Development of Human Protein Reference Database as an Initial Platform for Approaching Systems Biology in Humans
ABSTRACT Human Protein Reference Database (HPRD) is an object database that integrates a wealth of information relevant to the function of human proteins in health and disease. Data pertaining to thousands of protein-protein interactions, posttranslational modifications, enzyme/substrate relationships, disease associations, tissue expression, and subcellular localization were extracted from the literature for a nonredundant set of 2750 human proteins. Almost all the information was obtained manually by biologists who read and interpreted >300,000 published articles during the annotation process. This database, which has an intuitive query interface allowing easy access to all the features of proteins, was built by using open source technologies and will be freely available at http://www.hprd.org to the academic community. This unified bioinformatics platform will be useful in cataloging and mining the large number of proteomic interactions and alterations that will be discovered in the postgenomic era.
- SourceAvailable from: Maurizio Delvecchio[Show abstract] [Hide abstract]
ABSTRACT: Wolfram Syndrome type 2 (WFS2) is considered a phenotypic and genotypic variant of WFS, whose minimal criteria for diagnosis are diabetes mellitus and optic atrophy. The disease gene for WFS2 is CISD2. The clinical phenotype of WFS2 differs from WFS1 for the absence of diabetes insipidus and psychiatric disorders, and for the presence of bleeding upper intestinal ulcers and defective platelet aggregation. After the first report of consanguineous Jordanian patients, no further cases of WFS2 have been reported worldwide. We describe the first Caucasian patient affected by WFS2.BMC Medical Genetics 07/2014; 15(1):88. · 2.54 Impact Factor
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ABSTRACT: Computational models using metabolic reconstructions for in silico simulation of metabolic disorders such as type 2 diabetes mellitus (T2DM) can provide a better understanding of disease pathophysiology and avoid high experimentation costs. There is a limited amount of computational work, using metabolic reconstructions, performed in this field for the better understanding of T2DM. In this study, a new algorithm for generating tissue-specific metabolic models is presented, along with the resulting multi-confidence level (MCL) multi-tissue model. The effect of T2DM on liver, muscle, and fat in MKR mice was first studied by microarray analysis and subsequently the changes in gene expression of frank T2DM MKR mice versus healthy mice were applied to the multi-tissue model to test the effect. Using the first multi-tissue genome-scale model of all metabolic pathways in T2DM, we found out that branched-chain amino acids' degradation and fatty acids oxidation pathway is downregulated in T2DM MKR mice. Microarray data showed low expression of genes in MKR mice versus healthy mice in the degradation of branched-chain amino acids and fatty-acid oxidation pathways. In addition, the flux balance analysis using the MCL multi-tissue model showed that the degradation pathways of branched-chain amino acid and fatty acid oxidation were significantly downregulated in MKR mice versus healthy mice. Validation of the model was performed using data derived from the literature regarding T2DM. Microarray data was used in conjunction with the model to predict fluxes of various other metabolic pathways in the T2DM mouse model and alterations in a number of pathways were detected. The Type 2 Diabetes MCL multi-tissue model may explain the high level of branched-chain amino acids and free fatty acids in plasma of Type 2 Diabetic subjects from a metabolic fluxes perspective.PLoS ONE 01/2014; 9(7):e102319. · 3.53 Impact Factor
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ABSTRACT: Human uveitis is a type of T cell-mediated autoimmune disease that often shows relapse-remitting courses affecting multiple biological processes. As a cytoplasmic process, autophagy has been seen as an adaptive response to cell death and survival, yet the link between autophagy and T cell-mediated autoimmunity is not certain. In this study, based on the differentially expressed genes (GSE19652) between the recurrent versus monophasic T cell lines, whose adoptive transfer to susceptible animals may result in respective recurrent or monophasic uveitis, we proposed grouping annotations on a subcellular layered interactome framework to analyze the specific bioprocesses that are linked to the recurrence of T cell autoimmunity. That is, the subcellular layered interactome was established by the Cytoscape and Cerebral plugin based on differential expression, global interactome, and subcellular localization information. Then, the layered interactomes were grouping annotated by the ClueGO plugin based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. The analysis showed that significant bioprocesses with autophagy were orchestrated in the cytoplasmic layered interactome and that mTOR may have a regulatory role in it. Furthermore, by setting up recurrent and monophasic uveitis in Lewis rats, we confirmed by transmission electron microscopy that, in comparison to the monophasic disease, recurrent uveitis in vivo showed significantly increased autophagy activity and extended lymphocyte infiltration to the affected retina. In summary, our framework methodology is a useful tool to disclose specific bioprocesses and molecular targets that can be attributed to a certain disease. Our results indicated that targeted inhibition of autophagy pathways may perturb the recurrence of uveitis.PLoS ONE 01/2014; 9(8):e104404. · 3.53 Impact Factor
Development of Human Protein Reference Database
as an Initial Platform for Approaching Systems
Biology in Humans
Suraj Peri,1,4,16J. Daniel Navarro,1,5,16Ramars Amanchy,1Troels Z. Kristiansen,1,4
Chandra Kiran Jonnalagadda,1,6Vineeth Surendranath,6Vidya Niranjan,6
Babylakshmi Muthusamy,6T.K.B. Gandhi,6Mads Gronborg,1,4Nieves Ibarrola,1
Nandan Deshpande,6K. Shanker,6H.N. Shivashankar,6B.P. Rashmi,6M.A. Ramya,6
Zhixing Zhao,1K.N. Chandrika,6N. Padma,6H.C. Harsha,6A.J. Yatish,6M.P. Kavitha,6
Minal Menezes,6Dipanwita Roy Choudhury,6Shubha Suresh,6Neelanjana Ghosh,6
R. Saravana,6Sreenath Chandran,6Subhalakshmi Krishna,6Mary Joy,6
Sanjeev K. Anand,6V. Madavan,6Ansamma Joseph,6Guang W. Wong,7
William P. Schiemann,8Stefan N. Constantinescu,9Lily Huang,7Roya Khosravi-Far,10
Hanno Steen,11Muneesh Tewari,12Saghi Ghaffari,13Gerard C. Blobe,14Chi V. Dang,2
Joe G.N. Garcia,2Jonathan Pevsner,3Ole N. Jensen,4Peter Roepstorff,4
Krishna S. Deshpande,6Arul M. Chinnaiyan,15Ada Hamosh,1Aravinda Chakravarti,1
and Akhilesh Pandey1,17
1McKusick-Nathans Institute of Genetic Medicine,2Department of Medicine, and3Kennedy Krieger Research Institute, Johns
Hopkins University, Baltimore, Maryland 21287, USA;4Department of Biochemistry and Molecular Biology, University of Southern
Denmark, 5230 Odense M, Denmark;5Divisio ´n de Hepatologı ´a y Terapia ge ´nica, Unidad de Proteo ´mica, CIMA, Universidad de
Navarra, 31008 Pamplona, Spain;6Institute of Bioinformatics, International Technology Park Ltd., Bangalore 560 066, India;
7Whitehead Institute for Biomedical Research, Cambridge, Massachusetts 02142, USA;8National Jewish Medical and Research
Center, Denver, Colorado 80202, USA;9Ludwig Institute for Cancer Research, Brussels, Belgium;10Department of Pathology,
Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 02215, USA;11Department of Cell
Biology,12Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02215, USA;13Mount Sinai School of
Medicine, New York, New York 10029, USA;14Pharmacology and Cancer Biology program, Duke University, Durham, North
Carolina 27710, USA; and15Departments of Pathology and Urology, University of Michigan, Ann Arbor, Michigan 48109, USA
Human Protein Reference Database (HPRD) is an object database that integrates a wealth of information relevant to
the function of human proteins in health and disease. Data pertaining to thousands of protein–protein interactions,
posttranslational modifications, enzyme/substrate relationships, disease associations, tissue expression, and subcellular
localization were extracted from the literature for a nonredundant set of 2750 human proteins. Almost all the
information was obtained manually by biologists who read and interpreted >300,000 published articles during the
annotation process. This database, which has an intuitive query interface allowing easy access to all the features of
proteins, was built by using open source technologies and will be freely available at http://www.hprd.org to the
academic community. This unified bioinformatics platform will be useful in cataloging and mining the large number
of proteomic interactions and alterations that will be discovered in the postgenomic era.
The past several years have witnessed an exponential increase in
the amount of biological data, mainly due to development and
application of high-throughput technologies including auto-
mated sequencing, gene expression microarrays, and mass spec-
trometry to characterize DNA, mRNA, and proteins, respectively
(Brown and Botstein 1999; Pandey and Mann 2000; Hood and
Galas 2003). With the sequence of the human genome sequence
already available, a large majority of protein-coding genes has
been identified that pave the way for additional genome-wide
functional studies (Lander et al. 2001; Venter et al. 2001). Newer
technologies such as protein microarrays, live cell microarrays,
and RNAi hold the promise of systematically studying the entire
human proteome to help understand the ultimate molecular ma-
16These authors contributed equally to this work.
E-MAIL email@example.com; FAX (410) 502-7543.
Article and publication are at http://www.genome.org/cgi/doi/10.1101/
13:2363–2371 ©2003 by Cold Spring Harbor Laboratory Press ISSN 1088-9051/03 $5.00; www.genome.org
chine—a human cell (Elbashir et al. 2001; Zhu et al. 2001; Ziaud-
din and Sabatini 2001). Nevertheless, the classical approach of
performing individual biochemical and genetic experiments to
assess the function of proteins and genes that has been followed
for decades will continue to be just as valuable in the postge-
Systems biology is an emerging discipline that uses experi-
mental approaches and computational biology to help under-
stand biological phenomena on a global scale (Kitano 2002). For
example, cellular networks such as signaling pathways provide us
an opportunity to study the cell as a system. However, one of the
key issues to tackle before the power of systems biology can be
fully harnessed is to integrate genomic and proteomic data effi-
ciently along with other sources of information such as the pub-
lished literature (Birney et al. 2002). This integration will trans-
late raw data into useful information, thereby expanding our
knowledge about organisms. Recent advances in software tech-
nology permit the creation of suitable platforms for storage,
analysis, and representation of complex biological information
in such a manner that biologists can undertake a systems biology
approach without being overwhelmed by the data (Navarro et al.
2003). Despite this, biological databases are still far from being
such platforms because building databases that are comprehen-
sive and scalable and that have intuitive and robust query sys-
tems requires a truly coordinated effort between biologists and
computer professionals—something that is difficult to achieve.
Although there exists a large number of protein databases, it is
still difficult for biologists to sift through the available informa-
tion and to synthesize it for analyzing and interpreting their own
data. This is because of a number of factors, including redun-
dancy in databases, erroneous entries, annotation errors, failure
to account for alternative nomenclatures, lack of “interpreted
information,” inability to query all fields of the data, and the
paucity of experimental data incorporated into databases. In ad-
dition to this, blending of automated annotation with biological
sequence databases could result in loss of clarity of the underly-
ing biologically relevant information. For example, inclusion of
novel gene transcripts predicted through gene prediction pro-
grams in databases that house experimentally determined tran-
scripts could lead to confusion. Similarly, inclusion of predicted
posttranslational modifications based on computer algorithms or
sequence homology along with other experimentally derived
data about proteins could lead to erroneous interpretation, or
worse, unnecessary experimentation.
Therefore, we sought to create a protein-centric database
that would serve as a comprehensive resource of information
pertaining to human proteins, their characteristics, and func-
tions. Access to such information would be invaluable to biolo-
gists in several ways. For instance, the type of domains found in
proteins is generally predictive of their functional class or bio-
logical role. Posttranslational modifications such as phosphory-
lation and ubiquitination can drastically influence the activity of
proteins and are commonly used as regulatory mechanisms in
signal transduction pathways (Karin and Ben-Neriah 2000). Be-
cause proteins act in concert with other proteins, knowing the
identity of interacting proteins along with the relevant binding
sites can facilitate hypothesis-driven studies and elucidation of
regulatory networks (Pawson and Nash 2003). The exact subcel-
lular localization of proteins and their tissue distribution in the
body is also pivotal to protein function (Nakai 2000). Finally, it is
important to know whether any proteins are known to be asso-
ciated with human diseases, as this might implicate them in cer-
tain pathways (Hanash 2003).
The Reference Sequence Database (RefSeq) initiated by the
query system allows Boolean searches across the entire database. Pop-up lists that allow a user to choose the terms for querying are provided for most
The query page of HPRD. A screenshot of HPRD query system shows different fields by which a user can effectively search the database. The
Peri et al.
National Center for Biotechnology Information (NCBI) serves as
one of the central repositories for DNA and protein sequences
and some of their characteristics (Pruitt et al. 2000). SWISS-PROT
is another popular database that contains curated information
about proteins (Boeckmann et al. 2003). However, several fea-
tures of proteins that are crucial to understanding proteins and
their functions are either not or inadequately addressed by exist-
ing databases. First, a large body of data on protein–protein in-
teractions exists in humans, and yet, none of the databases
covers this adequately. Second, although thousands of sites of
posttranslational modifications have been determined experi-
mentally, the protein sequences found in databases are not an-
notated with this information. Third, most databases do not pro-
vide enzyme/substrate relationships that are crucial for under-
standing molecular networks within cells. Fourth, although
automatic prediction of domains from different programs is pro-
vided by several databases, it can be quite confusing to nonex-
perts to determine which predictions are correct and which ones
are to be ignored. Lastly, information such as tissue distribution,
subcellular localization, and disease association of proteins is
available by searching literature databases but is not generally
linked to database entries.
Our approach to solving this problem has been to create a
database that integrates protein information from several sources
and supports interoperability with other existing databases. Hu-
man Protein Reference Database (HPRD) is an object-oriented
database developed with open source technologies. It has a user-
friendly graphic interface that not only allows a biologist to
query proteins but also permits browsing based on several pa-
rameters. Currently, it contains a completely nonredundant set
of 2750 human proteins with ∼25,050 links to PubMed citations
that allow a user to obtain additional information about the an-
notations. We anticipate that HPRD, which is freely available to
the academic community, will become a unified bioinformatics
platform that will not only allow easy storage and retrieval of
protein data but also lead to systematic approaches to dissect the
human and other proteomes.
Almost all of the initial annotation was performed by scientists at
the Institute of Bioinformatics (http://www.ibioinformatics.org),
a nonprofit research organization, whose initial task was to create
HPRD. The person who performed the initial curation and an-
notation is listed under credits as the initial annotator for each
entry. A list of genes implicated in human diseases was obtained
from the Online Mendelian Inheritance in Man (OMIM) data-
domains as polygons and sites of posttranslational modifications as vertical straight or wavy lines (colored symbols at the end of lines represent different
posttranslational modifications). Moving the cursor over the graph shows detailed domain names, range of amino acids, type of modification, and site
of modification. The tabs guide the user to other annotated fields of the molecule. The summary tab shows a description of molecular weight, locus,
subcellular localization, domain architecture details, and functions. For every molecule, the left panel shows links to query, browse, BLAST, and pathways
pages. Most annotations are linked to corresponding PubMed articles that are accessible by clicking on the text.
A screenshot showing the molecule page of “Fyn” in HPRD. The molecule page shows a graphic representation of Fyn with its protein
Human Protein Reference Database
base (Hamosh et al. 2002). In addition to this list, proteins that
may have indirect involvement in human diseases were also con-
sidered for annotation. Other categories of genes were taken
based on important classes of proteins, including signaling mol-
ecules, transcription factors, enzymes, ion channels, ligands, and
receptors. Because of the redundancy of protein sequences, we
chose only the longest isoform for every entry, and the shorter or
other alternatively spliced variants were manually filtered out by
using BLASTP and BLASTN programs available at the NCBI Web
site (Altschul et al. 1997, 1990). We chose entries in the RefSeq
database (with NP_ or NM_ prefixes for protein and DNA se-
quences, respectively) wherever possible. These sequences were
used for further annotation. This was manually and individually
repeated for every OMIM entry.
The most important aspect of our annotation is that the litera-
ture extraction, analysis, and interpretation were performed by
trained biologists. The starting point in each case was the OMIM
database, and every entry was individually and manually anno-
tated. Information about name, accession numbers, sequence
features of mRNA, and protein sequence were obtained from Ref-
Seq annotation; disease information was obtained from OMIM;
and literature analysis from articles extracted from PubMed. In-
formation about protein domain architecture was obtained by
sequence analysis using SMART and Pfam programs as well as the
literature (Schultz et al. 1998; Bateman et al. 2002). The informa-
tion from SMART and Pfam was carefully interpreted by annota-
tors who, in most cases, chose the default thresholds applied by
these two programs. Protein domains obtained below the thresh-
old were carefully inspected before rejecting them. The motifs
were generally extracted directly from the literature, as many of
them are not included in the domain prediction programs.
The information about protein–protein interactions was
cataloged after a critical reading of the published literature. Ex-
haustive searches were done based on keywords and medical sub-
ject headings (MeSH) by using Entrez. The type of experiments
that served as the basis for establishing protein–protein interac-
tions was also annotated. Experiments such as coimmunopre-
cipitation were designated in vivo, GST fusion and similar “pull-
down” type of experiments were designated in vitro, and those
identified by yeast two-hybrid were annotated as yeast two-
Posttranslational modifications were annotated based on
the type of modification, site of modification, and the modified
residue. In addition, the upstream enzymes that are responsible
for modifications of these proteins were reported if described in
the articles. The most commonly known and the alternative sub-
cellular localization of the protein were based on the literature.
The sites of expression of protein and/or mRNA were annotated
based on published studies.
HPRD was created by using BioBuilder, an annotation tool that
we developed during the creation of this database (J.D. Navarro,
S. Peri, C.K. Jonnalagadda, B.M. Vrushabendra, S. Vineeth, N.
Talreja, A. Pandey, unpubl.). It is based on an extension of ZOPE
(Z object publishing environment), an open source application
server, which includes several advanced technologies such as
ZOPE page templates and a robust object database. Instead of
structured query language (SQL), the query engine for relational
database management systems, we used ZCatalogs to query the
ZOPE object database. ZCatalogs indexes the properties associ-
ated with every protein to provide rapid and efficient retrieval of
objects or subcomponents of objects from the database. The
schema of the database provides an overview of the objects used
in the construction of HPRD and can be accessed at the HPRD
BioBuilder has a content management system in which the
information contained in HPRD can be easily modified through
an HTML interface. The interface is designed such that the an-
notation process is greatly facilitated. BioBuilder was used for the
administration of review of annotation that was carried in dif-
ferent places of the globe. The information associated with every
protein object can be obtained in an extensible markup language
(XML) format. The utility of the XML format is that it is a struc-
tured data format and lends itself well to importing from and
exporting to different types of database systems. This format pro-
vides a unique architecture that can be efficiently used to parse
the data and export to other file formats, and provides for easy
data integration. In this respect, it is important to note that the
evolving standards for microarray and proteomic data also use
XML for their implementation.
RESULTS AND DISCUSSION
The diversity of protein-related data, in contrast to nucleotide
sequence data, presents unique issues that must be taken into
Type of modification
The number of sites annotated by various posttranslation modifica-
tions is shown.
protein interactions in HPRD based on reading of the literature.
Distribution of types of experiments used to derive protein–
Peri et al.
2366 Genome Research
account before designing an optimum database system. During
the development of HPRD, it was of utmost importance for us to
ensure that every category of data was searchable and that the
database was visual and user-friendly. We chose to develop a
database that is based on a bioinformatics analysis by expert
biologists as well as extensive curation of the published literature.
With the participation of the biomedical community, this data-
base will be extended to include all the known human proteins.
The database is publicly available and can be accessed at http://
www.hprd.org. Below, we will discuss some of the unique fea-
tures of HPRD.
Human Disease Genes in HPRD
In the current release of HPRD, we have annotated a total of 2750
protein sequences. This number includes proteins encoded by
1484 genes that represent all genes with allelic variants that are
annotated as linked to a human disease in OMIM database (Ha-
mosh et al. 2002). Our database therefore serves as a nice protein-
centric complement to the OMIM database that provides exten-
sive annotations about genes and their variations associated with
Asking Biological Questions
The Web page for the query has been designed to be as simple to
use as possible without losing precision. Figure 1 shows a screen-
shot of the query page, indicating that the proteins can be re-
trieved based on name, type of protein, domain structure, post-
translational modification, size, localization, function, or in-
volvement in disease. In addition to the query page, a user can
find the proteins of interest in three other ways: by browsing
through the categories of molecules, by using BLAST to search for
homologous proteins, or by visualizing protein networks. The
browse mode allows the user to navigate through categories of
proteins that belong to a certain class (e.g., G protein–coupled
receptors; tyrosine phosphatases), that contain certain domains
and motifs (e.g., Sushi domains; RGD motif), that undergo post-
translational modifications (e.g., palmitoylation), or that are lo-
calized to certain subcellular compartments (e.g., lysosomes). Us-
ing the BLAST feature not only provides the user with an e-value
but also displays the domain structure of the retrieved protein(s),
which facilitates an intuitive visualization of the protein. The
protein pathways provide a graphic view of protein–protein in-
teraction data contained in HPRD and will be discussed in greater
A knowledge of protein domain architecture is essential to assess
the function and class of a protein. In the current release, we
have annotated 417 protein domains predicted by SMART, Pfam,
and the literature. Motifs are generally shorter elements of a pro-
tein that are important in protein–protein interactions and func-
tion (e.g., coiled-coil; nuclear localization signal). We have an-
notated 18 types of protein motifs. As more proteins are anno-
tated, these numbers are expected to increase. A screenshot of the
molecule page shows the domain architecture along with post-
translational modifications of a cytoplasmic tyrosine kinase, Fyn
A large majority of proteins undergo modifications during or
after translation. These co- or posttranslational modifications are
crucial for protein stability, sorting, and function. Many modifi-
cations are directly related to diseases, and as such, information
about these modifications is an important resource to investigate
the function of proteins. However, there is a paucity of databases
that provide exhaustive information about protein modifications
and their representation in the context of protein domains. We
have extracted 23 different types of protein modifications (Table
1) from the literature and annotated the modified residue and
the enzymes responsible for the modifications. Phosphorylation
is one of the most intensively studied protein modifications and
occurs on serine, threonine, and tyrosine residues in vertebrates
(Hunter 1998). It plays a vital role in cell growth, differentiation,
and signal transduction and has important implications in the
development of many diseases, including cancers. We have
manually extracted a total of >1100 experimentally determined
phosphorylation events. Another widespread posttranslational
modification is glycosylation (Hart 2003). Glycoproteins have
important functions in cell processes, such as protein sorting,
immune recognition, receptor binding, and pathogenicity. We
have annotated >190 glycosylation sites in the proteins anno-
tated thus far. All posttranslational modifications are depicted
visually on the graph of each molecule. In addition to these two
modifications, several other types of modifications listed in Table
1 can be used to query or browse the database.
The Human Interactome
Proteins do not generally function as isolated entities but are
often a part of larger protein complexes within a cell. A crucial
aspect of any proteomic analysis lies in the elucidation of inter-
acting proteins—the interactome—and in mapping the corre-
sponding binding sites (Walhout and Vidal 2001). By under-
standing the proteins and their binding partners in the context
of a network, a clearer insight into the functioning of a cell can
Total number of annotated proteins
Number of protein–protein interactions
Number of posttranslational modification events
Number of types of domains and motifs
Number of substrates
The total number of entries in the various fields in HPRD are shown.
tein in HPRD. The histogram shows the distribution of proteins with zero
to one, two, three, four, or five or more interaction partners out of a total
of 10,534 interactions.
A distribution of the number of interaction partners per pro-
Human Protein Reference Database
The EGF receptor signaling pathway generated by using the data contained in HPRD. The graph was drawn by using Pajek program and further manipulated manually (Batagelj and Mrvar
1998). The major signaling molecules involved in the EGF receptor pathway are labeled in red boxes. The interactors of the major proteins are shown as small circles. The graphs are generated in
Scalable Vector Graphics (SVG), which allows the user to zoom in without any loss of resolution. Clicking any node links to the corresponding molecule page in HPRD.
Peri et al.
2368 Genome Research
be obtained. We have annotated >10,000 direct unique protein–
protein interactions in HPRD that were derived from individual
small-scale experiments published in the literature. Protein–
protein interactions are sometimes difficult to evaluate because
there is no established gold standard for determining protein–
protein interactions, with each experimental method having its
own limitations. False-positive results (biologically nonsignifi-
cant interactions) and false-negative results (missed biological
interactions) can occur for a number of reasons. False positives
can occur due to nonspecific binding, whereas false negatives can
occur because of transient interactions, low abundance of pro-
tein expression, or inefficient extraction of certain types of pro-
teins (e.g., hydrophobic proteins). Some of the important high-
throughput methods such as yeast two-hybrid (Ito et al. 2000;
Uetz et al. 2000) and coimmunoprecipitation and mass spec-
trometry (Gavin et al. 2002; Ho et al. 2002) have been used to
systematically identify protein–protein interactions in yeast. Sur-
prisingly, very little overlap was observed between the different
methods implying that such interaction data from high-
throughput studies must be interpreted with caution (Bader and
Hogue 2002; Schachter 2002; von Mering et al. 2002). In this
regard, it is notable that only 2% of the interactions contained in
HPRD are derived solely from the yeast two-hybrid system, and
more than two thirds of the interactions have been derived from
experiments performed in vivo (Fig. 3). In light of the depth and
the quality of experimental data, we expect that our database will
make it easier to establish a benchmark database for human pro-
As an illustration, Grb2, is an adapter protein that is in-
volved in diverse pathways ranging from cytoskeletal organiza-
tion to proliferation to cell–cell communication. Our annotation
revealed >200 interaction partners for this protein, which reflects
a central role for this molecule as a linker in signal transduction
pathways. One important aspect of our annotation of Grb2 was
that although it is widely known to localize to the cytoplasm, a
careful literature search revealed an alternate nuclear localization
as well (Romero et al. 1998). Figure 4 shows the distribution of
the number of interacting proteins per entry in HPRD, with the
average number of interacting proteins being ∼3.7 per protein.
Notably, 44% of proteins have three or more interacting pro-
teins, and it is likely that with continued experiments to eluci-
date the function of these proteins, this number will increase
significantly. Although it is difficult to estimate the total number
of interactions in the human proteome, we believe that the mini-
mum number of protein–protein interactions will be >200,000 if
a gene count of ∼30,000 is assumed.
A large number of cellular processes in the cell are amplified by
cascades of enzyme/substrate interactions. Typically, enzymes
act on substrates to catalyze reactions ranging from phosphory-
lation or ubiquitination to proteolytic cleavage. In a given path-
way, it is important to be aware of the enzymes and their sub-
strates in addition to the other more stable protein–protein in-
teractions that do not depend on any catalytic activity. However,
no database currently offers a list of substrates and the enzymes
that act upon them. We have annotated >1600 enzymes and
substrates in our database. The site of modification on the sub-
strate and the corresponding upstream enzyme is annotated for
posttranslational modifications in most cases.
One major advantage of HPRD is that it allows us to construct
protein interaction networks for different signaling pathways
based on data contained in the database. Figure 5 shows the
representation of the Epidermal Growth Factor (EGF) receptor
pathway, in which the red boxes show the major proteins, and
the circles in the network represent other interaction partners.
The graph helps not only to visualize the protein interaction
networks but also to potentially identify the function for a novel
molecule by placing it in the context of a larger signaling net-
work. Such networks reveal the complexity of patterns for a given
class of molecules, pathway, or cellular process made possible by
the availability of a large number of interacting proteins ex-
tracted from the literature. Thus far, we have generated nine
signaling pathway networks and are in the process of expanding
BRCA1 as a Representative Entry in HPRD
We will provide BRCA1 as an example to illustrate the breadth
and depth of annotation in HPRD and to highlight the impor-
tance of manual annotation of the entries. BRCA1 is a transcrip-
tion factor that is mutated in certain forms of cancers and has
been extensively studies over the past several years (Venkitara-
man 2002). By a careful analysis of the literature, we were able to
catalog 62 proteins that interact with BRCA1. The interacting
domains/regions are indicated in most cases and can be easily
visualized by clicking the “visualize interactions” button. For in-
stance, the N terminus of BRCA1 is responsible for homodimer-
ization with BRCA1 and for heterodimerization with BARD1, and
its C-terminal region or BRCT domains mediate binding to
BRCA2, RB, p53, CtIP, and RNA helicase A, among others. Eighty
percent of the interactions were based on biochemical evidence
that was derived from either in vivo or both in vitro and in vivo
type of experiments. The remaining 20% of the interactions were
based on in vitro interactions alone (including yeast two-hybrid),
and none of that interaction data for BRCA1 was based on yeast
two-hybrid data alone.
BRCA1 has been clearly demonstrated to be localized to the
nucleus by several different groups. However, alternative local-
ization to the cytoplasm and the centrosome can be easily found
by browsing the BRCA1 entry in HPRD along with direct links to
the primary literature. BRCA1 is annotated as phosphorylated on
nine serine and one threonine residues by four different up-
stream kinases. Although it is widely known even to the nonspe-
cialist that BRCA1 is implicated in breast and ovarian cancers, the
entry in HPRD shows that it is also mutated in two other cancers,
including prostate cancer. Similarly, although BRCA1 is well
known to be expressed in breast and ovarian tissues, the HPRD
entry indicates that it is also expressed in the thymus, testis, and
lymphocytes in addition to several other tissues. In this regard, it
is interesting to note that when BRCA1 was originally cloned as
a candidate susceptibility gene for breast and ovarian cancers, it
was clearly noted that it is more abundantly expressed in the
thymus and testis than in breast and ovarian tissues (Miki et al.
1994). Finally, BRCA1 can be visualized as a direct interaction
partner of a major signaling molecule in five out of nine receptor-
mediated signaling pathways that are linked to HPRD, including
IL-2, erythropoietin, and Fas receptor signaling pathways. This
intimate involvement of BRCA1 in these hematopoietic path-
ways coupled to information about its expression in lymphocytes
and thymus is very easily appreciated by using HPRD, which
might otherwise be difficult to assimilate from a plethora of pub-
lications and numerous databases. It is hoped that inferences
made from such connections would lead to generation of new
hypotheses and experimentation to test them.
Our database complements other protein–protein interaction da-
tabases that are mostly focused on the yeast proteome, including
Human Protein Reference Database
BIND (Bader et al.2001) and DIP (Xenarios et al. 2000), or on
signaling proteins found in certain cell types such as that by the
Alliance for Cell Signaling (Gilman et al.2002). We have anno-
tated 2750 proteins in total, including 10,534 unique protein–
protein interactions. We have provided >25,000 PubMed links to
various fields that direct a user to the relevant primary literature.
Table 2 shows the statistics of the current release of HPRD: These
numbers will grow with continued annotation efforts. It took
∼50,000 person hours over 8 months to develop the software and
to read >300,000 research articles for the initial manual annota-
tion of 2750 proteins currently residing in HPRD. Indeed, we
anticipate completing annotation of ∼10,000 proteins by the end
of 2003. However, a truly error-free and comprehensive database
is impossible without the involvement of the biomedical com-
munity—no one can do a better job of annotations than those
working on the proteins themselves. In this regard, we have pro-
vided a comment button for every molecule that will facilitate
feedback from users. These user comments will allow us to cor-
rect any errors and to update published data concerning anno-
tated proteins in addition to our own ongoing efforts to enrich
and update the data. The information contained in HPRD is open
to the scientific community—the source code of BioBuilder is
freely available under the Lesser General Public License condi-
We plan to integrate publicly available microarray data into
HPRD in the near future. This will facilitate a gene-centric view to
determine whether the mRNA expression pattern of a given gene
is reported to be altered by any published study. Microarray users
can already take advantage of our detailed annotation to classify
proteins in several ways to generate novel hypotheses or to nar-
row down the likely candidates involved in a biological process.
This database will be a valuable resource for the proteomic com-
munity as well because a majority of posttranslational modifica-
tions have a fixed molecular mass that will allow more precise
searches of the protein database. The information about site of
expression, subcellular localization, and association with diseases
will be invaluable in experiments involving subproteomes. The
wealth of information of HPRD will play a crucial role in adopt-
ing integrative approaches to interpret high-throughput experi-
mental data such as those derived from microarrays and pro-
teomic experiments that are critically dependent upon existence
of good databases. We believe that this database will strengthen
efforts at careful manual analysis and interpretation of the role of
genes and proteins in complex systems and will become a knowl-
edge base for the human proteome in the near future.
Dr. Pandey serves as Chief Scientific Advisor to the Institute of
Bioinformatics. The terms of this arrangement are being man-
aged by the Johns Hopkins University in accordance with its
conflict of interest policies.
The publication costs of this article were defrayed in part by
payment of page charges. This article must therefore be hereby
marked “advertisement” in accordance with 18 USC section 1734
solely to indicate this fact.
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. 1990.
Basic local alignment search tool. J. Mol. Biol. 215: 403–410.
Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller,
W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new
generation of protein database search programs. Nucleic Acids Res.
Bader, G.D. and Hogue, C.W. 2002. Analyzing yeast protein–protein
interaction data obtained from different sources. Nat. Biotechnol.
Bader, G.D., Donaldson, I., Wolting, C., Ouellette, B.F., Pawson, T., and
Hogue, C.W. 2001. BIND: The Biomolecular Interaction Network
Database. Nucleic Acids Res. 29: 242–245.
Batagelj, V. and Mrvar, A. 1998. Pajek: Program for large network
analysis. Connections 21: 47–57.
Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R.,
Griffiths-Jones, S., Howe, K.L., Marshall, M., and Sonnhammer, E.L.
2002. The Pfam protein families database. Nucleic Acids Res.
Birney, E., Clamp, M., and Hubbard, T. 2002. Databases and tools for
browsing genomes. Annu. Rev. Genomics Hum. Genet. 3: 293–310.
Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.C., Estreicher, A.,
Gasteiger, E., Martin, M.J., Michoud, K., O’Donovan, C., and Phan,
I. 2003. The SWISS-PROT protein knowledge base and its
supplement TrEMBL in 2003. Nucleic Acids Res. 31: 365–370.
Brown, P.O. and Botstein, D. 1999. Exploring the new world of the
genome with DNA microarrays. Nat. Genet. 21: 33–37.
Elbashir, S.M., Harborth, J., Lendeckel, W., Yalcin, A., Weber, K.,
and Tuschl, T. 2001. Duplexes of 21-nucleotide RNAs mediate
RNA interference in cultured mammalian cells. Nature
Gavin, A.C., Bosche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A.,
Schultz, J., Rick, J.M., Michon, A.M., and Cruciat, C.M. 2002.
Functional organization of the yeast proteome by systematic analysis
of protein complexes. Nature 415: 141–147.
Gilman, A.G., Simon, M.I., Bourne, H.R., Harris, B.A., Long, R., Ross,
E.M., Stull, J.T., Taussig, R., Arkin, A.P., and Cobb, M.H. 2002.
Overview of the Alliance for Cellular Signaling. Nature
Hamosh, A., Scott, A.F., Amberger, J., Bocchini, C., Valle, D., and
McKusick, V.A. 2002. Online Mendelian Inheritance in Man
(OMIM): A knowledge base of human genes and genetic disorders.
Nucleic Acids Res. 30: 52–55.
Hanash, S. 2003. Disease proteomics. Nature 422: 226–232.
Hart, G.W. 2003. Structural and functional diversity of glycoconjugates:
A formidable challenge to the glycoanalyst. Methods Mol. Biol.
Ho, Y., Gruhler, A., Heilbut, A., Bader, G.D., Moore, L., Adams, S.L.,
Millar, A., Taylor, P., Bennett, K., and Boutilier, K. 2002. Systematic
identification of protein complexes in Saccharomyces cerevisiae by
mass spectrometry. Nature 415: 180–183.
Hood, L. and Galas, D. 2003. The digital code of DNA. Nature
Hunter, T. 1998. The Croonin lecture 1997: The phosphorylation of
proteins on tyrosin: Its role in cell growth and disease. Philos. Trans.
R Soc. Lond. B Biol. Sci. 353: 583–605.
Ito, T., Tashiro, K., Muta, S., Ozawa, R., Chiba, T., Nishizawa, M.,
Yamamoto, K., Kuhara, S., and Sakaki, Y. 2000. Toward a
protein–protein interaction map of the budding yeast: A
comprehensive system to examine two-hybrid interactions in all
possible combinations between the yeast proteins. Proc. Natl. Acad.
Sci. 97: 1143–1147.
Karin, M. and Ben-Neriah, Y. 2000. Phosphorylation meets
ubiquitination: The control of NF-?B activity. Annu. Rev. Immunol.
Kitano, H. 2002. Computational systems biology. Nature 420: 206–210.
Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C.,
Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al.
2001. Initial sequencing and analysis of the human genome. Nature
Miki, Y., Swensen, J., Shattuck-Eidens, D., Futrealm P.A., Harshman, K.,
Tavtigian, S., Liu, Q., Cochran, C., Bennett, L.M., Ding, W., et al.
1994. A strong candidate for the breast and ovarian cancer
susceptibility gene BRCA1. Science 266: 66–71.
Nakai, K. 2000. Protein sorting signals and prediction of subcellular
localization. Adv. Protein Chem. 54: 277–344.
Navarro, J.D., Niranjan, V., Peri, S., Jonnalagadda, C.K., and Pandey, A.
2003. From biological databases to platforms for biomedical
discovery. Trends Biotechnol. 21: 263–268.
Pandey, A. and Mann, M. 2000. Proteomics to study genes and
genomes. Nature 405: 837–846.
Pawson, T. and Nash, P. 2003. Assembly of cell regulatory systems
through protein interaction domains. Science 300: 445–452.
Pruitt, K.D., Katz, K.S., Sicotte, H., and Maglott, D.R. 2000. Introducing
RefSeq and LocusLink: Curated human genome resources at the
NCBI. Trends Genet. 16: 44–47.
Romero, F., Ramos-Morales, F., Dominguez, A., Rios, R.M.,
Schweighoffer, F., Tocque, B., Pintor-Toro, J.A., Fischer, S., and
Tortolero, M. 1998. Grb2 and its apoptotic isoform Grb3–3 associate
with heterogeneous nuclear ribonucleoprotein C, and these
interactions are modulated by poly(U) RNA. J. Biol. Chem.
Peri et al.
2370 Genome Research
Schachter, V. 2002. Bioinformatics of large-scale protein interaction
networks. Biotechniques (Suppl): 16–27.
Schultz, J., Milpetz, F., Bork, P., and Ponting, C.P. 1998. SMART: A
simple modular architecture research tool: Identification of signaling
domains. Proc. Natl. Acad. Sci. 95: 5857–5864.
Uetz, P., Giot, L., Cagney, G., Mansfield, T.A., Judson, R.S., Knight, J.R.,
Lockshon, D., Narayan, V., Srinivasan, M., and Pochart, P. 2000. A
comprehensive analysis of protein–protein interactions in
Saccharomyces cerevisiae. Nature 403: 623–627.
Venkitaraman, A.R. 2002. Cancer susceptibility and the functions of
BRCA1 and BRCA2. Cell 108: 171–182.
Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton,
G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., et al. 2001.
The sequence of the human genome. Science 291: 1304–1351.
von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S.,
and Bork, P. 2002. Comparative assessment of large-scale data sets of
protein–protein interactions. Nature 417: 399–403.
Walhout, A.J. and Vidal, M. 2001. Protein interaction maps for model
organisms. Nat. Rev. Mol. Cell Biol. 2: 55–62.
Xenarios, I., Rice, D.W., Salwinski, L., Baron, M.K., Marcotte, E.M., and
Eisenberg, D. 2000. DIP: The database of interacting proteins. Nucleic
Acids Res. 28: 289–291.
Zhu, H., Bilgin, M., Bangham, R., Hall, D., Casamayor, A., Bertone, P.,
Lan, N., Jansen, R., Bidlingmaier, S., and Houfek, T. 2001. Global
analysis of protein activities using proteome chips. Science
Ziauddin, J. and Sabatini, D.M. 2001. Microarrays of cells expressing
defined cDNAs. Nature 411: 107–110.
WEB SITE REFERENCES
http://www.hprd.org; Human Protein Reference Database.
http://www.ibioinformatics.org; Institute of Bioinformatics home page.
Received June 23, 2003; accepted in revised form August 12, 2003.
Human Protein Reference Database