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In Silico Screening of Novel Inhibitors for HPV: A Rational Structure Based Approach (Docking Versus Pharmacophore Model Generation)

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The E2 protein from HPV 16 was selected as a molecular target and its known structures were exploited for broad scope of "hits" to be identified in the screening process. We compared both structure-based and ligand-based design approaches for virtual screening. Databases enriched in natural compounds were used for virtual screening based on molecular docking. In this study, we identified novel classes of HPV inhibitors by means of a structure-based drug-design protocol involving Pharmacophore based virtual screening with molecular docking simulation.
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Letters in Drug Design & Discovery, 2009, 6, ???-??? 1
1570-1808/09 $55.00+.00 © 2009 Bentham Science Publishers Ltd.
In Silico Screening of Novel Inhibitors for HPV: A Rational Structure
Based Approach (Docking Versus Pharmacophore Model Generation)
G. Reshmi and M. Radhakrishna Pillai*
Translational Cancer Research Laboratory, Rajiv Gandhi Center for Biotechnology, Thycaud PO, Thiruvananthapu-
ram-695014, Kerala, India
Received March 05, 2009: Revised June 09, 2009: Accepted June 09, 2009
Abstract: The E2 protein from HPV 16 was selected as a molecular target and its known structures were exploited for
broad scope of “hits” to be identified in the screening process. We compared both structure-based and ligand-based design
approaches for virtual screening. Databases enriched in natural compounds were used for virtual screening based on mo-
lecular docking. In this study, we identified novel classes of HPV inhibitors by means of a structure-based drug-design
protocol involving Pharmacophore based virtual screening with molecular docking simulation.
Keywords: HPV, TAD, Molecular docking, Pharmacophore model, Virtual screening, GOLD.
INTRODUCTION
Oncogenic Papillomaviruses (HPVs) are responsible for
approximately half a million cases of cervical cancer each
year. HPVs also cause genital warts, and are the most com-
mon sexually transmitted diseases in many countries. This
awareness has paved the way for new exciting approaches
for preventing cervical cancer, which is the second most
common malignancy of women worldwide [1]. The principal
agent in the etiology of cervical cancer, human papillomavi-
rus (HPV) type 16, encodes early proteins, E1 and E2, and is
involved with host cell systems to maintain and produce vi-
rus [2]. Loss of E2 function, which usually occurs in early
stage cancers, allows dysregulated expression of the viral
oncoproteins, E6 and E7 [3]. HPV E2 binds with high affin-
ity to specific sites in viral DNA and also binds to E1 and
targets this replication factor to the viral origin of replication.
HPV16 E2 and Cervical Tumor Progression
The major factor contributing to cervical cancer progres-
sion rests upon disruption or loss of the E2 gene product. E2
protein maintains a strict control over the E6 and E7 gene
expression while it is maintained in the extra chromosomal
form. During the progression of cervical cancer, the viral
DNA is integrated into the host genome such that E6 and E7
and the upstream transcriptional regulatory region are intact
while the expression of E2 is lost suggesting that these pro-
teins play a regulatory role in carcinogenic progression [4-5].
It has been postulated that in the absence of E2 polypeptides,
the transcription control over E6 and E7 is lifted leading to
their expression in a deregulated manner which may lead to
tumor formation.
Druggable Hot Spots of Molecular Target-HPVE2
E2 protein of human papillomavirus regulates the
primary transcription and replication of the viral genome.
*Address correspondence to this author at the Translational Cancer Re-
search Laboratory, Rajiv Gandhi Center for Biotechnology, Thycaud PO,
Thiruvananthapuram-695014, Kerala, India; Tel: +91- 471-2341716 |
2347975; Fax: 91- 471-2348096; E-mail: mrpillai@rgcb.res.in
Both activities are governed by a-200 amino acid N-terminal
module (E2NT) which is connected to a DNA binding C-
terminal module by a flexible linker [6]. The crystal structure
of the E2NT module from high-risk type 16 human
papillomavirus reveals an L-shaped molecule with two
closely packed domains, each with a novel fold. The
transactivation module is composed of two domains, N1 and
N2, arranged so as to give it an overall L-shaped appearance.
Arg37, Glu39 and Ile73 are three conserved amino acids,
their point mutational effects mainly control the two
principal functions of E2, transactivation and HPV DNA
replication [reviewed in 7-10]. The dimerisation surface of
E2 represents a good target for designing anti-viral drugs,
since it is essential for viral transcription, there is no
homologous human protein and the residues forming the
interface are highly conserved among different viral strains.
It has been suggested that E2 can regulate the switch
between early gene expression and viral genome replication
during HPV infection [11]. Inhibition of HPV E2 might lead
to increased expression of the E6 and E7; however, recent
publications question this assumption which is based primar-
ily on E2 over-expression. For these reasons targeting E2
might be effective in early lesions by inhibiting replication
and viral episome segregation.
Structure-Based Drug Discovery
Structure-based drug discovery has made significant pro-
gress in the past 30 years benefiting from recent advances in
high performance and distributed grid computing [12]. Com-
puter-aided or in silico design is being utilized to expedite
and facilitate hit identification, hit-to-lead selection, optimize
the absorption, distribution, metabolism, excretion and toxic-
ity profile and avoid safety issues [13]. Virtual screening
[14], which has shown a great promise in drug discovery,
will play an important role in digging out active leads from
large compound libraries. It is intended to reduce the size of
chemical space and thereby allow focus on more promising
candidates for lead discovery and optimization. Natural
products, containing inherently large scale structural diver-
sity than synthetic compounds, have been the major re-
sources of bioactive agents and will continually play as pro-
2 Letters in Drug Design & Discovery, 2009, Vol. 6, No. 7 Reshmi and Pillai
tagonists for discovering new drugs [15]. Here, we focus on
an integrated database screening strategy involving 3D data-
base searching approaches such as Pharmacophore mod-
els(ligand-based design) applicable as queries in 3D database
searches and molecular docking (structure-based design) of
potential ligands into a binding pocket. In the post-genomic
era, rational anti-cancer drug discovery aims to discover or
design small molecules that modulate the activity of key
therapeutic targets pivotal for carcinogenesis [16]. This work
mainly aims to discover novel small molecular inhibitors
against important molecular targets involved in cervical can-
cer.
This in silico study strongly highlights the importance of
computational approaches in drug discovery and an attempt
to identify putative small inhibitors that might inhibit the
activity of the target molecule chosen for the study (HPV
E2NT). GOLD [17] a commercial docking package was used
to dock ligands in the E2NT binding site by virtual screening
(VS) of downloaded ligand databases [18]. Since there are
no known inhibitors of HPV 16 E2 TAD, we developed the
ligand-based Pharmacophore model (generated by Ligand
Scout 2.02[19]) based on the ligand bound structure of HPV
11E2 TAD. The overall structure for both proteins has been
found to be highly similar. In our study we developed a bind-
ing site model for HPV 16 E2 TAD and this promising result
confirmed the applicability of our active site model for fur-
ther studies. To date, there have been no lead generation and
optimization studies reported for this transactivation domain
of HPV E2 receptor. In the present study, we attempt virtual
screening (VS) of a number of ligands on HPV E2 and
evaluate how different ligand poses change with respect to
the scoring functions.
METHODS
Active Site Prediction
The X-ray crystallographic structure of the complete
transactivation domain (TAD) of human papillomavirus type
16 E2 protein was retrieved from the Protein Data Bank,
(PDB-ID: 1 DTO) [6]. We decided to exploit the protein-
ligand structure as a starting point for structure-based virtual
screening based on molecular docking. Since HPV 16 E2
TAD lacks any bound ligands, the binding site residues need
to be identified prior to docking. Experimental studies car-
ried out by Wang et al. [20] provide an insight into the ex-
treme similarity between HPV 11 and HPV 16 E2 TAD pro-
tein structures. The all C superposition of HPV 11 and
HPV 16 (187 residues) gave an RMSD of mere 1.54 Å. The
N terminal (residues 3-94) and C terminal (residues 95-195)
sub-domains of these proteins were superimposed individu-
ally and were shown to yield RMSD of 0.85 Å and 1.14 Å,
respectively. All the secondary structural elements were ob-
served to be conserved. Identification of homologous resi-
dues making up the active site of HPV16 E2 was carried out.
This was taken into account in conjunction with the results
obtained from Site Finder implemented in MOE (Chemical
Computing Group Inc., Montreal, Canada). HPV 16 E2 TAD
active site is a channel that is lined with hydrophobic resi-
dues and protrudes towards the centre .The binding pocket
includes both a deep and narrow cavity and Tyr 32 sterically
hinders the binding pocket. Active site residues of HPV 16
E2 TAD are Ile 15, Tyr 19, Asp 28, His 29, Ile 30, Tyr 32,
Trp 33, Met 36, Glu 39, Ala 63, Val 64, Ser 65, Asn 67, Lys
68, Ala 69, Gln 71, Ala 72, Leu 79, Thr 93, Leu 94, Gln 95,
Val 97, Ser 98, Leu 99, Glu 100, Val 101.
Preparation of the Protein and the Binding Site
After detection of active site of the TAD domain, a ‘clean
input file’ was generated by removing water molecules, ions,
ligands and subunits not involved in ligand binding from the
original structure file. Hydrogen atoms were then added to
the protein and the active site was inspected to make suitable
corrections for tautomeric states of histidines, hydroxyl
group orientations and protonation states of charged residues
[21-22]. Local minimization was then performed in the pres-
ence of restraints to relieve potential bad contacts, at the
same time maintaining the protein conformation very close
to that observed in the crystallographic model. The resulting
receptor model was saved to a PDB file (compatible with
GOLD input file formats).
Preparation of the Database
The chemical databases used in our virtual screening in-
cluded the ZINC (http:/blaster.docking.org/zinc/), Chembank
(chembank.broad.harvard.edu/), and Ambinter (http://www.
ambinter.com/) [23]. Collectively, these three databases,
offered a collection of ~one million small-molecule organic
compounds. From this databases physiochemical properties,
such as LogP, number of H-bond donors/acceptors, numbers
of rotatable bonds calculated and 2D filters were applied to
remove inorganic, and compounds that were not drug-like
[24]. The filtered datasets were converted 2-D to 3-D using
CORINA (Molecular Networks GmbH) program with the
standard settings.
Molecular Docking
The molecular docking program GOLD (Version 3.0.1)
[17], was used to perform the virtual screening [25]. For the
study, the binding pocket on the TAD was the region tar-
geted for docking. The receptor was defined from the crys-
tallographic coordinates of the ligand (residues within 3.5 Å
of the ligand). The binding site atoms were further defined
from a file listing the cavity atoms. Dockings were per-
formed under ‘Standard default settings’ mode. The docking
results were analyzed using SILVER software, which allows
visualization of the protein-ligand docking and calculation of
several descriptors such as feasible hydrogen bonding be-
tween the protein and the ligand [22]. The Smiles formula of
the molecules with the highest scores was taken from the
corresponding entries in the database and was checked for
fulfillment of the Lipinski rule of five using PubChem [24,
26]. GOLD is widely regarded as one of the best docking
programs [28-30], Goldscores and Chemscores fitness func-
tion was employed in scoring compounds. For each of the 10
independent Genetic Algorithm (GA) runs, with a selection
pressure 1.1, 100 000 and default operator weights were used
for crossover, mutation, and migration of 95, 95, and 10,
respectively. Default cutoff values of 3 Å for hydrogen
bonds and 4.0 Å for vdW were employed while docking, a
limited flexibility is allowed for the hydrogen atoms in the -
In Silico Screening of Novel Inhibitors for HPV Letters in Drug Design & Discovery, 2009, Vol. 6, No. 7 3
OH and -NH3 substituents of the side chains of Ser, Thr, and
Lys residues. The best docked solutions were subjected to
energy minimization, re-docking and re-scoring [31-33]. The
highest ranking molecules were re-docked independently
into the protein active site under the default settings and
were checked for satisfactory GOLD scores and exhibition
of correct binding to the active site of the protein molecule.
Pharmacophore Generation
In the pharmacophore generation procedure, is important
to know biological function of the studied target. In this
study, the Pharmacophore model was derived by means of a
new tool LigandScout [19], a program for automatic Phar-
macophore identification from ligand bound protein com-
plexes. Here we developed a model from ligand bound
HPV11 E2 transactivation domain because it shows extreme
similarity with HPV 16 E2 TAD. The E2-TAD of HPV11
shares 47% amino acid identity with HPV16 and at position
101, HPV16 has a valine, whereas “low-risk” HPV11 has a
methionine at this position. We referred common chemical
features derived from the bioactive ligand conformations of
protein complex of HPV11 E2 TAD and LigandScout gener-
ates models of each one. The chemical features of both the
ligands were effectively mapped and the pharmacophore
model developed for HPV 16 E2 TAD protein. This model
was used to perform a Pharmacophore search of 3-D com-
pound database to identify ‘hits’ that satisfy all conforma-
tional requirements.
In Silico Screening
After assessing the query Pharmacophore model, virtual
screening was carried out by using the tool MOE (Chemical
Computing Group Inc., Montreal, Canada).The flexible
search mode was adopted to screen the database which con-
tains the structural information of millions of chemicals. The
resulting hit molecules were ranked and the compounds with
highest fit values were extracted and subjected to docking
study to select hits which satisfy the hydrogen bond acceptor
(HBA) feature of the models.
RESULT AND DISCUSSION
Virtual Screening Using Molecular Docking
The application of docking offers the possibility of virtu-
ally testing the ability of a molecule to bind to a target.
Docking tools utilize the crystal structure of a target protein
and find different binding modes for the molecules of inter-
est. Before preceding to Virtual Screening it was necessary
to identify the binding pocket residues of HPV 16 E2 TAD
because its X-ray crystallographic structure retrieved from
the PDB lacks any bound inhibitors. Therefore in the present
case, the binding pocket was detected by Site Finder module
implemented in MOE (Chemical Computing Group Inc.,
Montreal, Canada) Fig. (1), with the help of shape-based
algorithm the tool identified concave regions on the protein
surface. The module also evaluates whether these cavities
have an appropriate composition of hydrophobic and hydro-
philic atoms. The genetic algorithm-based docking program
GOLD [27] was used to screen the 3D databases, flexibly
docking million compounds to HPV E2 TAD. A total of
1568 molecules from a pool of 86,723 molecules were iden-
tified as hits while 157 molecules were re-docked, re-scored
and indicated as highly active. The targeted region (HPV E2
TAD) defined in our virtual screening covers Tyr19, Tyr32,
Leu94 residues in active site. We hypothesize that a small
molecule that binds to this region will compete with the
above residues, consequently blocking the transactivation of
domain Fig. (2). Some of the hits retrieved in database
search also showed good Gold scores and have good interac-
tions with active site residues. Three hits out of 46 com-
pounds with high GOLD dock score >50 listed in Table (1)
were considered as final hits for further experimental evalua-
tion Fig. (3). These molecules showed extraordinary results
with respect to all properties like calculated drug-like proper-
ties [21, 24] listed in Table (2) and thus can be treated as
good putative hits in the design of potent inhibitors of HPV.
Fig. (1). Transactivation domain of HPV16 E2 protein showing
binding pocket.
Fig. (2). HPV16 E2 TAD domain with major active site residues.
Virtual Screening Using Pharmacophore Model
The pharmacophore model generated by the LigandScout
program includes five features: one hydrogen bond donor
4 Letters in Drug Design & Discovery, 2009, Vol. 6, No. 7
Reshmi and Pillai
Table 1. Potential Hit Compounds with High Gold Scores Obtained by Structure- Based Approach
Compound And Zinc id Gold Score Compound Structure
2-(2-hydroxyphenyl)sulfanyl-N-[2-[[2-(2-hydroxyphenyl)
sulfanylacetyl] amino] phenyl] acetamide
Zinc id: 01083718
Mol.Wt-440.537
59.11 C22H20N2O4S2
Propyl4-[[2-(1H-benzoimidazol-2-ylsulfanyl)
acetyl]amino]benzoate
Zinc id:03302168
Mol.Wt-369.439
59.01 C19H19N3O3S
2-[[4-amino-5-(3-propan-2-yloxyphenyl)
-1,2,4-triazol-3-yl] sulfanyl]-N-
(3,4-difluorophenyl) acetamide
Zinc id:02393865
Mol.Wt - 419.449
57.35 C19H19F2N5O2S
Fig. (3). Docked conformation of potential hits and respective active site residues involved in binding (structure-based approach).
In Silico Screening of Novel Inhibitors for HPV Letters in Drug Design & Discovery, 2009, Vol. 6, No. 7 5
Table 2. Table Showing Values of Lipinski Parameters for Hits Obtained
No Zinc id Pubchem id Molecular weight H donors H Acceptors Log p
1 01083718 1269872 440.537 4 4 4.31
2 03302168 2417786 369.439 2 6 3.30
3 02393865 1992969 419.449 2 8 2.94
Fig. (4). Docked conformation of potential hits obtained from GOLD (Ligand-based approach).
Fig. (5). (A): Pharmacophore model of Ligand-1 used within LigandScout (red arrows, HBA; yellow spheres, hydrophobic sites; gray, ex-
cluded volumes).
(B): Pharmacophore model of Ligand-2 used within LigandScout (red arrows, HBA; yellow spheres, hydrophobic sites; gray, excluded vol-
umes).
6 Letters in Drug Design & Discovery, 2009, Vol. 6, No. 7
Reshmi and Pillai
(HBD), two hydrogen bond acceptors (HBA) and two hy-
drophobic groups. Besides, the program automatically gen-
erated several excluded volumes. Herein, we present struc-
ture-based PCM Fig. (5A) and Fig. (5B) which are used for
first -screening [34-35]. In order to select hits satisfying the
hydrogen bond acceptor (HBA) features of the models, mo-
lecular docking was carried out as second-screening [36].
The model query PCM Fig. (6) was exported to MOE for
subsequent VS with ZINC database (http:/blaster.docking.
org/zinc).Only the filtered subset of 5000 molecules was
used for the screening. VS with ZINC using ligand-based
Pharmacophore model yielded 82 hits that satisfied the
specified requirements and out of which three compounds
shows high GOLD dock score >50 listed in Table (3). All the
hits obtained in database searching underwent molecular
docking into HPV 16 E2 TAD binding pocket with GOLD
Fig. (4). These putative hits showed good drug-like proper-
ties listed in Table. (4). The results indicate that our model is
as a valuable tool for screening databases to identify mole-
cules with potential antiviral activity.
Fig. (6). LigandScout Pharmacophore model generated from
HPV11 E2 TAD/ligand complex (red arrows, HBA; yellow
spheres, hydrophobic sites; gray spheres, excluded volumes).
Table 3. Potential Hit Compounds with High Gold Scores Obtained by Ligand -Based Approach
Compound And Zinc id Gold Score Compound Structure
Bis (phenylmethoxyamino) phosphoryloxybenzene)
Zinc id: 03851138
Mol.Wt-384.366
56.2 C20H21 N2O4P
2-[[4-amino-5-(4-methyl phenyl)
-1,2,4-triazol-3-yl] sulfanyl]-N-
(5-chloro-2-methyl-phenyl) acetamide
Zinc id:02387705
Mol.Wt-337.887
55. 65 C18H18lCN5OS
2-[(4-amino-5-phenyl-1,2,4-triazol-3-yl)
sulfanyl]-N-(9-ethylcarbazol-3-yl)
acetamide
Zinc id:02361742
Mol.Wt- 442.537
55.41 C24H22N6OS
In Silico Screening of Novel Inhibitors for HPV Letters in Drug Design & Discovery, 2009, Vol. 6, No. 7 7
CONCLUSIONS
Since HPV is the major causative factor of cervical can-
cer, an inhibitor of HPV E2 may block progression to inva-
sive cancer by inhibiting HPV replication. To find such puta-
tive inhibitors of HPV E2, the x-ray structure of the transac-
tivation domain of HPV E2 was chosen as a molecular tar-
get. Since experimental screening of therapeutic targets is
time-consuming and cost-intensive, in this study we used
computational screening approaches to identify putative
small molecular inhibitors of HPV16 E2 from natural com-
pound enriched databases. In the present work, we carried
out docking studies on the commercial dataset of million
molecules to HPV 16 E2 TAD protein, with the purpose of
developing a docking protocol fit for the target under study,
eventually applicable for more time-consuming virtual
screening of larger database of compounds. Usually, docking
to protein structures that do not have a ligand present, as in
the case of HPV 16 E2 TAD protein, dramatically reduces
the expected performance of structure-based methods. To
overcome such a limitation, we enhanced the simple docking
procedure by means of a combined structure-and ligand-
based drug design approach. The mapping information based
on the Pharmacophore model we developed is now being
taken advantage in the identification of novel lead molecules
through 3D databases searches.
In conclusion all of these goals have been realized and 6
putative inhibitors identified. Hence, these candidates can be
taken to the wet laboratory in order to carry out further test-
ing, optimization and investigation of their specific activities
and effectivity against HPV related diseases. In this respect,
this paper represents the first example of successful indi-
viduation of a potential hits through molecular docking
simulations on a HPV 16 E2 TAD target. At the same time,
compounds showing good docking scores and satisfy Lipin-
ski’s rule of five showing good drug-like properties derived
from this structure and its different binding modes, could
carry through further lead optimization to more potent HPV
16 E2 TAD inhibitors. Overall, the results from these Phar-
macophore versus docking studies underscore the impor-
tance and usefulness of employing computational biology
and chemoinformatics approaches in the areas of drug dis-
covery and molecular target identification.
ACKNOWLEDGEMENT
This study is supported by Department of Biotechnology
Government of India, (Grant No: - BT/PR5435/BID/07/095/
2004).
ABBREVIATIONS
VS = Virtual screening
TAD = Transactivation domain
PCM = Pharmacophore model
HBA = Hydrogen bond acceptor
HBD = hydrogen bond donor
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... The discovery of new lead compounds from large database of ligands is widely used in virtual screening method to identify the most promising compounds from the database for further study [12][13][14]. Successful virtual screenings have resulted in discovery of molecules either resembling the *Address correspondence to this author at the Faculty of Science, Lampan-gRajabhat University, Lampang 52100 Thailand; Tel (+66)5424-1052; Fax: (+66)5424-1052; E-mail: Siripit_p@hotmail.com native ligands of the specific targets or in the discovery of the entire leads [15,16]. To develop new Cdk5 inhibitors, a Cdk5 template based on the protein target of Cdk5 inhibitors was employed [17]. ...
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