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BMC Bioinformatics
Open Access
Database
PDTD: a web-accessible protein database for drug target
identification
Zhenting Gao1,3, Honglin Li*1,2, Hailei Zhang2, Xiaofeng Liu1, Ling Kang2,
Xiaomin Luo1, Weiliang Zhu1, Kaixian Chen1, Xicheng Wang*2 and
Hualiang Jiang*1,3
Address: 1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of
Sciences, Shanghai 201203, China, 2Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment,
Dalian University of Technology, Dalian 116023, China and 3School of Pharmacy, East China University of Science and Technology, Shanghai
200237, China
Email: Zhenting Gao - zhentg@mail.shcnc.ac.cn; Honglin Li* - hlli@mail.shcnc.ac.cn; Hailei Zhang - hailei@nus.edu.sg;
Xiaofeng Liu - xxffliu@gmail.com; Ling Kang - klxju@163.com; Xiaomin Luo - xmluo@mail.shcnc.ac.cn;
Weiliang Zhu - wlzhu@mail.shcnc.ac.cn; Kaixian Chen - kxchen@mail.shcnc.ac.cn; Xicheng Wang* - guixum@dlut.edu.cn;
Hualiang Jiang* - hljiang@mail.shcnc.ac.cn
* Corresponding authors
Abstract
Background: Target identification is important for modern drug discovery. With the advances in the development of
molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins
with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database
with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Target Fishing Docking)
http://www.dddc.ac.cn/tarfisdock, which has been used widely by others. Recently, we have constructed a protein target
database, Potential Drug Target Database (PDTD), and have integrated PDTD with TarFisDock. This combination aims
to assist target identification and validation.
Description: PDTD is a web-accessible protein database for in silico target identification. It currently contains >1100
protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and
several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830
known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related
diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both
nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease
name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be
downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock,
PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2
file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores.
Conclusion: PDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock,
PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications
include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e.
new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable
platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/.
Published: 19 February 2008
BMC Bioinformatics 2008, 9:104 doi:10.1186/1471-2105-9-104
Received: 14 August 2007
Accepted: 19 February 2008
This article is available from: http://www.biomedcentral.com/1471-2105/9/104
© 2008 Gao et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Background
Until 2000, only ~500 drug targets had been reported [1],
among which only 120 drug targets are actually marketed
[2]. The completion of human genome and numerous
pathogen genomes suggests that there are 30,000 to
40,000 genes and at least the same number of proteins,
and many of these proteins are potential targets for drug
discovery. It has been estimated that there are more than
2,000 potential drug targets with at least one drug candi-
date in clinical trial [2,3]. This is a reservoir for drug dis-
covery and target identification. However, how to
extensively utilize this source is a challenge. Expressing all
these proteins and screening compounds against the cor-
responding models constructed based on the proteins is
extremely unpractical, because it is intolerably expensive
and time-consuming. Recent promising advancement in
docking-based virtual screening has demonstrated the
efficiency of this approach in discovering lead (active)
compounds [4,5]. On the other hand, reverse (or inverse)
docking approaches have become promising computa-
tional tools to find the probable target proteins for active
compounds, natural products or old drugs [6-10]. Both
these two researches need the information of target pro-
teins, in particular the information of structures and active
sites. However, such information of most drug targets is
dispersedly deposited in the literatures or other databases
like Protein Data Bank (PDB). Therefore, it is in dire need
of a database containing comprehensive information of
the potential target proteins.
Recently, some notable efforts have been made to par-
tially satisfy this requirement. The Therapeutic Target
Database (TTD) is one such example [11], which provides
information about the known therapeutic targets, disease
conditions and the corresponding drugs. DrugBank is a
bioinformatics/cheminformatics resource that combines
detailed drug data with comprehensive drug target infor-
mation [12]. A number of ligand-protein interaction data-
bases have also emerged including LigBase [13], PDBsite
[14], SitesBase [15], MSDsite [16], PDB-Ligand [17] and
AffinDB [18]. Unfortunately, these databases were not
specifically designed for discovering new leads by using
virtual screening approaches and new targets by using
reverse docking. They also cannot be used to figure out
specific pharmaceutical information related to the sec-
ondary effects of an old drug (i.e. new pharmacological
usage) or an existing target (i.e. new pharmacological or
toxic relevance). Ideally, a target database may provide
not only abundant information about the potential target
proteins such as 3D structures, binding (active) sites, bio-
logical (pharmacological) functions, related diseases, but
also appropriate computational tools to mine the infor-
mation about targets. Herein, we present a web-accessible
protein database, PDTD (Potential Drug Target Database).
Integrated with our reverse docking server, TarFisDock [8],
PDTD is a valuable platform for target identification.
Construction and content
Fundamentally, PDTD has dual functions of querying
drug target information and identifying the potential
binding proteins of an active compound or an existing
drug by using reverse docking approach. Accordingly,
PDTD contains two sub-databases types, one is the struc-
tural sub-database and another is the informatics sub-
database. All data are associated with a relational database
implemented using MySQL and can be queried through
web interface. Through three computational engines,
search engine, visualization engine and TarFisDock, users
can implement interactive query and computation with
the PDTD (Figure 1). The structural sub-database stores
each protein in both PDB format and mol2 format with
Amber charges; sequence and active site information were
also included in the structural sub-database. The infor-
matics database stores the data of target categories, related
disease information, biological functions and associated
regulating (signaling) pathways. PDTD currently contains
>1100 entries covering the information of >800 known
drug targets.
The target proteins in PDTD were selected from scientific
literatures [1,19-21] and several online databases such as
TTD [11], DrugBank [12] and Thomson Pharma [22].
Since PDTD is designed to search the probable binding
proteins for new active compounds or existing drugs by
using reverse docking, it only contains the proteins with
known 3D structures determined experimentally by the X-
ray crystallographic or NMR methods. The coordinates of
proteins were isolated from the PDB. Since not all PDB
structures are of equal quality, a protein structure is
selected according to the following criteria when it has
several redundant records in PDB: (i) select the structure
without mutation and missing residues around the active
site; (ii) select the structure with high resolution; (iii)
select the structure complexed with ligand. For each
selected protein in PDTD, amino acid residues within 6.5
Å around the bound ligand were used to define the bind-
ing (active) site. A PDB entry could contain data on a
number of binding sites. If so, a separate entry was gener-
ated in the PDTD to accommodate each of the sites.
HETATM records in PDB files were used to define the lig-
ands. PDB and mol2 files of each protein were also stored
in the structural sub-database. All kinds of structures for a
drug target can be visualized using the "Jmol" JAVA applet
[23].
Most of drug targets in PDTD have been collected with
single structure (709 cases). Since our reverse docking pro-
gram, TarFisDock, has not considered the flexibility of
proteins, PDTD contains some redundancy for the flexible
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proteins. For example, HIV-1 protease has 27 entries and
dihydrofolate reductase (DHFR) has 14 entries in the
database. The redundancy of each target is shown in Fig-
ure 2a.
According to therapeutic areas, the drug targets may be
categorized into 14 types (Figure 2b). It is convenient for
users to custom a special list when they predict potential
binding targets for small molecules using TarFisDock.
Among targets having explicit therapeutic functions, the
targets related neoplastic disease are most populated, fol-
lowing are hormones and hormones antagonists related
targets. Targets related to viral infections are also major
contributors in PDTD. The distribution of biochemical
classification is shown in Figure 2c, indicating that PDTD
mainly consists of enzymes, receptors, ion channels,
transporters, nuclear receptors, binding protein, structural
proteins, signaling proteins, factors, regulators and hor-
mones. The targets which can not be assigned into any of
these biochemical classes are grouped into an "unknown"
class. The selected drug targets are enriched in enzymes
(80.2%). G-protein coupled receptors (GPCR) and other
receptors which account for most drug targets seldom
have crystal structures, resulting that the ratio of receptor
targets in PDTD is only 4.2%.
Utility and Discussion
Web interface: query, download and exploration
PDTD is supported with a friendly designed web interface
so that users can easily query the target information, and
retrieve, visualize or download the distributions of the
drug target files as they desire (Figure 3). PDTD has been
designed to provide fast and easy access to target informa-
tion. The popular MySQL backend was chosen as database
server. Using the scripting language PHP, special care was
taken to generate a clearly structured layout which enables
fast and easy navigation.
All the data can be accessed and retrieved directly via the
web browser, PDTD consists of a classification table and a
keyword search box. The user can search a drug target
manually from the classification table, or automatically
by using the keyword search function, such as PDB ID, tar-
get name, or disease. Every target has its own result page
containing comprehensive information including PDB
ID, target name, target category, related disease, its struc-
ture, and active site. The PDTD was carefully annotated
according to information found in the PDB, UniProt [24],
KEGG [25] and Enzyme Structures Database [26]. PDTD
also provides hyperlinks to other databases like TTD and
DrugBank, which allow easy navigation for more infor-
mation about target structure, source and function (See
Links to other databases). The related structures for each
PDTD system architectureFigure 1
PDTD system architecture. The system is implemented in MySQL and PHP script, user can freely access the database at
http://www.dddc.ac.cn/pdtd/.
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Functional and biochemical classifications of PDTD protein entriesFigure 2
Functional and biochemical classifications of PDTD protein entries. (A) Database redundancy. Each bar represents
the number of targets that have the same amounts of copies in the PDTD. Break is applied to the y axes. (B) Distribution of
drug targets according to their therapeutic areas. (C) Distribution of drug targets according to their biochemical criteria, which
include enzymes, receptors, ion channels, transporters, nuclear receptors, binding protein, structural proteins, signaling pro-
tein, and factors, regulators and hormones.
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Screen shots of the PDTDFigure 3
Screen shots of the PDTD. A screen shot of the PDTD showing several possible view of information describing the drug
target. Not all fields are shown.
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drug target can be downloaded freely from the detailed
page by clicking on "MORE" button. Furthermore, user
can download the classified target structures and all target
files from the "Download" page.
Also, users can customize the list of drug targets in which
they want to perform reverse docking process to predict
the potential binding targets for any small molecule,
which to our knowledge is not provided by other public
websites. Consequently, TarFisDock will output the list of
results with binding poses of molecules against each tar-
gets, along with corresponding information, disease,
annotation and links to other databases, which are also
presented (See the Applications below).
Applications
In bringing together the reverse docking server TarFisDock
[8], PDTD has been widely used to identify binding pro-
teins for small molecule. The binding proteins for several
molecules have been verified through bioassay and crystal
structure determination for target-ligand complexes [9].
In general, one drug molecule may interact with several
targets including targets associated with side effect (toxic-
ity). TarFisDock provides multiple options for selecting
protein targets. These clues are useful for further experi-
mental test in discovering new pharmacological efficacy
or toxicity for an existing drug. In general, combining with
PDTD, TarFisDock web sever is a convenient tool for
"fishing" the target proteins of small molecules, the user
just inputs the structure of querying compound and cus-
tomizes a target list from PDTD (a list of all the targets is
recommended). The results can be downloaded in the
form of mol2 file with the binding pose of each com-
pound and a list of potential binding targets according to
their ranking scores.
In addition, benchmark searches for the old version of
PDTD (698 entries) were performed using TarFisDock
taking vitamin E, an anti-oxidant, and 4H-tamoxifen, an
anti-cancer agent as probes [8]. In this study, similar
benchmark searches for the current version of PDTD
(1186 entries) have been carried out. For vitamin E, eight
(12 entries) of the twelve experimentally verified targets
fall into the top 10% candidates picked up from the PDTD
by the TarFisDock program. For 4H-tamoxifen, five (14
entries) of the eleven experimentally confirmed targets
appear amongst the top 10% of the TarFisDock predicted
candidates. In addition, the PDTD was searched by the
TarFisDock using N-trans-caffeoyltyramine (compound
1), an active natural product discovered by anti-H. pylori
screening in our lab, as a probe in the previous research
[9]. Homology search revealed that, among the fifteen
candidates discovered by reverse docking, diami-
nopimelate decarboxylase (DC) and peptide deformylase
(PDF) are possible binding proteins of compound 1.
Enzymatic assay demonstrated compound 1 and its deriv-
ative compound 2 are the potent inhibitors against the H.
pylori PDF (HpPDF) with IC50 values of 10.8 and 1.25 µM,
respectively. X-ray crystal structures of HpPDF and the
complexes of HpPDF with 1 and 2 were determined, indi-
cating that these two inhibitors bind well with the HpPDF
binding pocket [9]. To exemplify the applications of
PDTD combining with TarFisDock, the brief results of
these three benchmark examples have been uploaded to
the PDTD homepage under the "Benchmark" option.
Links to other databases
General links are given to related drug and target informa-
tion with other databases [11,12]. Each data in PDTD is
linked to the Protein Data Bank, DrugBank, there are also
hypertext links to UniProt [24], Kegg [25] and Enzyme
Structures Database [26] for further structural and func-
tional information.
Conclusion
In summary, PDTD is a comprehensive, web-accessible
database of drug targets, which focuses on those drug tar-
gets with known 3D-structures. By far, PDTD has collected
>1100 entries covering >800 known and potential drug tar-
gets from the Protein Data Bank. PDB structure, mol2 file
and active site information of each drug target were
extracted from the crystal structure, and all the information
can be viewed with molecular visualization tools and can
be downloaded freely by users. Drug targets of PDTD were
categorized by two criteria: therapeutic areas and biochem-
ical criteria. Each target was carefully annotated by brows-
ing several databases, such as DrugBank, TTD, and UniProt.
All these information were stored in informatics sub-data-
base, which was associated to structural sub-database with
a relational database. Users can also use our reverse docking
program to search PDTD for finding the possible binding
protein(s) of a small molecule.
One drug molecule may interact with several targets includ-
ing targets associated with side effect (toxicity). By search-
ing PDTD, TarFisDock may provide multiple options of the
binding proteins for a small molecule. These clues are use-
ful for further experimental test in discovering new targets
and new pharmacological efficacy or toxicity for an existing
drug. Thus, combining with TarFisDock, PDTD is a good
web-accessible protein database for identifying drug targets
and for discovering new usages of old drugs [27,28]. The
user just inputs the structure of querying compound and
customizes a target list from PDTD (a list of all the targets
is recommended), TarFisDock may provide possible bind-
ing proteins of the compound. The results can be down-
loaded in the form of mol2 file with the binding pose of
each compound and a list of potential binding targets
according to their ranking scores.
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PDTD will be updated continuously. We intend to classify
the drug targets according more completely to their biolog-
ical functions, which will be achieved by integrating and/or
linking PDTD with other bioinformatics databases. For
example, links can be directed to the databases of SOURCE
[29] and Gene Ontology [30] for more descriptions of
functional annotations, ontologies, and gene expression
data.
Availability and requirements
PDTD is freely available for academic user at http://
www.dddc.ac.cn/pdtd. To download the files of PDTD,
users must complete a simple registration process and agree
not to republish the data without explicit permission. Users
are invited to contact us through the 'Contact' link and to
participate in the user forum at http://www.dddc.ac.cn/tar
fisdock/forum/.
Authors' contributions
ZG developed the web interface, designed the relational
database scheme, and integrated the database-PDTD with
the reverse docking program-TarFisDock. HL developed the
reverse docking program, participated in the design of web
interface, and contributed to writing the manuscript. HZ,
XL, and LK were major data contributors of the current sys-
tem. XL, WZ and KC provided comments and suggestions
about the features of the database. XW and HJ conceived
the idea of the database, provided direction for its develop-
ment and revised the subsequent drafts of this manuscript.
All authors read and approved the final manuscript.
Acknowledgements
We thank all the colleagues in Drug Discovery and Design Center for their
contributions in literature searching and necessary dealing with files. The
work was partly supported by the Special Fund for Major State Basic
Research Project (grant 2002CB512802), the National Natural Science Foun-
dation of China (grants 20721003 and 10572033), and the 863 Hi-Tech Pro-
gram of China (grant 2007AA02Z304). HL was also sponsored by the
Shanghai Postdoctoral Scientific Program.
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