that exist naturally in the whole proteins, we suggestthat specific observed biases in the sequences surrounding
sets of Ser(P) sites might promote the targeting of phosphosites within a protein by common kinase proteins.
Model of Phosphoprotein Evolution—We further developed
a computational program simulating the evolution of thephosphoprotein population according to the preferential attachment growth rule (supplemental Fig. S6). The model generated power-law distributions of phosphoproteins when several parameters were adopted (supplemental Fig. S7). Thesefindings from our model might explain why phosphoproteins
containing many phosphosites are likely to be conserved
among multiple species (supplemental Fig. S8).
DISCUSSION

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Molecular & Cellular Proteomics 8.5

FIG. 6. Occurrence percentages of the most common motifs inphosphoproteins having many Ser(P) sites. The occurrence percentages of the most common motifs in each phosphoprotein areplotted as black-filled circles corresponding to the number of Ser(P)(pS) sites within the protein. Phosphoproteins with ⱖ10 Ser(P) sites
were used in this analysis. Negative controls are represented as graycrosses; for each plot, we used the mean of the occurrence percentages of the most common motifs calculated by 1,000 negative trials(see “Experimental Procedures”).

Downloaded from www.mcponline.org by Yasushi Ishihama on May 1, 2009

FIG. 5. Sequence logo representation of sequences surrounding the Ser(P) sites within example phosphoproteins. Sequence logo
representations of all the sequences surrounding the Ser(P) sites of the SRRM1, IWS1, MKI67, HIRIP3, KIAA1802, and LEO1 phosphoproteins
are shown in A–F, respectively. The surrounding sequences include the relative positions of ⫺6 to ⫹6 from the Ser(P) (pS) site. The height ofeach symbol represents the occurrence percentage of the corresponding amino acid in the relative position indicated on the horizontal axis.
The name of each protein is denoted in the left side of the black box on the upper side of each figure, and the mean information entropy (Hmean)calculated by using the sequences surrounding all of the Ser(P) sites in each protein is indicated on the right side with the number ofsurrounding sequences (equal to the number of Ser(P) sites in the protein) in parentheses.

On the Evolutionary Increase in Phosphosites

similar protein fragments through alternative splicing (47). This
problem has not been addressed in the UniProt data. To ruleout the effects of redundant annotations of phosphosites
possibly included in the UniProt data, we reannotated thephosphosites by using data from PHOSIDA and IPI and probabilistic logic, and we determined that the experimental andannotation procedures used for the phosphoprotein data did
not affect the bias of phosphosite number or the power-law
distribution of the phosphoproteins. However, the possibility
remained that the phosphosite numbers of individual proteins
were correlated with the abundance of the protein. We further
demonstrated that phosphosite numbers were not related toprotein abundance and that the power-law rule was not affected by the abundance of each protein (supplemental Fig.
S5). The evolution of the phosphoprotein population according to the preferential attachment growth rule was further
supported by our computational model (see supplemental
Figs. S6 and S7).
Protein phosphorylation is an important post-translational
modification that regulates a variety of biological processes,
such as cellular signaling pathways, subcellular protein localizations, and protein interactions and stabilization (1– 4).
Among the list in Table I of human phosphoproteins that
include many phosphosites, there are several that have notable functions. For example, TP53BP1 is a DNA damage
checkpoint protein that binds to the DNA-binding domain of
p53 and enhances p53-mediated transcriptional activation(48). TP53BP1 is known to be hyperphosphorylated in response to DNA damage (48). MDC1 is another DNA damage
checkpoint protein that contributes to the early cellular responses to DNA damage by inhibiting phosphorylation of p53
to protect cells from apoptotic cell death (49). Additionally thephosphorylation of INCENP, an inner centromere protein, by
Aurora B kinase regulates mitosis (50). Because the functional
behaviors of these proteins might be highly related to theirphosphorylation status, improving our understanding of theevolution of phosphosites will help us to understand the important functions of these and other phosphoproteins.
We then explored what factors can lead to the preferential
attachment of new phosphosites within the growing phosphoprotein population. In our two bioinformatics sequence analyses (of phosphosite localization and the consensus sequence around phosphosites), phosphosites within aphosphoprotein tended to be concentrated in limited regionsof the protein surface and to be targeted together by several
common kinases. We therefore suggest that groups of phosphosites concentrated in specific regions of protein surfaces
tend to be activated simultaneously by their respective kinases and act biologically as functional modules. Recently
simultaneous phosphorylation of closely concentrated Ser(P)sites was identified in the amino acid sequence of the Amphiphysin I protein from rat brain nerve terminals (51). The
simultaneous activation of localized phosphosites and theircooperativity may provide some benefits for the cellular sys-

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quantity is said to follow a power law, also known as Zipf’s law
or the Pareto distribution (30). Notably in the field of statisticalphysics, it has been shown that this distribution emerges by arich-get-richer process (31, 32) known as preferential attachment growth. A simple example of this process is the growing
network of Web page links, which follow a power-law distribution (32). In this rich-get-richer generative model, each new
Web page creates links to existing Web pages with a probability distribution that is not uniform or normal but is proportional to the current in degrees of Web pages (32). Power-law
distributions have also been observed in some biological phenomena, such as the distribution of the node degrees in a
protein-protein interaction network (41, 42) and the distribution of the gene expression levels of various species (43).
These findings suggest that genome evolution might be promoted by some extremely general and simple mechanisms
based on the preferential attachment growth principle (41,44 – 46). Here we hypothesized that the distribution of phosphosites and phosphoproteins has evolved through the richget-richer process of preferential attachment growth whereby
the phosphoprotein population regularly grows and each
phosphoprotein gains new phosphosites according to a probability that is proportional to its current phosphosite number.
We initially found that human and mouse phosphoproteins
identified in the UniProt database followed a power-law distribution with regard to their numbers of phosphosites:
whereas most of the phosphoproteins included only a few
phosphosites, there were some that contained many tens ofphosphosites. Because every category of phosphoprotein
data (all types of phosphosites, only Ser(P) sites, only Thr(P)sites, and only Tyr(P) sites) exhibited a power-law distribution
and the data were most abundant for Ser(P) sites and Ser(P)proteins in both the human and mouse (giving the most support for further statistical analyses), we focused on Ser(P)sites throughout this study to further investigate the powerlaw rule in phosphoproteins and their biological and evolutionary significance.
In the human phosphoprotein data in UniProt, we observed
phosphoproteins with up to 142 Ser(P) sites (Table I). Although the overall content of Ser residues within a few ofthese proteins, such as the SRRM2 protein, was also high(Table I), we demonstrated that the power-law rule did notarise from the amino acid contents or any sequence characteristics of the phosphoproteins. We also eliminated the possibility that the power-law distribution of phosphoproteins
was affected by redundant annotation in the UniProt database. In MS-based phosphoproteome analyses, proteins aredigested to peptides, and then proteome-wide mapping ofidentified phosphopeptides is conducted to search for theirsource phosphoproteins and to determine the positions ofphosphosites on the protein sequences. However, a fragment
ion spectrum of a unique phosphopeptide can sometimes bematched to, and potentially attributed to, multiple proteins.
Even a single gene may produce multiple proteins that containOn the Evolutionary Increase in Phosphosites

Acknowledgments—We are grateful to Dr. Naoyuki Sugiyama for
helpful discussions and Hiroyuki Nakamura for creating the graphicalsequence logo representations.
* This work was supported by research funds from Yamagata Prefecture and Tsuruoka City to Keio University and a grant from theJapan Society for the Promotion of Science (to N. Y.).
□S The on-line version of this article (available at http://www.
mcponline.org) contains supplemental material.
储 To whom correspondence should be addressed. Tel.: 81-466-475099; Fax: 81-466-47-5099; E-mail: rsaito@sfc.keio.ac.jp.

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Downloaded from www.mcponline.org by Yasushi Ishihama on May 1, 2009tem. In some cases, phosphorylation of multiple amino acidresidues enables protein interaction or stabilization that is notachieved by single phosphorylation. For example, multiple
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In the evolution of a living system, biological merit is directly
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Phosphosites are significantly more conserved than nonphosphorylated sites (21). Here we have suggested that multiple phosphosites concentrated on a protein surface form
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factor that has promoted the preferential attachment growthof phosphoproteins. Although the increasing amount of phosphoproteomics data is remarkable because of breakthroughs
in many experimental techniques, our knowledge of the functions and evolution of phosphoproteins has not kept pace. We
believe that our proposed hypothesis and findings will provide
useful tools for uncovering the role of phosphorylation in living
cellular systems.

On the Evolutionary Increase in Phosphosites

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