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1. RESEARCH ARTICLE
Assignment of protein function and discovery of novel
nucleolar proteins based on automatic analysis of
MEDLINE
Martijn Schuemie1*, Christine Chichester2, 3, 4*, Frederique Lisacek5
, Yohann Coute6
,
Peter-Jan Roes1
, Jean Charles Sanchez6
, Jan Kors1
and Barend Mons1, 2, 3
1
Biosemantics Group, Medical Informatics Department, Erasmus Medical Center, Rotterdam, The Netherlands
2
Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
3
Knewco Inc., Rockville, MD, USA
4
Swiss-Prot Group, Swiss Institute of Bioinformatics, Centre Medical Universitaire, Geneva, Switzerland
5
Proteome Informatics Group, Swiss Institute of Bioinformatics, Centre Medical Universitaire,
Geneva, Switzerland
6
Biomedical Proteomics Research Group, Departement de Biologie Structurale et Bioinformatique,
Centre Medical Universitaire, Geneva, Switzerland
Attribution of the most probable functions to proteins identified by proteomics is a significant
challenge that requires extensive literature analysis. We have developed a system for automated
prediction of implicit and explicit biologically meaningful functions for a proteomics study of the
nucleolus. This approach uses a set of vocabularyterms to map and integrate the information from
the entire MEDLINE database. Based on a combination of cross-species sequence homology
searches and the corresponding literature, our approach facilitated the direct association between
sequence data and information from biological texts describing function. Comparison of our
automated functional assignment to manual annotation demonstrated our method to be highly
effective. To establish the sensitivity, we defined the functional subtleties within a family contain-
ing a highly conserved sequence. Clustering of the DEAD-box protein family of RNA helicases
confirmed that these proteins shared similar morphology although functional subfamilies were
accurately identified by our approach. We visualized the nucleolar proteome in terms of protein
functions using multi-dimensional scaling, showing functional associations between nucleolar
proteins that were not previously realized. Finally, by clustering the functional properties of the
established nucleolar proteins, we predicted novel nucleolar proteins. Subsequently, non-
proteomics studies confirmed the predictions of previously unidentified nucleolar proteins.
Received: September 14, 2006
Revised: December 18, 2006
Accepted: December 20, 2006
Keywords:
Exosome / Multi-dimensional scaling / Nucleolus / RNA helicase / Sequence alignment
Proteomics 2007, 7, 921–931 921
1 Introduction
There is a high demand for approaches and techniques that
can accelerate the annotation of genes, proteins, and other
entities in the biomedical sciences. The manual annotation
of proteins, such as performed by UniProtKB/Swiss-Prot, is
a demanding process that could benefit enormously from
computer assistance. Here we describe the evaluation of a
novel approach that uses a combination of techniques that
Correspondence: Dr Christine Chichester, Human and Clinical
Genetics, Leiden University Medical Center, gebouw 2, Eintho-
venweg 20, 2333ZC Leiden, The Netherlands
E-mail: chichester@knewco.com
Fax: 131-79-593-1601
Abbreviations: AC, UniProtKB/Swiss-Prot accession number;
AUC, area under the curve; GO, Gene Ontology; MDS, multi-
dimensional scaling; MeSH, Medical Subject Headings; PARN,
poly(A)-specific ribonuclease; PMID, PubMed ID; Rbm3, RNA-
binding protein 3; rpL12, ribosomal protein L12; SC35, splicing
factor, arginine/serine-rich 2 protein; SRP, signal recognition par-
ticle; TTF-1, transcription factor 1 * Both authors have contributed equally to this work.
DOI 10.1002/pmic.200600693
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2. 922 M. Schuemie et al. Proteomics 2007, 7, 921–931
process free text resources to reconstruct and refine the
manual annotation of 585 proteins identified in the human
nucleolus.
The nucleolus is a subnuclear compartment of eukar-
yotic cells that has classically been characterized as the site of
ribosomal RNA (rRNA) synthesis and ribosome biogenesis.
Recently, remarkable progress in understanding the func-
tional organization of the nucleolus has occurred, mainly
due to a direct application of proteomics research which has
enabled the identification of more than 705 proteins [1–4].
These studies and more recent findings [5] have suggested
that the nucleolus may also play a key role in growth and cell
cycle control, tumorigenesis, aging, and sequestration of
nonribosomal macromolecules and the consequent modula-
tion of their molecular pathways [6]. The recent high-
throughput studies have delivered massive amounts of data,
which in turn lead to time-consuming manual examination
of information contained in databases and biomedical litera-
ture to attribute functional characteristics to the proteins
discovered. Many of the protein entities have been previously
described in the literature, thus they need to be correlated
with the established knowledge and eventually categorized
into functional pathways. Many of the nucleolar proteins
identified via proteomics are still classified as “function
unknown.” Proteins that represent new entities present the
challenge of deciphering the biological function without lit-
erature validation specific for the individual human protein.
The first step in these processes is to employ techniques that
support educated inferences by the scientist.
An established method of attributing possible biological
functions to newly discovered proteins is through the use of
sequence alignments. Significant similarity between
sequences infers biological relatedness and often, although
not always, functional similarity. Current prediction of pro-
tein function is based on extrapolation of the information
accumulated for a relatively small set of proteins for which
direct functions have been determined experimentally. Ad-
ditional clues to individual protein function reside in the vast
ocean of the biomedical literature.
Existing methodologies mine functional information by
relying on document classification methods based on the
relationships between a limited set of GO terms and manually
assigned Medical Subject Headings (MeSH) [7], by associat-
ing literature with proteins and GO terms using a dictionary
[8], or sequence homology clustering with a GOA protein [9]
for the transfer of GO terms. The increasing number and
diversity of protein sequence families require new methods to
define and predict details regarding function. Our method
extracts relations between biological entities from the entire
MEDLINE literature database to produce a condensedversion
of it; an impossible task for a single scientist. In the present
work, we focus on human nucleolar proteins and their
sequence homologs. The condensed literature is represented
by a set of vocabulary terms taken from a thesaurus built from
the MeSH and several gene and protein databases. These
representations are called concept profiles. Concept profiles
calculated from papers related to human nucleolar proteins
and their sequence homologs are then compared to the con-
cept profiles calculated from papers associated with each GO
term. This comparison functions as a bridge from the molec-
ular function data to the sequence data and allows the associ-
ation of the correct term to each nucleolar protein. For new
sequences known to be homologous to an existing family, but
of unknown function, our method can exploit the information
stored in an unstructured form in literature and complement
the information in structured databases by predicting specific
functions at a high level of detail. We demonstrate the method
by application to a well-characterized protein family, the
DEAD-box RNA helicases.
Particularly important from the perspective of systems
biology, we show how our methodology can generate biolog-
ical leads concerning proteins associated with the exosome
surveillance mechanism. This is due to the fact that we are
able to capture the correlation between the concept profiles
representing the exosome proteins and the concept profiles
of the exosome functional categories to suggest this over-
looked biological system. Equally relevant to systems biology,
the concept profiles resulting from the condensed literature
are also used to perform a large-scale prediction of new
potential nucleolar proteins; we automatically derived and
aggregated the functional concepts for all known nucleolar
proteins and used these to predict novel nucleolar proteins.
Several of our predictions, although not found by proteomics
analysis of the nucleolus, have been subsequently confirmed
by manual evaluation of published experimental findings.
2 Materials and methods
We automatically assigned GO terms to each of the nucleolar
proteins that were identified by Coute et al. [16] for which
enough information was available. The process is depicted in
Fig. 1 and consists of the following steps.
2.1 Step 1: Creating literature sets per protein
We used the information from several sequence databases to
connect proteins to the PubMed IDs (PMIDs) of relevant arti-
cles about these proteins. For human proteins, we used the
articlesreferredinEntrezGene[10].Formouse,drosophila,and
yeast, we used the Mouse Genome Database [11], FlyBase [12],
and the SaccharomycesGenome Database [13], respectively. We
removed articles that were referred in more than 25 entries that
focused in general on sequencing projects. These papers do not
contain functional information for specific proteins.
To provide a potential function for previously undocu-
mented proteins we combined the information found for
similar sequences. This technique is based on the assump-
tion that proteins with high sequence homology will have
similar function and that using all the identified literature,
including cross-species information will provide more func-
tional insight than the unique sequence literature alone. We
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3. Proteomics 2007, 7, 921–931 Systems Biology 923
Figure 1. Overview of the process for assigning GO terms to
proteins. Step 1: A set of PMIDs is extracted from sequence
databases for each protein in the databases. Step 2: A set of
PMIDs is extracted from the GOA database for each GO term.
Step 3: For each set of PMIDs, a concept profile is generated. Step
4: The concept profiles of proteins are compared to the concept
profiles of GO terms using vector matching.
aligned the protein sequences from UniProtKB/Swiss-Prot
to similar sequences using blastp with the default parame-
ters [14]. We pooled the literature from human sequences
that matched the query sequence with an E-value of 10290
or
lower. For nonhuman sequences, we relaxed the criteria to
sequences with an E-value of 10220
or lower to allow for
cross-species differences in sequence that were not related to
the function of the protein.
2.2 Step 2: Finding articles related to GO terms
The GOA database lists proteins and their corresponding GO
terms, and when applicable, mentions the PMIDs of articles
used for assigning a specific GO term. We used these articles
to represent each GO term. We found that many GO terms
were seldom or not even assigned to gene products. We had
included only those GO terms for which more than five
PMIDs were found.
2.3 Step 3: Creating concept profiles
Concepts are defined via a thesaurus. A thesaurus contains
terms and synonyms that are used to identify textual refer-
ences to concepts. Our thesaurus was created by combining
biomedical concepts obtained from MeSH, gene product-
specific information obtained from GO, and gene/protein
names obtained from five different databases for four differ-
ent species [15]. There is some overlap between MeSH and
GO, resulting in some concepts being identified twice.
However, this has little consequence for the subsequent pro-
cessing steps.
The next step is to create concept profiles for each protein
and each GO term. A concept profile is a list of concepts with
weights. These weights indicate the association between a
concept and the protein or GO term to which the concept
profile belongs.
For each PMID related to a protein or a GO term, the title
and abstract were retrieved from the MEDLINE database.
Using the thesaurus and the Collexis software, concepts
appearing in the title or abstract were identified.
The concept profiles were extracted from the literature sets
belonging to each protein and each GO term. Concepts that
were found in at least one abstract of a literature set were added
to the concept profile. The weight of each concept was calcu-
lated as follows: for each concept the hypothesis was tested
whether there was a statistical dependency between (a) the
occurrence of that concept in an abstract and (b) whether the
abstract in which the concept occurred was in the literature set.
The weight of a concept in the profile was defined as the log-
likelihood of this test, where a high weight indicated a concept
that was most distinguishing for the abstracts in the set when
compared to all the other abstracts used in this experiment.
2.4 Step 4: Comparing concept profiles
The concept profiles of the proteins were compared to the
concept profiles of the GO terms using cosine vector match-
ing, resulting in a matching score. The highest matching GO
terms for each protein were considered to be the most rele-
vant GO terms for that protein.
2.5 Evaluation of functional assignment
To evaluate the performance of our system, we used the
manually defined categorization by Coute et al. [16], who
grouped the nucleolar proteins into nine main categories,
some of which were decomposed furtherinto subcategories. In
collaboration with the creators of that classification, a mapping
between categories and subcategories, and GO terms was per-
formed manually. This mapping is provided in Table 1. When
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4. 924 M. Schuemie et al. Proteomics 2007, 7, 921–931
Table 1. Mapping between the functional categories defined by Coute et al. [16] and terms from the Gene Ontol-
ogy (GO)
Category GO term GO identifier
Chaperones Protein folding GO:0006457
Chromatin structure Chromatin assembly or disassembly GO:0006333
Fibrous proteins Intermediate filament GO:0005882
mRNA metabolism
30
end cleavage and polyadenylation mRNA polyadenylylation GO:0006378
Editing mRNA editing GO:0006381
Export/trafficking Ribosome–nucleus export GO:0000054
Splicing Nuclear mRNA splicing, via spliceosome GO:0000398
Stability/degredation mRNA catabolism GO:0006402
Transcription Transcription, DNA-dependent GO:0006351
Others
Cytoskeleton organization/transport Cytoskeleton organization and biogenesis GO:0007010
DNA repair DNA repair GO:0006281
DNA replication DNA replication GO:0006260
Kinases/phosphatases Kinase activity GO:0016301
Mitosis/cytokinesis/cell cycle regulation Mitosis GO:0007067
Cytokinesis GO:0000910
Regulation of cell cycle GO:0000074
Nucleocytoplasmic transport Protein–nucleus export GO:0006611
Other enzymes Catalytic activity GO:0003824
Sumoylation Protein sumoylation GO:0016925
Ubiquitination/protein degradation Protein ubiquitination GO:0016567
Ribosomal proteins Structural constituent of ribosome GO:0003735
Ribosome biogenesis rRNA processing GO:0006364
Translation
Methionyl aminopeptidase activity Methionyl aminopeptidase activity GO:0004239
Regulation of translation Regulation of translation GO:0006445
SRP SRP binding GO:0005047
Translation elongation Translation elongation GO:0006414
Translation initiation Translation initiation GO:0006413
Translation termination Translation termination GO:0006415
tRNA methyltransferase activity tRNA processing GO:0008175
Column 1 shows the functional terms originally designated to the nucleolar proteins. The creators of the original
classification selected specific GO terms (Column 2) that were determined to match most closely to their original
categories. Column 3 contains the GO identifiers for the terms found in Column 2.
the GO terms assigned to a protein by our system matched
the GO terms mapped to the manual categorization, the
automatic assignment was assumed to be correct.
The performance of the automated assignment was scored
using receiver operator characteristics (ROC) curves; for each
category, all proteins were ranked according to the vector match-
ing score between the protein profile and the profile of the GO
term associated with that category. If more than one GO term
was associated with a category, as is the case for several higher-
level categories, the ranking was based on the sum of the vector
matching scores. Based on the rankings, the area under the
curve (AuC) was calculated as a measure of the similarity be-
tween the manual classification and the automated assignment.
2.6 Visualization
We visualized the functional relationships by using a
technique known as multi-dimensional scaling (MDS)
[17]. This technique optimizes the positions of objects, in
this case nucleolar proteins, in a 2-D space to make the
distances in this space as similar as possible to a given
distance measure. Our distance measure was the cosine
vector matching score between the protein concept pro-
files, used as an indication of the functional similarity
between proteins. Thus, in the resulting 2-D MDS display,
proteins that are functionally similar should appear close
together.
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5. Proteomics 2007, 7, 921–931 Systems Biology 925
3 Results
3.1 Creation of concept profiles
Using the multi-level approach as in Section 2, we created
concept profiles from the scientific literature for 585 of the
704 known nucleolar proteins. For the remaining 119 pro-
teins, no literature was available, either for the protein itself
or for one of its homologs in Entrez Gene. Sixty-two of these
proteins were labeled function unknown by Coute et al. [16].
For the remaining 57, there was apparent literature available
to enable manual annotation, although we restricted our
analysis to proteins for which Entrez Gene references were
available. The number of homologs and subsequent papers
used for the creation of the 585 protein concept profiles is
displayed in Table 2.
Table 2. Summary of the data used for generating protein con-
cept profiles for all proteins for which at least one article
was found
Minimum Maximum Mean
Homologs used
Human 0 9 1.66
Mouse 0 10 1.37
Fruitfly 0 5 0.70
Yeast 0 8 1.21
Articles used
Articles 1 1046 91.31
This table shows the minimum, maximum, and mean number of
concept profiles from homologous proteins and the number of
articles used to create the concept profiles. The number of hu-
man homologs includes the query protein itself, for which a
concept profile was not always available.
3.2 Assignment of functional categories
Each main category of functional classification showed a
significant relationship between the functional term attrib-
uted automatically and the function of the proteins within
the category (Table 3). For each nucleolar protein, the best
matching GO terms and their associated categories were
determined. A complete table is included in Supplemen-
tary Table S1, showing these categories and the vector
matching scores between the GO term profiles and protein
profiles.
The automatically assigned categories did not always
match the manually assigned categories. An investigation
showed three main reasons for the observed dis-
crepancies. First, each protein is assigned to only one
class by the manually defined categorization, but in reality
several classes could be correct. For instance, SFRS pro-
tein kinase 1 (AC Q8IY12) plays a central role in the
splicing regulatory network and therefore is manually
assigned to the “Splicing” subcategory of “mRNA meta-
Table 3. AuC measures correlating the main nucleolar functional
categories and subcategories as assigned by Coute et al.
[16] with those automatically derived by literature anal-
ysis
Category AuC p
Chaperones 1.00 ,0.001
Chromatin structure 0.98 ,0.001
Fibrous proteins 0.97 ,0.001
mRNA metabolism 0.72 ,0.001
30
end cleavage and polyadenylation 0.65 ,0.001
Editing 0.68 ,0.001
Export/trafficking 0.66 ,0.001
Splicing 0.74 ,0.001
Stability/degradation 0.55 0.060
Transcription 0.56 ,0.001
Others 0.81 ,0.001
Cytoskeleton organization/transport 0.69 ,0.001
DNA repair 0.72 ,0.001
DNA replication 0.70 ,0.001
Kinases/phosphatases 0.58 0.002
Mitosis/cytokinesis/cell cycle regulation 0.74 ,0.001
Nucleocytoplasmic transport 0.57 0.015
Other enzymes 0.65 ,0.001
Sumoylation 0.73 ,0.001
Ubiquitination/protein degradation 0.67 ,0.001
Ribosomal proteins 0.97 ,0.001
Ribosome biogenesis 0.69 ,0.001
Translation 0.88 ,0.001
Methionyl aminopeptidase activity 0.56 0.273
Regulation of translation 0.77 ,0.001
SRP 0.64 0.009
Translation elongation 0.80 ,0.001
Translation initiation 0.80 ,0.001
Translation termination 0.83 ,0.001
tRNA methyltransferase activity 0.55 0.331
This table gives the AuC summary measure for the ROC curves
used to describe the capacity of our method to correctly dis-
criminate between the possible functional categories for each
protein. For each functional category one or more GO terms were
selected that best represented that category. The membership of
a protein to a category was calculated by determining the dis-
tance between the profile belonging to the GO term and the pro-
tein profile. An AuC of 0.5 represents a random assignment of the
functional category and 1.0 represents a perfect assignment.
bolism.” Additionally, it is a kinase and thus the GO term
“Kinase activity” is assigned by the automated system as the
primary term.
Second, words occurring in the documents related to a
protein do not always indicate its function. For example,
Structural maintenance of chromosome 3 (AC Q9UQE7) is
involved in chromosome cohesion during cell cycle, and is
primarily assigned by the system to the functional category
“mitosis” instead of the category “chromatin structure,” be-
cause the word mitosis is often mentioned in the articles
related to this protein.
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6. 926 M. Schuemie et al. Proteomics 2007, 7, 921–931
Third, in some cases ambiguity in language introduces
errors. For instance, the ambiguous term “factor 1” is a
synonym of the protein, transcription factor 1 (TTF-1, AC
Q15361). This term is erroneously recognized in the name
of the protein cleavage and polyadenylation specificity fac-
tor, 160 kDa subunit (CPSF1, AC Q10570) which in the lit-
erature maybe referred to as the protein complex poly-
adenylation factor 1. The TTF-1 protein is assigned to many
papers related to the GO term “mRNA polyadenylylation,”
and incorporated in the concept profile of that GO term,
resulting in the faulty assignment of the GO term to TTF-1.
Efforts are underway to remove these errors from the sys-
tem following the method, as described by Schijvenaars et
al. [18].
3.3 Functional categories of DEAD-box motif proteins
As a case study to investigate whether our system has added
value over purely sequence based annotations, we con-
structed a multiple sequence alignment of the nucleolar
members of the DEAD-box proteins (Fig. 2) using the tool
ClustalW [19]. The aligned proteins were all related by the
DEAD-box element, nine conserved sequence motifs that
possessed RNA dependent ATPase and ATP-dependent RNA
helicase activities. Although the specific function of many
DEAD-box proteins is uncertain, generally they are found to
play important roles in; RNA metabolism by affecting spli-
cing, ribosome biogenesis, RNA turnover, and mRNA export.
The proteins in the alignment were divided into three clus-
ters. Assuming most of the functional aspects of the
sequences matching DEAD-box family are known, we at-
tributed to each cluster the functions, from a limited set of
manually chosen GO terms most likely possessed by those
proteins. Given the fact that these proteins are all related by
the DEAD-box motif [20], a simple transfer of sequence
annotations would assign the same functional attributes to
all DEAD-box proteins, as seen in protein sequence classifi-
cation databases [21]. At the highest level of functional
Figure 2. Multiple sequence alignment of nucleolar DEAD-box
proteins and the automatic assignment of functional attributes.
Each of the proteins in the alignment is represented by the gene
name followed by the UniProtKB/Swiss-Prot accession number
in parenthesis. The alignment shows three main clusters. The
primary assigned term, ATP-dependent RNA helicases activity,
which represents the generally accepted activity of the DEAD box
motif, is assigned to all clusters. Differences between the clusters
are seen at the level of the secondary terms.
assignment our system behaved in this manner as predicted.
Assignment of functional attributes showed that all of the
clusters possessed ATP-dependant RNA helicase activity as
the major functional category (Fig. 2). However, at the level of
the secondary term, the clusters appeared to have the diver-
gent functions: translational initiation, mRNA-nucleus
export, and ribosome biogenesis (Fig. 2). Manual investiga-
tion showed that within full text articles there was evidence
to demonstrate that the automated assignment of the specific
concept was correct for many of the proteins in each cluster.
This fulfills an important goal in high-throughput proteom-
ics. The automated incorporation of prior knowledge across
species supports the generation of functional predictions at a
level more refined than the simple transfer of annotations
based on sequence similarities alone. The propagation of
erroneous assignments based on repeated reference in the
literature to a singular alignment probably plays a minor role
in literature derived automated annotation. We show that the
system is not limited to the highest level designation, and
most of the protein profiles are based on a large number of
papers (96% of the profiles are based on more than 20
papers) thus marginalizing errors. We have no indication
that this bias plays more than an inconsequential role, which
it will also do in manual annotation. Moreover, the integra-
tion of automated literature derived information facilitates
prediction strategies based on the analysis of diverse sources
of information, which if viewed individually may provide
incomplete or inconsistent representation of a biological
phenomenon.
3.4 MDS display
MDS is a technique for representing high-dimensional ob-
jects in typically two dimensions. In the main MDS display
(Fig. 3A), the proteins of each of the nucleolar functional
categories are represented by a different color and shape. The
MDS display appears to capture the correlation between the
concepts representing the proteins and the underlying fea-
tures of the functional categories. The proteins in the func-
tional categories of: chaperones, chromatin structure,
fibrous proteins, cytoskeleton organization/transport, DNA
repair, DNA replication, kinases/phosphotases, mitosis/
cytokinesis/cell cycle regulation, nucleocytoplasmic trans-
port, ubiquitination–protein degradation, and ribosomal
proteins appeared to cluster essentially along the functional
categories lines. Some categories featured a few outliers
from their respective clusters. The proteins in the functional
categories of ribosome biogenesis, mRNA metabolism, and
translation appeared to cluster but had more dispersion than
other categories.
Outliers from the principal cluster are of special interest
when they appear to fall directly within a different functional
cluster. For example, the ribosomal protein, 40S ribosomal
protein S27a (AC P62979) is located centrally within a group
of ubiquitination and sumoylation proteins. To make the
position of this outlier in relation to these groups and the
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7. Proteomics 2007, 7, 921–931 Systems Biology 927
Figure 3. MDS display of
nucleolar proteins. The auto-
matically derived concept pro-
file for each protein is used to
place each point on the map.
MDS achieves a drastic reduc-
tion in dimensionality and
therefore may not visually pre-
serve the strong separation of
distinct clusters in the original
high dimensional space. The
manually described functional
categories for each protein are
represented by different colors
and shapes. (A) The map of the
complete set of the 700 nucleo-
lar proteins is shown with an
inset of the exosome cluster. In
the inset, the proteins belonging
to the exosome functional cate-
gory are circled. The three pro-
teins detailed in the text, exo-
some component 10, PARN, and
SRP9 are indicated. The three
function unknown proteins
described in the text are indi-
cated by their UniProtKB/Swiss-
Prot accession numbers
(O43390, P98179, Q8N220). (B)
The clusters of the nucleolar
proteins from the functional
categories of ubiquitin–protein
degradation and sumoylation
were evaluated in combination
with the ribosomal protein cate-
gory to investigate the place-
ment of the ribosomal S27a
protein (AC P62979).
other ribosomal proteins more apparent, we have displayed
only the relevant proteins (Fig. 3B). Note that the MDS tech-
nique attempts to optimize the positions of the proteins to
reflect the relationships between the proteins, and that dis-
playing a subset of the proteins will result in a slightly dif-
ferent figure as compared to the complete MDS (Fig. 3A).
This is because only the relationships of the proteins being
displayed are taken into account.
Analysis of the ubiquitination and sumoylation func-
tional clusters shows that like ubiquitination, sumoylation
(small ubiquitin-related modifier modification) modulates
protein function through post-translational covalent attach-
ment to lysine residues within targeted proteins. Over the
past few years, it has become apparent that the two mod-
ification systems often communicate and jointly affect the
properties of common substrate proteins, sometimes even
involving the same lysine residue. This justifies their close
association on the MDS diagram. The placement of the 40S
ribosomal protein S27a within these clusters was rationa-
lized by the fact that ubiquitin is synthesized in eukaryotes as
a linear fusion with either itself or to one of two carboxy-ter-
minal extension proteins (CEPs). The 40S ribosomal protein
S27a is the nucleolar protein that is generated during the
post-translational processing events which generate the
ribosomal forms of CEPs [22].
Many proteins appear to be multi-functional and there-
forein annotation, these can correctly be attributed to a variety
of different functional classes. In the original manual classi-
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8. 928 M. Schuemie et al. Proteomics 2007, 7, 921–931
fication each nucleolar protein was associated with only one
category and the selection of category depended greatly upon
the bias of the annotator. Coute et al. [16] have leaned toward
finding the link to ribosome biogenesis for the classification
of proteins since the nucleolus is classically known as the site
of ribosome biogenesis. They have also reduced the granular-
ity to facilitate the annotation by limiting the number of cate-
gories to the main functional roles of the proteins.
The MDS visualization can offer an interpretation of the
functional categorization potentially different from the bias
of the scientist and potentially suggests unrealized cate-
gories. Evaluation of the ribosome biogenesis proteins
(Fig. 3A, purple dots) showed a small group of exosome pro-
teins located away from the principle cluster. The exosome
had been implicated in both biogenesis and degradation of
many RNA species including rRNA. Inclusion of exosome
proteins in ribosomal biogenesis is thus warranted as is the
separation from the main cluster due to their role in RNA
degradation. In close proximity to the exosome proteins is
the 30
-exoribonuclease (manually assigned to the functional
category mRNA metabolism, stability and degradation)
which preferentially cleaves poly(A) tails, poly(A)-specific
ribonuclease (PARN, AC O95453). This is interesting in light
of the recent discovery in Saccharomyces cerevisiae that all of
the rRNAs can be polyadenylated at a certain level [23]
therefore PARN along with exosome component 10 (AC
Q01780) could have a role in removing these poly(A) tails.
Additionally, the exosome group contains a protein manually
classified in the translation category. The exosome might
play a role in the biogenesis of the small RNA component of
the cytoplasmic signal recognition particle (SRP), which is
assembled in the nucleolus of eukaryotic cells [24, 25]. Due to
this association, the SRP 9 kDa protein is also found within
the exosome cluster (Fig. 3A, inset). The clustering of these
proteins from varied functional categories, but with an
underlying functional cohesion, suggests another possible
category, exosome.
3.5 Elucidating function unknown proteins from
MDS clusters
Coute et al. [16] have classified a number of proteins as
function unknown because explicit experiments to deter-
mine their function have not yet been carried out. Several of
these function unknown proteins appeared to fall within the
cluster analyzed for exosome proteins (Fig. 3A). Literature
concerning these “unknown” proteins was evaluated to cor-
relate their position on the MDS with the other clustered
proteins.
The heterogeneous nuclear ribonucleoprotein (hnRNP)
R (AC O43390, Fig. 3A, inset) was found near the exosome
proteins. hnRNPs are among the most abundant proteins in
the eukaryotic cell nucleus and play a direct role in several
aspects of the RNA life including pre-mRNA splicing,
mRNA transport and mRNA translation, stability, and turn-
over. It is commonly accepted that these proteins bind nas-
cent RNA in a transcript-specific assembly and that both
protein–RNA and protein–protein interactions govern the
final outcome of the ribonucleoprotein fiber, which forms
the substrate of the ensuing processing events [26, 27]. The
cooperative interactions between the proteins that assemble
onto an intricate splicing-regulatory sequence and the
hnRNP assembly are altered in different cell types by incor-
porating different but highly related proteins [28]. The KH-
type splicing-regulatory protein (KSRP) and the hnRNP each
bind to distinct sequence elements of specific transcripts to
mediate RNA binding, mRNA decay, and interactions with
the exosome and PARN [29]. It has been suggested that
NSAP1, which shares 80% homology with hnRNP R, may be
a common factor recruited by different destabilizing ele-
ments for directing the deadenylation of transcripts. This
suggests a possible role for hnRNP R with exosome proteins.
Another protein classified as function unknown found in
the exosome proximity is the RNA-binding protein 3 (Rbm3,
AC P98179). This protein belongs to the hnRNP subgroup of
RNA-binding proteins [30] that contain a single RNA recog-
nition motif (RRM) and glycine-rich tail. Although the func-
tion of this family of proteins is not yet precisely known, it
has been suggested that they affect both transcription and
translation [31]. Additionally, it has been hypothesized that
microRNAs are part of a homeostatic mechanism that con-
trols global protein synthesis [32] and play a crucial role in
post-transcriptional gene silencing. Dresios et al. [33] found
that Rbm3 had a major effect on the relative abundance of
microRNA-containing complexes in cells and that the
expression of an Rbm3 fusion protein led to a decrease in
microRNA levels. Since the cellular levels of different RNA
molecules must be tightly controlled, the inclusion of Rbm3
in the cluster with the exosome proteins involved in main-
taining correct RNA levels is justifiable.
One additional protein of the function unknown category
protein found associated with the exosome cluster was the
Hypothetical protein FLJ36307 (AC Q8N220). Upon detailed
inspection of the sequence data, this protein appears to be a
new splice variant of the splicing factor, arginine/serine-rich
2 protein (SC35, AC Q01130) (Supplementary Fig. S1). SC35
belongs to the family of SR proteins that regulate alternative
splicing in a concentration-dependent manner in vitro and in
vivo. A striking feature of most of the SR protein genes is that
they express alternative mRNAs encoding truncated proteins
whose biological roles remain to be established [34]. It has
been reported that SC35 overexpression leads to a pro-
nounced decrease of its endogenous mRNA levels in HeLa
cells [35]. Moreover, SC35 accumulation is also correlated to
changes in the splicing pattern of its own mRNAs. Finally, it
appears that SC35 autoregulates its expression by promoting
alternative splicing events altering the stability of its own
mRNAs which possibly make it the target of the exosome
surveillance mechanism.
In summary, the correlation between the concepts
representing the proteins and the underlying features of the
functional categories are explicit enough to suggest coherent
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9. Proteomics 2007, 7, 921–931 Systems Biology 929
mechanistic descriptions for exosome proteins that were
manually classified in other functional categories or as func-
tion unknown. The system provides the possibility of evalu-
ation of proteins under circumstances with an absence of
personal bias so that associations previously hidden from the
researcher can be found.
3.6 Prediction of novel nucleolar proteins
The concept profiles of the 90 proteins determined to have a
nucleolar location as part of their functional spectrum by all
four proteomic studies described in Coute et al. [16], were
used to derive a typical “nucleolar profile.” The proteins
selected to collectively form this profile were localized in the
human nucleolus either by direct visualization methods,
such as antibody detection, protein tagging with green fluo-
rescent protein, or electron microscopy, or thought to be
involved in ribosome biogenesis. These profiles were com-
pared to the profiles of all the other human proteins in the
UniProtKB/Swiss-Prot database. The resulting matching
scores were aggregated, and the overall 14 best matching
proteins are shown in Table 4. In the groups of the highest
Table 4. Prediction of novel nucleolar proteins
AC Protein name Description
Q92901 60S ribosomal
protein L3-like
Ribosomal protein
Q9NQI0 DDX4 DEAD-box protein
O15523 DDX3Y DEAD-box protein
Q9UI26 Importin-11 Interacts with nucleolar
protein rpL12 [38]
Q9UHI6 DDX20 DEAD-box protein
P05388 60S acidic ribosomal
protein P0
Ribosomal protein
Q15477 Helicase SKI2W Shown to be nucleolar
[41]
P60866 40S ribosomal
protein S20
Ribosomal protein
P26196 DDX6 DEAD-box protein
O95793 STAU1 Shown to be nucleolar
[40]
Q9UHL0 DDX25 DEAD-box protein
Q14240 Eukaryotic initiation
factor 4A-II
DEAD-box protein
Q9UMR2 DDX19B DEAD-box protein
P23396 40S ribosomal
protein S3
Ribosomal protein
This table shows the top 14 predicted nucleolar proteins. Col-
umns 1 (AC) and 2 (Protein name) contain the accession number
and the protein name. Column 3 contains a brief description of
the protein or the citation containing evidence for nucleolar
localization.
scoring proteins, many represent members of the DEAD/
DEAH-box protein and the ribosomal protein families. These
families have a precedent for nucleolar localization [36, 37]
and thus are not unexpected as potential nucleolar proteins,
although explicit nucleolar localization has not yet been
demonstrated. Although two of the potential nucleolar
DEAD-box proteins, ATP-dependent RNA helicase DDX3Y
(AC O15523) and eukaryotic initiation factor 4A-II (AC
Q14240) share a high degree of sequence similarity with two
proteins, ATP-dependent RNA helicase DDX3X (AC
O00571) and eukaryotic initiation factor 4A-I (AC P60842),
respectively, both of which have previously been identified as
nucleolar proteins by proteomics [16]. The appearance of
three of the top 14 proteins not classified in the DEAD box or
ribosomal protein families was justified by a literature search
of nonproteomic eukaryotic studies.
Importin-11 (AC Q9UI26) serves as a transport receptor
for the ribosomal protein L12 (rpL12) [38], a nucleolar pro-
tein. This interaction with the nucleolar protein, rpL12,
rationalizes its inclusion. Double-stranded RNA-binding
protein staufen homolog 1 (AC O95793) also interacts with a
nucleolar molecule, telomerase RNA, and has been inde-
pendently visualized by immunofluorescence in nucleoli [39,
40]. Additionally, immunofluorescence and colocalization
with the nucleolar protein nucleophosmin, has been used to
detect helicase SKI2W (AC Q15477) in nucleoli [41].
4 Discussion
The unmanageable amount of information that is both wait-
ing to be incorporated into curated databases and the rate
with which new information is produced today emphasizes
the need for the system presented here. We demonstrate a
technique that allows a rapid and accurate assignment of
functions to individual proteins and clusters proteins/genes
at the functional level. Such approaches can drastically de-
crease the time needed for manual annotation, but also
reveal functional clusters and correlations between result
sets that are not easily discernable at the individual gene
level. The techniques and approaches we propose are widely
applicable for genomics microarrays and proteomics re-
search as well as investigation of individual proteins in spe-
cific pathways.
The manual annotation of many biological objects and
molecules for databases, such as UniProtKB/Swiss-Prot and
GO, is crucial for modern complex biological research.
However, manual annotation by professionals is increasingly
challenging because of the information overload. With
greater than 200 000 proteins presently in UniProtKB/Swiss-
Prot and only slightly more than 70 annotators, it becomes
quite a daunting task to keep up with the existing informa-
tion, let alone to face the millions of proteins still to be
annotated. Our system includes more literature than
humanly possible and presents a concise overview of the
most important concepts associated with a given protein. In
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10. 930 M. Schuemie et al. Proteomics 2007, 7, 921–931
regard to the 704 proteins of nucleolus proteome, the meth-
od has reproduced the annotation of a 6-month literature
search with high accuracy in only a couple of hours. Appar-
ently, this wide coverage of literature compensates for the
fact that our system only used abstracts from MEDLINE,
while Coute et al. [16] had the full text articles at their dis-
posal. Evidently, UniProtKB/Swiss-Prot annotation will
remain the necessary standard for many years to come, and
therefore our system can function as a computer-assistance
to speed up, widen the scope and increase the objectivity of
annotation.
Data emerging from any single species or experimental
approach provides only one perspective on protein function.
Combining the literature, due to similarities in sequence
alignment, increases the scope of evaluation even further, in
that it may suggest the function of a protein in one species
based on conserved sequences in multiple other species for
which more information is available in the specialized liter-
ature. Certainly, the simple transfer of sequence annotations
can wrongly assign functional attributes but our meta-analy-
sis method makes it possible to go beyond identification of
the strict sequence associated activities to determine the
subtle functional subtypes as seen with the different clusters
of the family of DEAD-box proteins.
The aggregation of information over multiple documents
has great potential for actual discovery of knowledge in
genomics and proteomics, which are fundamentally net-
work-based systems biology disciplines. Complex cell prop-
erties such as those that control the exosome arise from net-
works of molecular interactions. Control of these networks
involves cellular processes at multiple levels, including
mRNA and protein quantities, and their molecular mod-
ifications, localization, and interactions. The integration of
information of disparate literature, and subsequent auto-
matic analysis enabled the elucidation of information that
may not even be explicit in the literature. This is exemplified
by our functional prediction for proteins previously identi-
fied as function unknown by Coute et al. [16]. Information
from multiple levels was extracted from the literature and
integrated to suggest biological functions for these less well-
characterized proteins. Moreover, a new splice variant of
SC35 was discovered due to inferences from this system.
Systems biology research is characterized by the devel-
opment and application of technologies that allow the mon-
itoring of biology at the system level. In our study, based on
the aggregation of the nucleolar protein functional profiles,
other proteins within the nucleolar system were identified.
System biology predicts that the nucleolus protein composi-
tion may vary as the biological processes respond to specific
conditions. Ninety proteins are consistently found in the
nucleolus by proteomic studies indicating that they are loca-
ted in the nucleolus at least for part of their functional life
span. Although these proteins have a wide range of func-
tions, the aggregated conceptual information appeared to
produce correct predictions of other proteins that may be
related to this biological system. Additionally, we have pre-
liminary evidence from other groups using the same
approach that the profiles from a focused group of proteins
such as those centered on a property like pathogenicity are
also powerful tools to predict the additional proteins
involved.
Our method for the extraction of biological information
from literature associated with high-throughput data appears
to accurately represent biological phenomena due to the fact
that we:
(i) exploit sequence similarities at a level more refined
than the simple transfer of annotations,
(ii) correctly capture relationships between the functional
terms attributed automatically and the function of the pro-
tein, and
(iii) visualize relationships by clustering proteins using
their underlying functional cohesion.
This process is essentially a generic tool for network-
based systems biology, and we have no reason to believe that
the approach described here will be any less powerful with
different biological datasets. We are currently working on a
number of features that systematize the approach and make
the steps more user-friendly. We anticipate having an open
access prototype system online early 2007, to be used by the
scientific community at large, which will be made available
through our website: http://www.biosemantics.org.
This study was supported by the Biorange project sp 4.1.1.
from the Netherlands Bioinformatics Centre, by Leiden Uni-
versity Medical Centre, Erasmus University Medical Centre, and
Knewco Inc., and was conducted within the Centre for Medical
Systems Biology (CMSB), established by the Netherlands
Genomic Initiative/Netherlands Organisation for Scientific
Research (NGI/NWO).
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