Integrative Features of the Yeast Phosphoproteome andProtein–Protein Interaction Map
Nozomu Yachie1,2,3, Rintaro Saito1,4*, Naoyuki Sugiyama1, Masaru Tomita1,2,4, Yasushi Ishihama1,5
1 Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan, 2 Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa,
Japan, 3 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America, 4 Systems Biology
Program, Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan, 5 PRESTO, Japan Science and Technology Agency, Tokyo, Japan

Abstract
Following recent advances in high-throughput mass spectrometry (MS)–based proteomics, the numbers of identified
phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms. Although a critical role ofphosphorylation is control of protein signaling, our understanding of the phosphoproteome remains limited. Here, wereport unexpected, large-scale connections revealed between the phosphoproteome and protein interactome byintegrative data-mining of yeast multi-omics data. First, new phosphoproteome data on yeast cells were obtained by MSbased proteomics and unified with publicly available yeast phosphoproteome data. This revealed that nearly 60% of ,6,000
yeast genes encode phosphoproteins. We mapped these unified phosphoproteome data on a yeast protein–protein
interaction (PPI) network with other yeast multi-omics datasets containing information about proteome abundance,
proteome disorders, literature-derived signaling reactomes, and in vitro substratomes of kinases. In the phospho-PPI,
phosphoproteins had more interacting partners than nonphosphoproteins, implying that a large fraction of intracellular
protein interaction patterns (including those of protein complex formation) is affected by reversible and alternative
phosphorylation reactions. Although highly abundant or unstructured proteins have a high chance of both interacting withother proteins and being phosphorylated within cells, the difference between the number counts of interacting partners ofphosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level.
Moreover, analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting
proteins by single kinases is common within cells. These multi-omics analyses illuminate how wide-ranging intracellularphosphorylation events and the diversity of physical protein interactions are largely affected by each other.
Citation: Yachie N, Saito R, Sugiyama N, Tomita M, Ishihama Y (2011) Integrative Features of the Yeast Phosphoproteome and Protein–Protein Interaction
Map. PLoS Comput Biol 7(1): e1001064. doi:10.1371/journal.pcbi.1001064
Editor: Yanay Ofran, Bar Ilan University, Israel
Received June 10, 2010; Accepted December 20, 2010; Published January 27, 2011
Copyright: ß 2011 Yachie et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by research funds from Yamagata Prefecture and Tsuruoka City to Keio University; by a generous grant from the Naito
Foundation to YI; and by a Grant-in-Aid for JSPS fellows from the Japan Society for the Promotion of Science (JSPS) to NY (1924). The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: rsaito@sfc.keio.ac.jp

features common to PPI networks (e.g., scale-free and small-worldproperties) are of prime importance in interpreting intracellular
protein behavior and the evolutionary aspects of PPIs [28–31]. PTM
changes the physical characteristics of proteins. It is therefore
probable that reversible PTM has large effects on the dynamic statesof intracellular protein-binding patterns and complex formation, andthat it controls not only signal transduction but also many othercellular pathways. However, the impact of PTM on the whole pictureof the PPI network has not yet been described.
Here, we describe the intracellular global relationships between
protein phosphorylation and physical PPI, as derived from theresults of integrative and systematic data-mining of Saccharomyces
cerevisiae multi-omics data (Fig. 1). New phosphoproteome data on
S. cerevisiae were initially obtained by MS–based analysis andunified with data on previously identified phosphoproteomes. We
superimposed the unified phosphoproteome data onto a S. cerevisiae
PPI network with other multi-omics data on S. cerevisiae. From theresults, we infer that the tremendous numbers of phosphorylations
within a cell have a large impact on PPI diversity, and that
intracellular phosphorylation patterns are affected partly bysimultaneous phosphorylation of physically bound proteins thatis triggered by the action of single kinases.

Introduction
Protein phosphorylation is a reversible, ubiquitous, and fundamentally post-translational modification (PTM) that regulates avariety of biological processes; one of its critical roles is the control ofprotein signaling [1–3]. Recent advances in mass-spectrometry(MS)–based technologies and phosphopeptide enrichment methods
have enabled the use of high-throughput in vivo phosphosite
mapping [4–7] to identify thousands of phosphoproteins. To date,around 10,000 phosphosites of serine, threonine, or tyrosine
residues have been identified in each of many organisms, including
human [8–12], mouse [13] and yeast [14–16]. Many public
databases, such as PHOSIDA [17], Phospho.ELM [18], and
UniProt [19], have been developed or expanded to catalog suchphosphoproteome data. Accordingly, the numbers of phosphoproteins that have been identified in various organisms now greatly
exceed the numbers known to have roles in protein signaling. This
has raised the question of whether this intracellular phosphorylation, which occurs on such a large scale, has other major roles.
In modern biology, the use of high-throughput screening methods
has enabled rapid progress in the disclosure of protein–protein
interaction (PPI) networks in many organisms [20–27]. Topological
PLoS Computational Biology | www.ploscompbiol.org

1

January 2011 | Volume 7 | Issue 1 | e1001064

Integrating Yeast Multi-Omics

unified these new phosphoproteome data with the publicly
available phosphoproteome datasets of Holt et al. [16] andUniProt [19] and obtained a total of 3,477 phosphoproteins
containing 25,997 phosphosites (Fig. 2; Supplementary Table S1).
The pS/pT/pY ratios of this study, the study of Holt et al., and
UniProt were 72%/23%/5%, 72%/23%/5%, and 80%/18%/
2%, respectively. Among the unified phosphoproteome data, 343
phosphoproteins and 2,778 phosphosites were not found in thedata of Holt et al. or UniProt. Comparison with S. cerevisiae
genomic information [32] revealed that 58.5% of the 5,815 known
and predicted genes were phosphoprotein-encoding genes (Supplementary Table S2). Although the use of current highthroughput technologies cannot disclose the entire phosphoproteome picture of a cell, these results imply that most intracellular
proteins can be phosphorylated under the appropriate environmental conditions.

Author Summary
To date, high-throughput proteome technologies haverevealed that hundreds to thousands of proteins in each ofmany organisms are phosphorylated under the appropriate environmental conditions. A critical role of phosphorylation is control of protein signaling. However, only afraction of the identified phosphoproteins participate incurrently known protein signaling pathways, and thebiological relevance of the remainder is unclear. This has
raised the question of whether phosphorylation has othermajor roles. In this study, we identified new phosphoproteins in budding yeast by mass spectrometry and unified
these new data with publicly available phosphoprotein
data. We then performed an integrative data-mining oflarge-scale yeast phosphoproteins and protein–protein
interactions (complex formation) by an exhaustive analysis
that incorporated yeast protein information from several
other sources. The phosphoproteome data integration
surprisingly showed that nearly 60% of yeast genes
encode phosphoproteins, and the subsequent datamining analysis derived two models interpreting themutual intracellular effects of large-scale protein phosphorylation and binding interaction. Biological interpretations of both large-scale intracellular phosphorylation andthe topology of protein interaction networks are highly
relevant to modern biology. This study sheds light on how
in vivo protein pathways are supported by a combinationof protein modification and molecular dynamics.

Generation of phospho-PPI network
The unified phosphoproteome data were superimposed onto the
PPI network to generate a ‘‘phospho-PPI’’ network. PPI data wereobtained via DIP (Database of Interacting Proteins) [33] andgrouped into four categories according to the experimental
method used for the PPI assay: all kinds of experimental methods(‘‘ALL’’), yeast two-hybrid (‘‘Y2H’’), co-immunoprecipitation(‘‘IMM’’), and tandem affinity purification (‘‘TAP’’). Among all
the protein nodes involved in every category of the phospho-PPI
network, the proportion of phosphoproteins was also nearly 60%(Supplementary Fig. S1). For example, the phospho-PPI networkof the ‘‘ALL’’ category was composed of 4,945 proteins, including
2,934 phosphoproteins (59.3%) and 17,215 physical interactions.

Results/Discussion
New yeast phosphoproteome data

Phosphoproteins have more PPI partners than dononphosphoproteinsOn the basis of liquid chromatography (LC)-MS analysis, we
initially identified 1,993 S. cerevisiae phosphoproteins containing
6,510 phosphosites. Information on the identified phosphopeptides
has been stored in PepBase (http://pepbase.iab.keio.ac.jp). We

To explore specific characteristics of the phospho-PPI network,
the number counts of interacting partners of phosphoproteins andnonphosphoproteins were analyzed (note that throughout thisstudy, the word ‘‘nonphosphoprotein’’ means a protein with nophosphosite identified to date). We found that, in general,phosphoproteins had more interacting partners than nonphosphoproteins. In each phospho-PPI network of the ‘‘ALL’’ and‘‘Y2H’’ categories with enough protein nodes for the subsequentstatistical analysis, the cumulative percentage distributions of nodedegrees (or the number count of interacting partners) ofphosphoproteins and nonphosphoproteins were markedly different(Fig. 3A and D). For example, in the dataset of ‘‘ALL’’, 47.6% of

Figure 1. Overview of integrative analysis of yeast multi-omics
data. New phosphoproteins were identified by LC-MS/MS analysis andunified with the publicly available phosphoproteome datasets of Holt etal. [16] and UniProt [19] (Step 1). A protein–protein interaction (PPI)
map was obtained from DIP (Database of Interacting Proteins) [33](Step 2). Y2H, yeast two-hybrid; IMM, co-immunoprecipitation; TAP,
tandem affinity purification. The ‘‘phospho-PPI’’ map was generated bysuperimposing the phosphoproteome data onto the PPI map (Step 3).
Negative controls for the phospho-PPI map were generated by ‘‘nodelabel shuffling (NLS)’’ and ‘‘random edge rewiring (RER)’’ (Step 4).
Comparative analyses of the real phospho-PPI and its negative controls
were performed with other yeast multi-omics data (Step 5).
doi:10.1371/journal.pcbi.1001064.g001

PLoS Computational Biology | www.ploscompbiol.org

Figure 2. Number counts of phosphoproteins (A) and theirphosphosites (B) newly identified in this study and of thoseobtained from the data of Holt et al. [16] and UniProt [19].
doi:10.1371/journal.pcbi.1001064.g002

2

January 2011 | Volume 7 | Issue 1 | e1001064

Integrating Yeast Multi-Omics

However, we demonstrated that in the ‘‘ALL’’ phospho-PPI
network there were still significant differences in the node degreelevels of phosphoproteins and nonphosphoproteins of similarabundance, and that the differences could be explained independently of protein copy number (Fig. 4B, C, E and F). Similar
results were derived from the phospho-PPI network generated only
from the ‘‘Y2H’’ category (Supplementary Fig. S2). We further
compared the abilities to predict phosphoproteins by using nodedegree and protein abundance levels above given thresholds. The
predictive power of node degree was markedly higher than that ofprotein abundance, except in the case of proteins that were
extremely abundant (Supplementary Fig. S3). If this higher
predictive ability were attributable to a spurious relationshipassociated with the actual intracellular proteome abundance, then
the node degree of a protein given by PPI assays would appear toprovide a better approximation of the intracellular protein copy
number than would single-cell proteomic analysis, which isunlikely.
Protein disorder is also a typical feature of ‘‘hub’’ proteins in PPI
networks [38–40]. Parts of unstructured proteins lack fixed
structure, and such disordered regions may have the ability tobind multiple proteins and to diversify PPI networks [38–40].
Additionally, at the proteome level, phosphorylation occurs at highrates in the disordered regions of proteins [16,17,41–44].
Therefore, it is highly likely that protein disorder affects the node

nonphosphoproteins had three or more interacting partners, but
this was true for 67.9% of phosphoproteins. Moreover, in bothdatasets, about twice as many phosphoproteins as nonphosphoproteins had 10 interacting partners (Fig. 3B and E). To
analyze the statistical significance of this difference in the context
of phosphorylation, we prepared randomly generated phosphoPPI networks by ‘‘node label shuffling’’ (NLS), in which the node
positions of phosphoproteins and nonphosphoproteins wererandomly moved within the phospho-PPI networks (for details,
see Materials and Methods). This demonstrated that the nodedegree of phosphoproteins was significantly higher than expectedfrom a random distribution (Fig. 3C and F).
Node degree in PPI networks has an exponential relationshipwith protein expression level [34–36], perhaps because cellular
proteins with more copies have a greater possibility of interactingwith others by chance [36]. Therefore, if the phosphoproteome
data are biased by protein abundance and highly abundant
proteins tend to be identified as phosphoproteins, there is a strong
possibility that the relationship between phosphorylation and nodedegree is spurious, with no direct causal connection. In fact,
proteome abundance data obtained through a single-cell proteomic analysis combining high-throughput flow cytometry and alibrary of GFP-tagged yeast strains [37] showed that the number ofphosphoproteins in the ‘‘ALL’’ phospho-PPI was skewed,
especially among highly abundant proteins (Fig. 4A and D).

Figure 3. Node degree distributions of phosphoproteins and nonphosphoproteins in the phospho-PPI data sets of ‘‘ALL’’ (A–C) and‘‘Y2H’’ (D–F). (A,D) Cumulative probability distribution of node degrees. For each group of phosphoproteins and nonphosphoproteins, circles
represent proportions of proteins with more than the k interacting partners indicated on the horizontal axis [P$(k)]. (B,E) P to N (P/N) ratio, where P
and N are P$(k) of phosphoproteins and nonphosphoproteins, respectively. (C,F) O to E (O/E) ratio, where O is P$(k) of phosphoprotein and E is that
expected from negative controls generated by node label shuffling (N = 10,000). For each node degree level, line graph and bar represent mean andtwo-sided 95% confidence intervals, respectively. Background colors and asterisks denote statistical significance over neutral O/E value of 1.0(*P,0.05, **P,0.01, ***P,0.001).
doi:10.1371/journal.pcbi.1001064.g003

PLoS Computational Biology | www.ploscompbiol.org

3

January 2011 | Volume 7 | Issue 1 | e1001064

Integrating Yeast Multi-Omics

Figure 4. Difference between node degree levels of phosphoproteins and nonphosphoproteins at each level of protein abundance(A–F) and protein disorder (G–I). (A–C) YEPD, protein abundance dataset for cells grown in rich medium; (D–F) SD, protein abundance dataset
for cells grown in synthetic complete medium. Protein abundance provided in the original dataset [37] was log-transformed (base 10) as abundancelevel a. The type of phospho-PPI network is ‘‘ALL’’ (for each analysis, protein nodes for which abundance or disorder levels and their correspondingedges were not provided were eliminated from the phospho-PPI network). Each bin corresponds to the protein abundance level between a(indicated on the horizontal axis) and a+0.5 (A–F) or the protein disorder probability between d (indicated on the horizontal axis) and d+0.2 (G–I). Forprotein nodes corresponding to each bin, the P to N (P/N) ratio of protein number count (where P and N are number counts of phosphoproteins andnonphosphoproteins) (A,D,G), average node degree levels [Log(k) (base 10)] of phosphoproteins (red line) and nonphosphoproteins (blue line)(B,E,H), and statistical significance [2Log(P value) (base 10)] of differences between Log(k) of phosphoproteins and nonphosphoproteins (C,F,I) arerepresented. Error bars denote s.e.m. Asterisks denote 2Log(P value).8.0 (i.e. P,1028).
doi:10.1371/journal.pcbi.1001064.g004

level view of Gene Ontology), there was a higher node degree levelfor phosphoproteins than for nonphosphoproteins (Supplementary
Fig. S5).
The average node degree of phosphoproteins is higher than thatof nonphosphoproteins [45], but it was unclear 1) whether thischaracteristic was observable only in hub proteins or whether it
existed broadly at the proteome level; and 2) whether this was a
spurious correlation that had emerged because of the presence ofsome third factor hidden in the complex and intertwining
proteomes. Our results show that, in many cases, this characteristic is present not only in hub proteins but also in proteins thathave few interacting partners. They also imply that these protein
interactions or binding patterns are not the result of influence by athird factor but are caused by phosphorylation-dependent cellular
activities.

degree difference between phosphoproteins and nonphosphoproteins. For every S. cerevisiae protein registered in UniProt, we
calculated the probability of harboring intrinsic disordered regions(see Materials and Methods). In the ‘‘ALL’’ phospho-PPI network,
the ratio of phosphoproteins to nonphosphoproteins increasedsmoothly with increasing disorder probability level (Fig. 4G).
However, in the same network, the node degree levels ofphosphoproteins and nonphosphoproteins of the same disorderprobability level were significantly different (Fig. 4H and I). Even
between phosphoproteins that had a low disorder probability of,0.1 and nonphosphoproteins that had an extremely highdisorder probability of .0.9, the node degree level of thephosphoproteins was significantly higher than that of the nonphosphoproteins (P = 0.0043). Similar results were observed in the‘‘Y2H’’ dataset (Supplementary Fig. S2). These results imply thatthe higher node degree of phosphoproteins than of nonphosphoproteins is at least partly independent of the PPI network
diversity produced by unstructured proteins.
Other factors that could influence the relationship between
protein phosphorylation and interaction are protein size andprotein groups with identical cellular function. Larger proteins
may have a greater chance of being phosphorylated and may
provide more binding domains for interactions with other proteins.
However, similar to the results for protein abundance anddisorder, statistical significance of the higher node degree ofphosphoproteins was observed independently of protein length(Supplementary Fig. S4). (Phosphorylation probability was highly
correlated with protein length; Supplementary Fig. S4.) In theevent that both protein phosphorylation and interaction eventsoccurring in a fraction of proteins confer a particular, identicalcellular function, then the global difference in node degree levels ofphosphoproteins and nonphosphoproteins would appear to becaused only by differences in function. However, we found that,for most functional annotations of S. cerevisiae in GO Slim (a higher
PLoS Computational Biology | www.ploscompbiol.org

Diversification of PPIs by phosphorylation
The additive effect of kinase–substrate and phosphatase–
substrate reactions is one possible model for interpreting thisphenomenon in the phospho-PPI network. If PPIs include manytransient signaling reactions between kinases, phosphatases, andtheir substrates (most of which are phosphorylated under certain
conditions), then the signaling proteins may have interactions
additional to the cohesive protein binding interactions in the PPI
data. Indeed, some enzyme–protein substrate interactions are
surprisingly stable and can be captured in protein interaction
assays [46]. However, of the 795 yeast phosphorylation anddephosphorylation reactions for which information has previously
been published [47], only 3.9%, 1.6%, 2.4%, and 0.8% overlapped with those in our ‘‘ALL,’’ ‘‘Y2H,’’ ‘‘IMM,’’ and ‘‘TAP’’PPI datasets, respectively (Supplementary Fig. S6). [Note,however, that these values were significantly higher than those
expected from negative controls of the corresponding PPI
networks generated by ‘‘random edge rewiring’’ (RER), and4

January 2011 | Volume 7 | Issue 1 | e1001064

Integrating Yeast Multi-Omics

similar, significant overlaps between physical PPI and signaling
network were obtained by another group [48]; for details of RER,
see Materials and Methods.] On the other hand, the node degreelevels of at least 600 proteins (.20% of phosphoproteomes in the‘‘ALL’’ phospho-PPI network) might have been related to, andaffected by, phosphorylation, as evidenced by the cumulative
percentage of phosphoproteins, which was more than 20% higher
than that of nonphosphoproteins (Fig. 3A). In addition to this, many
unidentified phosphoproteins are certain to be present in thenonphosphoprotein dataset. Therefore, it is difficult to interpret
such a large difference in the node degree of phosphoproteins andnonphosphoproteins only in terms of the additive effect of signaling
reactions, which had such a small overlap with the PPI data.
Furthermore, among the GO Slim ontology groups within the‘‘signal transduction’’ and ‘‘cell cycle’’ categories, which especially
include many signaling proteins, there were no great distinctions
between the node degree levels of phosphoproteins and nonphosphoproteins (although the node degree levels for ‘‘cytokinesis’’ and‘‘response to stress,’’ like those for most of the other ontology groups,
showed marked differences between phosphoproteins and nonphosphoproteins) (Supplementary Fig. S5).
In the phospho-PPI network, phosphoproteins had a greater
tendency than nonphosphoproteins to interact with proteins
harboring phosphoprotein binding domains (PPBDs). Out of 10
known PPBDs—14-3-3, BRCT, C2, FHA, MH2, PBD, PTB,
SH2, WD-40, and WW [49]—six (BRCT, C2, FHA, SH2, WD40, and WW) were present in the member proteins of the ‘‘ALL’’phospho-PPI network, and the average probabilities that phosphoproteins would interact with proteins that had all PPBDs or
each type of PPBD were higher than those for nonphosphoproteins (Fig. 5). (The gap between node degree levels of phosphoproteins and nonphosphoproteins was normalized; see Materials
and Methods.) Considering all of these results and perspectives, areasonable and generalized model that can be used to interpret thehigher node degree of phosphoproteins is that reversible andalternative phosphorylation reactions alter the physical characteristics of proteins under various environmental conditions; theinteracting or binding partners of phosphoproteins are thereby
more diversified than those of nonphosphorylated proteins.
Consistent with this interpretation, phosphoproteins harboring atleast two phosphosites had more interacting partners than thosewith a single phosphosite in the phospho-PPI network (Supplementary Fig. S7), even though phosphoproteins follow a powerlaw distribution with regard to phosphosite number counts andonly a small fraction of phosphoproteins have multiple phosphosites [50]. Protein phosphorylation reactions therefore seem tomake a large contribution to intracellular PPI diversity.

Figure 5. Probabilities that phosphoproteins and nonphosphoproteins will interact with proteins that have phosphoprotein binding domains (PPBDs). The average normalizedinteraction probabilities of phosphoproteins (red bars) and nonphosphoproteins (blue bars) in the ‘‘ALL’’ phospho-PPI network with eachtype of PPBD or with all PPBDs (indicated on the horizontal axis) are
shown.
doi:10.1371/journal.pcbi.1001064.g005

PPI data contain homodimer and heterodimer information thatcan be captured by experimental assays such as two-hybrid assays[52]. Therefore, to check the possibility that the tendency ofinteracting proteins to have similar phosphorylation patterns wascaused by protein interactions between structurally and sequentiallyhomologous proteins with similar phosphosites, we conducted thesame analysis as above but using ‘‘filtered’’ phospho-PPI networks, inwhich interactions between two homologous proteins were eliminated by E-value cut-offs of 1e–10 in the BLASTP program, but nomarked change was observed (Fig. 6; Supplementary Fig. S8).

Co-phosphorylation of interacting proteins by single
kinaseProteins involved in signal transduction pathways tend to bephosphorylated, and this is reflected in the PPI data, although theoverlaps between such signaling reactions and PPIs are limited (seeabove and Supplementary Fig. S6). Another possible interpretation
for the multitude of physical interactions between phosphoproteinsis that physically binding proteins that are members of the same
protein complex tend to be phosphorylated simultaneously by asingle enzyme. To search for the protein kinases potentially
responsible for the co-phosphorylation of proteins forming thesame complex, we analyzed a dataset of kinase–substrate relationships with PPI data of the ‘‘ALL’’ category. In the following analysis,
we used 85 and 65 kinases, respectively, from the experimental
results of an in vitro kinase–substrate assay [53] and a literaturederived collection of yeast signaling reactions [47], each having
multiple substrates (Supplementary Table S3). For each kinase, its
multiple substrates were superimposed on the PPI network and thenumber of ‘‘interacting kinate modules’’ (IKMs, triangle motifs
composed of a kinase and its two physically interacting substrates)(Fig. 7A) [53] was counted and compared with those estimated innegative controls of the PPI network produced by NLS and RER.
This analysis revealed that three kinases from the in vitro assay and
12 from the literature-based collection had significantly higher IKM
formability than those expected from both NLS and RER (P,0.05)(Fig. 7B and C; Supplementary Table S3). Similar results were

Both interacting proteins tend to be phosphorylated
We further analyzed the phosphorylation patterns of protein
pairs forming pair-wise interactions in the phospho-PPI network,
and we found that both interacting proteins in each pair tended tobe phosphorylated. For every category of phospho-PPI network,
three types of pair-wise interactions were counted, whereby‘‘Both,’’ ‘‘Either,’’ or ‘‘Neither’’ of two interacting proteins were
phosphorylated. The ‘‘Both’’ and ‘‘Neither’’ types of protein
interactions were significantly more common in the real phosphoPPI network than was expected from negative controls produced
by RER, whereas the ‘‘Either’’ types of protein interactions weresignificantly less common than expected (Fig. 6; Supplementary
Fig. S8). Notably, this outcome was independent of whether thenode degrees of the phosphoproteins were higher or lower thanthose of the nonphosphoproteins, because RER does not change
the node degree of each protein in a given network [51].
PLoS Computational Biology | www.ploscompbiol.org

5

January 2011 | Volume 7 | Issue 1 | e1001064

Integrating Yeast Multi-Omics

complex formation) is essentially influenced by the large number ofphosphorylation events; many reversible phosphorylations might
control condition-specific protein binding interactions related todifferent subcellular processes and molecular machines. On theother hand, protein phosphorylation patterns also seem to depend
largely on intracellular protein interaction diversity.
It is possible that many of the proteins defined as nonphosphoproteins in this study can actually be phosphorylated underappropriate cellular conditions. Even where this is true, however,the set we defined here as phosphoproteins should be enrichedwith proteins that are frequently phosphorylated under normal or
many different cellular conditions, because the frequently
phosphorylated proteins have a higher chance of being identified
as phosphoproteins than do the rarely phosphorylated proteins.
Accordingly, the features and models discussed in this study shouldreflect the overall characteristics of phosphoproteins and nonphosphoproteins among a number of different cellular conditions.
This is supported by the finding that proteins that had two or more
phosphosites physically interacted with more proteins than did
those with only a single phosphosite (Supplementary Fig. S7).
Although the quality of current yeast PPI data is also not perfectand the data may include false positives, the observed features withstatistical significance should be consequences of the actual
behaviors of intracellular proteins, because the effects of suchfalse positives on the statistical tests are supposedly random.
The integrative data-mining of yeast multi-omics data has now
shed light on the macroscopic and large-scale relationships
between phosphoproteomes and protein interactomes. Futurecomprehensive analyses of the in vivo link between protein
phosphorylation and physical interaction will yield more insights
into the complex and intertwined molecular systems of living cells.

Figure 6. Number counts of interacting protein pairs of each
phosphorylation pattern shown in the phospho-PPI network.
Respective rows of panels correspond to the three phosphorylation
patterns of two interacting proteins: ‘‘Both’’ (A,B) ‘‘Either’’ (C,D) and‘‘Neither’’ (E,F) respective columns correspond to PPI categories of‘‘ALL’’ (A,C,E) and ‘‘Y2H’’ (B,D,F). In each panel, data are shown for two
types of phospho-PPI networks: ‘‘whole’’ (i.e. unfiltered) and ‘‘filtered’’(see text). Colored bars (purple, blue, and pink) represent number
counts of protein interactions in real data sets; gray bars show meanvalues of those estimated by negative controls generated by random
edge rewiring (N = 10,000). Error bars represent s.d. Blue/red asterisks
denote significance of values higher/lower than those of negative
controls (***P,0.001).
doi:10.1371/journal.pcbi.1001064.g006

Materials and Methods
Phosphopeptide samples
Saccharomyces cerevisiae strain IFO 0233 cells grown continuously
on glucose medium [55] were used. Pelleted cells were vacuum
dried and frozen until further analysis. A Bioruptor UCW-310(Cosmo Bio, Tokyo Japan) was used to disrupt the pellets in 0.1 M
Tris-HCl (pH 8.0) containing 8 M urea, protein phosphataseinhibitor cocktails 1 and 2 (Sigma), and protease inhibitors(Sigma). The homogenate was centrifuged at 1,500g for 10 min
and the supernatant was reduced with dithiothreitol, alkylatedwith iodoacetamide, and digested with Lys-C; this was followed bydilution and trypsin digestion as described [56]. Digested samples
were desalted by using C-18 StageTips [57]. Phosphopeptide
enrichment by hydroxy acid–modified metal oxide chromatography (HAMMOC) was performed as reported previously [11,58].
Briefly, digested lysates (100 mg each) were loaded onto a selfpacked titania-C8 StageTip in the presence of lactic acid. After the
samples had been washed with 80% acetonitrile containing 0.1%
TFA, phosphopeptides were eluted by a modified approach using
5% ammonium hydroxide, 5% piperidine, and 5% pyrrolidine inseries [59].

obtained by using the ‘‘filtered’’ phospho-PPI network (Supplementary Fig. S9; Supplementary Table S3).
Accordingly, we suggest that, when a protein complex and kinase
are in close proximity within the intracellular environment, there isa high chance of simultaneous phosphorylation of member proteinsparticipating in the complex. This is consistent with the subcellularco-localization of signaling networks recently revealed through thesystematic prediction of signaling networks by using phosphoproteome data with an integrated protein network information derived
from curated pathway databases, co-occurring terms in abstracts,
physical protein interaction assays, mRNA expression profiles, andthe genomic context [48], and by data analysis of time-course
phosphoproteome data [54]. IKMs may enhance the subcellular colocalization of signaling reactions, and/or vice versa. The literaturederived signaling collection is presumably more enriched with wellinvestigated reactions and thus may more accurately reflect in vivo
signaling. This may explain why the collection harbored morekinases with high IKM formabilities (12 out of 65) than the in vitro
kinase–substrate relationship data (three out of 85).

LC-MS/MS analysis
An LTQ-Orbitrap XL (Thermo Fisher Scientific, Bremen,
Germany) coupled with a Dionex Ultimate 3000 (Germering,
Germany) and an HTC-PAL autosampler (CTC Analytics AG,
Zwingen, Switzerland) was used for nanoLC-MS/MS analyses.
An analytical column needle with a ‘‘stone-arch’’ frit [60] wasprepared with ReproSil C18 materials (3 mm, Dr. Maisch,Ammerbuch, Germany). The injection volume was 5 mL and theflow rate was 500 nL/min. The mobile phases consisted of (A)Bi-directional impacts of protein phosphorylation andbinding
It is plausible that, in living cells, the diversity of protein
interactomes (not only of protein signaling but also of protein
PLoS Computational Biology | www.ploscompbiol.org

6

January 2011 | Volume 7 | Issue 1 | e1001064

Integrating Yeast Multi-Omics

Figure 7. Kinases inferred to yield co-phosphorylation of proteins in the same protein complex. (A) Conceptual diagram of aninteracting kinate module (IKM) motif. (B,C) Kinases revealed to have significantly higher IKM formability than negative controls by data integrationof the PPI network with in vitro kinase–substrate relationships (B) and with a literature-based collection of signaling pathways (C). For each kinase,arrows denote number counts of IKMs formed by that kinase and the ‘‘whole’’ (i.e. unfiltered) PPI network, with P values estimated from negative
controls of the PPI network generated by RER and NLS (N = 10,000). Expected probability density distributions of number counts of IKMs observed innegative controls generated by node label shuffling and random edge rewiring are shown by gray and white bars, respectively.
doi:10.1371/journal.pcbi.1001064.g007

identified phosphoproteins and their phosphosites was obtained
from UniProt (release 15.14; http://www.uniprot.org/) [19]. All
UniProtKB/Swiss-Prot protein entries identified to have at least
one phosphosite in high-throughput phosphoproteomics studies
were downloaded via the Protein Knowledgebase (UniProtKB) in
XML format by querying the term scope: ‘‘PHOSPHORYLATION[LARGE SCALE ANALYSIS] AT’’. Some phosphoproteins registered in UniProt had multiple synonyms of UniProt accession.

0.5% acetic acid and (B) 0.5% acetic acid and 80% acetonitrile. A
three-step linear gradient of 5% to 10% B in 5 min, 10% to 40% B
in 60 min, 40% to 100% B in 5 min, and 100% B for 10 min was
employed throughout this study. The MS scan range was m/z 300
to 1500, and the top 10 precursor ions were selected in MS scans
by Orbitrap with R = 60,000 for subsequent MS/MS scans by ion
trap in the automated gain control (AGC) mode; AGC values of
5.00e+05 and 1.00e+04 were set for full MS and MS/MS,
respectively. The normalized collision energy was set at 35.0. A
lock mass function was used for the LTQ-Orbitrap to obtain
constant mass accuracy during gradient analysis.

Identifier standardization and data integration
For integrative analyses and comparisons of yeast multi-omics
data, all identities of proteins and genes obtained from different
data sources were standardized to UniProt accessions. If objects(e.g. gene names, ORF names, and/or locus names) in a data
source did not have UniProt accessions, the objects werestandardized to their corresponding UniProt accessions according
to the cross-reference list prepared from UniProtKB/Swiss-Protprotein entries obtained from UniProt (release 15.14). In cases
when an object corresponded to multiple synonyms of UniProt
accessions, all accessions were used to identify its corresponding
objects in other data sources.

Phosphosite identification
Both Mass Navigator v1.2 (Mitsui Knowledge Industry, Tokyo,
Japan) and Mascot Distiller v2.2.1.0 (Matrix Science, London,UK) were used to create peak lists based on the recorded
fragmentation spectra. Peptides and proteins were identified byautomated database searching using Mascot Server v2.2 (Matrix
Science) against UniProt/SwissProt v56.0 with a precursor masstolerance of 3 ppm, a fragment ion mass tolerance of 0.8 Da, andstrict trypsin specificity, allowing for up to two missed cleavages.
Carbamidomethylation of cysteine was set as a fixed modification,and oxidation of methionines and phosphorylation of serine,
threonine, and tyrosine were allowed as variable modifications.
Phosphopeptide identification and phosphorylated site determination were performed in accordance with a procedure reported
previously [11]. The false discovery rate was estimated to be
1.07% using a randomized database. All annotated MS/MS
spectra were stored in PepBase (http://pepbase.iab.keio.ac.jp).

Phosphoproteome data unification
The phosphoproteome data newly identified in this study andthe former phosphoproteome datasets obtained from Holt et al.
and UniProt were unified according to their UniProt accessions.
Positions of phosphosites and their amino acid residues in theunified phosphoproteome data were double-checked by using theproteome sequences obtained from UniProt (release 15.14).

Public phosphoproteome datasets

Gene annotations

Saccharomyces cerevisiae phosphoproteome data were obtained
from Dataset S1 of Holt et al. [16]. Another collection of formerly

From SGD (Saccharomyces Genome Database; http://yeastgenome.
org) [32], annotations of 5,815 known and predicted genes were

PLoS Computational Biology | www.ploscompbiol.org

7

January 2011 | Volume 7 | Issue 1 | e1001064

Integrating Yeast Multi-Omics

obtained. ORF names of genes were checked by using the unified
phosphoproteome data to determine whether the encoded protein
was identified as a phosphoprotein.

proteins [61]. For the analysis, we used the ‘‘disorder probability’’(i.e. the probability that a given protein is unstructured) output by
this program.

PPI network

Gene Ontology

The S. cerevisiae PPI network was obtained as XML files(Scere20081014) from DIP (Database of Interacting Proteins;
http://dip.doe-mbi.ucla.edu) [33]. We eliminated each interaction
entry including three or more ‘‘interactors’’ (e.g., in which multiple
prey proteins were detected for one bait protein in one
experimental assay) and used only those including two ‘‘interactors.’’ Every node in the PPI network was labeled by its
corresponding UniProt ID provided in the same XML file. For the
PPI assay, PPI data were further grouped into four categories: all
kinds of experimental methods (‘‘ALL’’), yeast two-hybrid(‘‘Y2H’’), co-immunoprecipitation (‘‘IMM’’), and tandem affinity
purification (‘‘TAP’’). A ‘‘filtered’’ PPI network was also preparedfor each category by eliminating interactions between two similarproteins by using the BLASTP program and an E-value cut-off of
1e–10.

Saccharomyces cerevisiae gene annotations belonging to ‘‘molecularfunction,’’ ‘‘biological process,’’ or ‘‘cellular component’’ of GO
Slim, a higher level view of S. cerevisiae Gene Ontology (GO), were
downloaded via the SGD ftp site.

PPBD
Information on S. cerevisiae proteins, each of which has at least
one of 10 known phosphoprotein binding domains (PPBDs),
namely 14-3-3, BRCT, C2, FHA, MH2, PBD, PTB, SH2, WD40, and WW [49], was obtained according to the protein domain
annotations of UniProt (release 15.14), which were provided byother protein databases.

Normalized probability of interaction with PPBDs
To evaluate the tendencies of phosphoproteins and nonphosphoproteins to interact with proteins that had PPBDs, thenormalized probabilities of such interactions were defined. For
each protein, the number of interacting protein partners that had
PPBDs was divided by the number of all interacting partners.

Phospho-PPI
Unified phosphoproteome data were mapped onto every
category of PPI data prepared from DIP according to their
UniProt accessions, and a phospho-PPI network was generated.
Throughout this study, proteins that did not correspond tophosphoproteome data were termed ‘‘nonphosphoproteins.’’

Collection of signaling reactions
To find possible IKMs, kinases previously reported tophosphorylate multiple substrates were obtained from data on invitro substrates recognized by most yeast protein kinases that were
measured with the use of proteome chip technology [Supplementary Data 2 of Ptacek et al. [53]], as well as from a literaturederived collection of documented yeast signaling reactions [Table
S3 of Fiedler et al. [47]]. All gene names of substrates in the in vitro
kinase–substrate relationship data and ORF names of substrates in
the literature-derived collection were standardized into UniProt
accessions and linked to proteins in the ‘‘whole’’ and ‘‘filtered’’ PPI
networks of the ‘‘ALL’’ category.

Negative control generation
To prepare negative controls for PPI and phospho-PPI
networks, two different processes (as diagrammed in Fig. 1) wereappropriately adopted on a case-by-case basis. ‘‘Node label
shuffling’’ (NLS) swaps the labels of two randomly selected nodes
in a given network; it repeats this operation a sufficient number oftimes until all pair-wise interactions in the queried network havedisappeared or until the number of iterations reaches 1,000 times
the number of interactions. ‘‘Random edge rewiring’’ (RER)
randomly selects two edges in a given network and randomly
rewires them. During this process, each rewiring operation isretried if a pair of nodes redundantly wired by two edges occurs in
the network; the iteration termination condition is the same as thatof NLS.

Statistics
The statistical significance of differences in a single real value
from a group of repeatedly generated random values wasestimated by calculating the proportion of random values equal
to the real value or more (or less, in certain instances). The
Wilcoxon–Mann-Whitney rank sum was used to assess statisticalsignificance between groups.

Proteome abundance data
Proteome abundance data for S. cerevisiae that were previously
acquired through a single-cell proteomics analysis combining highthroughput flow cytometry and a library of GFP-tagged strains[37] were used to analyze the characteristics of protein expression
in the phospho-PPI network. These data were composed ofproteome abundance data measured for cells grown in rich
(YEPD) and synthetic complete (SD) medium. For each cell
growth condition, protein names were standardized to UniProt
accessions, and protein abundance levels were log-transformed(base 10) and superimposed on each of the phospho-PPI networksof ‘‘ALL’’ and ‘‘Y2H.’’ In this case, protein nodes for which theabundance levels were not provided in the abundance data weredeleted from the phospho-PPI network.

Supporting Information
Figure S1 Contents of the phospho-PPI network generated foreach experimental method used in the PPI assay. Numbers inparentheses indicate those derived by the ‘‘filtered’’ network.
Found at: doi:10.1371/journal.pcbi.1001064.s001 (1.73 MB TIF)
Figure S2 Differences between node degree levels of phosphoproteins and nonphosphoproteins of the ‘‘Y2H’’ phospho-PPI
network at each level of protein abundance or protein disorder.
See legend to Fig. 4 for details.
Found at: doi:10.1371/journal.pcbi.1001064.s002 (0.67 MB TIF)

Comparison of abilities to predict phosphoproteins by
node degree level and protein abundance level. (A) ‘‘ALL’’ PPI
data and proteome abundance data measured for cells grown in
YEPD medium. (B) ‘‘ALL’’ PPI and proteome abundance for cells
grown in SD medium. (C) ‘‘Y2H’’ PPI and proteome abundancefor cells grown in YEPD medium. (D) ‘‘Y2H’’ PPI and proteome

Figure S3

Prediction of structured and unstructured proteins
The protein disorder level of every S. cerevisiae protein registeredin UniProt (release 15.14) was predicted by the POODLE-W
program, which uses the support vector machine–based learningof amino acid sequences of structurally confirmed disordered
PLoS Computational Biology | www.ploscompbiol.org

8

January 2011 | Volume 7 | Issue 1 | e1001064

Integrating Yeast Multi-Omics

abundance for cells grown in SD medium. For each predictor, thetrue-positive rate or ‘‘sensitivity’’ (defined here as the fraction ofphosphoproteins correctly predicted to be phosphoproteins) andthe false-positive rate or ‘‘1 – specificity’’ (defined here as thefraction of nonphosphoproteins incorrectly predicted to bephosphoproteins) are shown at a series of score thresholds.
Found at: doi:10.1371/journal.pcbi.1001064.s003 (5.75 MB TIF)

show proportions of proteins with more than the k interacting
partners indicated on the horizontal axis [P$(k)].
Found at: doi:10.1371/journal.pcbi.1001064.s007 (0.90 MB TIF)
Figure S8 Number counts of interacting protein pairs of each
phosphorylation pattern shown in the phospho-PPI networks ofthe ‘‘IMM’’ and ‘‘TAP’’ categories. See legend to Fig. 6 for details.
Found at: doi:10.1371/journal.pcbi.1001064.s008 (0.60 MB TIF)

Figure S4 Differences between node degree levels of phosphoproteins and nonphosphoproteins at each level of protein size. Seelegend to Fig. 4 for details. Analyses were performed for thephospho-PPI networks of ‘‘ALL’’ (A–C) and ‘‘Y2H’’ (E–F). Eachbin corresponds to the protein length between AA and AA+100(amino acids). See legend to Fig. 4 for details.
Found at: doi:10.1371/journal.pcbi.1001064.s004 (0.44 MB TIF)

Figure S9 Number counts of IKMs formed in the ‘‘filtered’’ PPI
network by each of the kinases shown in Fig. 7. For each kinase,arrows indicate number counts of IKMs formed by that kinase andthe ‘‘filtered’’ PPI network, with P values estimated by comparison
with negative controls. See legend to Fig. 7 for details.
Found at: doi:10.1371/journal.pcbi.1001064.s009 (0.90 MB TIF)
Table S1 Phosphoproteome data used in this study.
Found at: doi:10.1371/journal.pcbi.1001064.s010 (1.85 MB XLS)

Figure S5 Comparison of node degree counts of phosphoproteins and nonphosphoproteins in terms of yeast functionalannotations. The type of phospho-PPI network is ‘‘ALL.’’ Forproteins corresponding to each Gene Ontology annotation of‘‘biological process’’ (A), ‘‘molecular function’’ (B) and ‘‘cellularcomponent’’ (C) in the S. cerevisiae GO Slim set, the average nodedegree of phosphoproteins divided by that of nonphosphoproteinsis represented as the P to N (P/N) ratio.
Found at: doi:10.1371/journal.pcbi.1001064.s005 (2.10 MB TIF)

Table S2 List of known and predicted S. cerevisiae genes andphosphorylation annotation.
Found at: doi:10.1371/journal.pcbi.1001064.s011 (0.47 MB
XLS)
Table S3 Phosphoproteome data used in this study.
Found at: doi:10.1371/journal.pcbi.1001064.s012 (0.09 MB
XLS)

Figure S6 Number counts of intersections between pair-wiseprotein relationships in the literature-derived signaling collection
and in the PPI. For each category of PPI data, the number countof signaling reactions matched to protein interactions is represented by an arrow, along with P values estimated from negative
controls generated by 10,000 repeats by RER. Gray bars represent
the probability density distribution of the number count of theintersection observed using the negative controls generated by
RER.
Found at: doi:10.1371/journal.pcbi.1001064.s006 (0.24 MB TIF)

Acknowledgments
We thank our colleagues Yasuyuki Igarashi for experimental assistance inphosphoproteome identification, Kosaku Shinoda for maintaining the
PepBase, and Dr. Douglas B. Murray for providing S. cerevisiae strain IFO
0233. We also thank Dr. Kana Shimizu at the Computational Biology
Research Center (CBRC, Japan) for calculating yeast proteome disorders
by using POODLE-W, and Dr. Frederick P. Roth at the Harvard Medical
School for helpful discussions.

Figure S7 Cumulative probability distributions of node degrees

Author Contributionsof nonphosphoproteins, phosphoproteins with a single phosphosite, and phosphoproteins with two or more phosphosites in thephospho-PPI data of the ‘‘ALL’’ category. For each dataset, bars

Conceived and designed the experiments: NY RS MT YI. Performed theexperiments: NY NS. Analyzed the data: NY. Wrote the paper: NY RS.

References
13. Villen J, Beausoleil SA, Gerber SA, Gygi SP (2007) Large-scale phosphorylation
analysis of mouse liver. Proc Natl Acad Sci U S A 104: 1488–1493.
14. Chi A, Huttenhower C, Geer LY, Coon JJ, Syka JE, et al. (2007) Analysis ofphosphorylation sites on proteins from Saccharomyces cerevisiae by electron transfer
dissociation (ETD) mass spectrometry. Proc Natl Acad Sci U S A 104:
2193–2198.
15. Ficarro SB, McCleland ML, Stukenberg PT, Burke DJ, Ross MM, et al. (2002)
Phosphoproteome analysis by mass spectrometry and its application to
Saccharomyces cerevisiae. Nat Biotechnol 20: 301–305.
16. Holt LJ, Tuch BB, Villen J, Johnson AD, Gygi SP, et al. (2009) Global analysis of
Cdk1 substrate phosphorylation sites provides insights into evolution. Science
325: 1682–1686.
17. Gnad F, Ren S, Cox J, Olsen JV, Macek B, et al. (2007) PHOSIDA(phosphorylation site database): management, structural and evolutionary
investigation, and prediction of phosphosites. Genome Biol 8: R250.
18. Diella F, Gould CM, Chica C, Via A, Gibson TJ (2008) Phospho.ELM: a databaseof phosphorylation sites—update 2008. Nucleic Acids Res 36: D240–244.
19. UniProt Consortium (2010) The Universal Protein Resource (UniProt) in 2010.
Nucleic Acids Res 38: D142–148.
20. Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, et al. (2003) A protein
interaction map of Drosophila melanogaster. Science 302: 1727–1736.
21. Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, et al. (2004) Evidence fordynamically organized modularity in the yeast protein-protein interaction
network. Nature 430: 88–93.
22. Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, et al. (2002) Systematic
identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415: 180–183.
23. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, et al. (2001) A comprehensive
two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad
Sci U S A 98: 4569–4574.

1. Hunter T (2000) Signaling–2000 and beyond. Cell 100: 113–127.
2. Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S (2002) The
protein kinase complement of the human genome. Science 298: 1912–1934.
3. Pawson T, Nash P (2000) Protein-protein interactions define specificity in signaltransduction. Genes Dev 14: 1027–1047.
4. Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422:
198–207.
5. Cravatt BF, Simon GM, Yates JR, 3rd (2007) The biological impact of massspectrometry-based proteomics. Nature 450: 991–1000.
6. Huang PH, White FM (2008) Phosphoproteomics: unraveling the signaling web.
Mol Cell 31: 777–781.
7. Witze ES, Old WM, Resing KA, Ahn NG (2007) Mapping protein posttranslational modifications with mass spectrometry. Nat Methods 4: 798–806.
8. Beausoleil SA, Jedrychowski M, Schwartz D, Elias JE, Villen J, et al. (2004)
Large-scale characterization of HeLa cell nuclear phosphoproteins. Proc Natl
Acad Sci U S A 101: 12130–12135.
9. Molina H, Horn DM, Tang N, Mathivanan S, Pandey A (2007) Global
proteomic profiling of phosphopeptides using electron transfer dissociation
tandem mass spectrometry. Proc Natl Acad Sci U S A 104: 2199–2204.
10. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, et al. (2006) Global, in vivo,
and site-specific phosphorylation dynamics in signaling networks. Cell 127:
635–648.
11. Sugiyama N, Masuda T, Shinoda K, Nakamura A, Tomita M, et al. (2007)
Phosphopeptide enrichment by aliphatic hydroxy acid-modified metal oxide
chromatography for nano-LC-MS/MS in proteomics applications. Mol Cell
Proteomics 6: 1103–1109.
12. Olsen JV, Vermeulen M, Santamaria A, Kumar C, Miller ML, et al. (2010)
Quantitative phosphoproteomics reveals widespread full phosphorylation site
occupancy during mitosis. Sci Signal 3: ra3.

PLoS Computational Biology | www.ploscompbiol.org

9

January 2011 | Volume 7 | Issue 1 | e1001064

Integrating Yeast Multi-Omics

43. Tan CS, Bodenmiller B, Pasculescu A, Jovanovic M, Hengartner MO, et al.
(2009) Comparative analysis reveals conserved protein phosphorylation networks
implicated in multiple diseases. Sci Signal 2: ra39.
44. Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, et al. (2009)
Dynamic modularity in protein interaction networks predicts breast cancer
outcome. Nat Biotechnol 27: 199–204.
45. Tan CS, Jørgensen C, Linding R (2010) Roles of ‘‘junk phosphorylation’’ inmodulating biomolecular association of phosphorylated proteins? Cell Cycle 9:
1276–1280.
46. Walhout AJ, Vidal M (2001) Protein interaction maps for model organisms. Nat
Rev Mol Cell Biol 2: 55–62.
47. Fiedler D, Braberg H, Mehta M, Chechik G, Cagney G, et al. (2009) Functionalorganization of the S. cerevisiae phosphorylation network. Cell 136: 952–963.
48. Linding R, Jensen LJ, Ostheimer GJ, van Vugt MA, Jørgensen C, et al. (2007)
Systematic discovery of in vivo phosphorylation networks. Cell 129: 1415–1426.
49. Gong W, Zhou D, Ren Y, Wang Y, Zuo Z, et al. (2008) PepCyber:P,PEP: adatabase of human protein protein interactions mediated by phosphoproteinbinding domains. Nucleic Acids Res 36: D679–683.
50. Yachie N, Saito R, Sugahara J, Tomita M, Ishihama Y (2009) In silico analysisof phosphoproteome data suggests a rich-get-richer process of phosphosite
accumulation over evolution. Mol Cell Proteomics 8: 1061–1071.
51. Maslov S, Sneppen K (2002) Specificity and stability in topology of protein
networks. Science 296: 910–913.
52. Walhout AJ, Reboul J, Shtanko O, Bertin N, Vaglio P, et al. (2002) Integratinginteractome, phenome, and transcriptome mapping data for the C. elegans
germline. Curr Biol 12: 1952–1958.
53. Ptacek J, Devgan G, Michaud G, Zhu H, Zhu X, et al. (2005) Global analysis ofprotein phosphorylation in yeast. Nature 438: 679–684.
54. Imamura H, Yachie N, Saito R, Ishihama Y, Tomita M (2010) Towards thesystematic discovery of signal transduction networks using phosphorylation
dynamics data. BMC Bioinformatics 11: 232.
55. Murray DB, Beckmann M, Kitano H (2007) Regulation of yeast oscillatory
dynamics. Proc Natl Acad Sci U S A 104: 2241–2246.
56. Masuda T, Tomita M, Ishihama Y (2008) Phase transfer surfactant-aided trypsin
digestion for membrane proteome analysis. J Proteome Res 7: 731–740.
57. Rappsilber J, Ishihama Y, Mann M (2003) Stop and go extraction tips formatrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS
sample pretreatment in proteomics. Anal Chem 75: 663–670.
58. Rappsilber J, Mann M, Ishihama Y (2007) Protocol for micro-purification,enrichment, pre-fractionation and storage of peptides for proteomics using
StageTips. Nat Protoc 2: 1896–1906.
59. Kyono Y, Sugiyama N, Imami K, Tomita M, Ishihama Y (2008) Successive andselective release of phosphorylated peptides captured by hydroxy acid-modified
metal oxide chromatography. J Proteome Res 7: 4585–4593.
60. Ishihama Y, Rappsilber J, Andersen JS, Mann M (2002) Microcolumns with selfassembled particle frits for proteomics. J Chromatogr A 979: 233–239.
61. Shimizu K, Muraoka Y, Hirose S, Tomii K, Noguchi T (2007) Predicting mostly
disordered proteins by using structure-unknown protein data. BMC Bioinformatics 8: 78.

24. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, et al. (2000) A
comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.
Nature 403: 623–627.
25. Li S, Armstrong CM, Bertin N, Ge H, Milstein S, et al. (2004) A map of theinteractome network of the metazoan C. elegans. Science 303: 540–543.
26. Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, et al. (2005)
Towards a proteome-scale map of the human protein-protein interaction
network. Nature 437: 1173–1178.
27. Tarassov K, Messier V, Landry CR, Radinovic S, Serna Molina MM, et al.
(2008) An in vivo map of the yeast protein interactome. Science 320: 1465–1470.
28. Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science
286: 509–512.
29. Girvan M, Newman ME (2002) Community structure in social and biologicalnetworks. Proc Natl Acad Sci U S A 99: 7821–7826.
30. Goh KI, Oh E, Jeong H, Kahng B, Kim D (2002) Classification of scale-free
networks. Proc Natl Acad Sci U S A 99: 12583–12588.
31. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks.
Nature 393: 440–442.
32. Hong EL, Balakrishnan R, Dong Q, Christie KR, Park J, et al. (2008) Gene
Ontology annotations at SGD: new data sources and annotation methods.
Nucleic Acids Res 36: D577–581.
33. Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, et al. (2004) The
Database of Interacting Proteins: 2004 update. Nucleic Acids Res 32:
D449–451.
34. Ivanic J, Yu X, Wallqvist A, Reifman J (2009) Influence of protein abundance on
high-throughput protein-protein interaction detection. PLoS One 4: e5815.
35. von Mering C, Krause R, Snel B, Cornell M, Oliver SG, et al. (2002)
Comparative assessment of large-scale data sets of protein-protein interactions.
Nature 417: 399–403.
36. Yu H, Zhu X, Greenbaum D, Karro J, Gerstein M (2004) TopNet: a tool forcomparing biological sub-networks, correlating protein properties with topological statistics. Nucleic Acids Res 32: 328–337.
37. Newman JR, Ghaemmaghami S, Ihmels J, Breslow DK, Noble M, et al. (2006)
Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biologicalnoise. Nature 441: 840–846.
38. Dosztanyi Z, Chen J, Dunker AK, Simon I, Tompa P (2006) Disorder andsequence repeats in hub proteins and their implications for network evolution.
J Proteome Res 5: 2985–2995.
39. Dunker AK, Cortese MS, Romero P, Iakoucheva LM, Uversky VN (2005)
Flexible nets. The roles of intrinsic disorder in protein interaction networks.
FEBS J 272: 5129–5148.
40. Haynes C, Oldfield CJ, Ji F, Klitgord N, Cusick ME, et al. (2006) Intrinsic
disorder is a common feature of hub proteins from four eukaryotic interactomes.
PLoS Comput Biol 2: e100.
41. Gsponer J, Futschik ME, Teichmann SA, Babu MM (2008) Tight regulation of
unstructured proteins: from transcript synthesis to protein degradation. Science
322: 1365–1368.
42. Landry CR, Levy ED, Michnick SW (2009) Weak functional constraints on
phosphoproteomes. Trends Genet 25: 193–197.

PLoS Computational Biology | www.ploscompbiol.org

10

January 2011 | Volume 7 | Issue 1 | e1001064