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ORIGINAL RESEARCH
Molecular docking studies on analogues of quercetin withD-alanine:D-alanine ligase of Helicobacter pylori
Salam Pradeep Singh • Rocktotpal Konwarh •
Bolin Kumar Konwar • Niranjan Karak
Received: 5 March 2012 / Accepted: 17 August 2012
� Springer Science+Business Media, LLC 2012
Abstract Helicobacter pylori (Hp) is a human pathogen
associated with myriad of diseases such as gastritis, peptic
ulceration, piles and gastric cancer. The resistance of Hp
against antimicrobial agents has increased just as that of
other pathogens worldwide, thus emphasizing an urgent
need for developing new antibacterial agents. The D-ala-
nine:D-alanine ligase (Ddl, EC 6.3.2.4) has been considered
as a putative antimicrobial drug target and a lot of inhibitor
screening efforts have been made. Quercetin, a member of
the flavonoids, characterized by a flavone nucleus com-
posed of two benzene rings linked through a heterocyclic
pyrone ring is reported to possess antibacterial activity
against H. pylori Ddl (HpDdl) enzyme. In this milieu, we
have performed molecular docking analysis of quercetin
and its analogues at the active site of HpDdl. Some of the
screened compounds showed better affinity and interaction
with HpDdl enzyme. The docking analysis and absorption,
distribution, metabolism and toxicity study forward few of
them as plausible lead molecule or a novel class of drugs
with enhanced pharmacological properties.
Keywords Antimicrobial agent � Antibacterial activity �Flavonoids � Molecular docking
Introduction
The elucidation of the link between Helicobacter pylori
(Hp) and gastritis as well as ulceration of the stomach
fetched J. Robin Warren and Barry J. Marshall the Nobel
Prize in Medicine for the year 2005 (Sood, 2006). Since the
discovery of the bacterium, the exact etiopathogenesis is
continuously being updated. A perusal of recent reports
unmasks the association of the bacterium with diverse
extra-digestive morbidity, including insulin resistance
syndrome (Polyzos et al., 2011) and numerous auto-
immune disorders (Hasni et al., 2011) apart from adeno-
carcinoma and gastric lymphoma. Furthermore, the oral
cavity has also been proposed as a reservoir of Hp infection
(Abdel-Monem et al., 2011). The risk intensifies as it has
been hypothesized that the incidence of primary open-
angle glaucoma may increase due to industrialization via
quick transmission of Hp infection (Zavos et al., 2011).
The gamut of enzymes catalyzing the synthesis of the
peptidoglycan framework of the cell wall is a well-
corroborated target for the antibacterial therapy (Wu et al.,
2008a, b). D-alanine:D-alanine ligase (Ddl) synthesizes the
terminal dipeptide, D-Ala:D-Ala, of the peptidoglycan
precursor UDPMurNAc-pentapetide, a crucial building
block involved in peptidoglycan cross-linking. Ala ana-
logues (Neuhaus and Hammes, 1982), transition state
analogues (Ellsworth et al., 1996) and allosteric inhibitor
(Liu et al., 2006) are the reported inhibitors of Ddl.
Recently, HpDdl has been identified as a new target for
quercetin (3,30,40,5,7-pentahydroxyflavone) (Wu et al.,
2008a). It is pertinent to mention that HpDdl keeps two
conservatively substituted residues (Ile16 and Leu241) and
two non-conserved residues (Leu308 and Tyr311) in
the active region (that might partly contribute to the
unique catalytic properties of the enzyme). Furthermore, a
S. P. Singh (&) � B. K. Konwar
Bioinformatics Infrastructure Facility, Department of Molecular
Biology and Biotechnology, Tezpur University,
Tezpur 784028, Assam, India
e-mail: salampradeep@gmail.com
R. Konwarh � N. Karak (&)
Advanced Polymer and Nanomaterial Laboratory,
Department of Chemical Sciences, Tezpur University,
Tezpur 784028, Assam, India
e-mail: nkarak@tezu.ernet.in
123
Med Chem Res
DOI 10.1007/s00044-012-0207-7
MEDICINALCHEMISTRYRESEARCH
310-helix (including residues from Gly306 to Leu312) near
the D-Ala binding region may be involved in D-Ala binding
and conformational change of the enzyme (Wu et al.,
2008b).
Quercetin is a member of the flavonoids, characterized
by a flavone nucleus composed of two benzene rings linked
through a heterocyclic pyrone ring. It mainly occurs in
apples, onions, tea, red wines and berries that show anti-
bacterial action by acting on multiple cellular targets
(Cushnie and Lamb, 2011). Enzymatic assay against
HpDdl established quercetin as the reversible inhibitor,
competitive with one substrate ATP and non-competitive
with the other substrate D-Ala (Wu et al., 2008a). This was
indicative of probable overlap of the binding site of quer-
cetin with the active centre, especially the binding grove of
ATP but not that of D-Ala. However, it showed poorer anti-
Hp activity in comparison to apigenin. This may be due to
greater hydrophilic nature of quercetin in comparison to
apigenin, resulting in its poorer penetration into the bac-
terial cell (Ohemeng et al., 1993). It is pertinent to mention
that flavonoids also target the cytoplasmic membrane
(Mirzoeva et al., 1997) and the hydrophobicity might
facilitate their interactions with the membrane. Further-
more, clinical use of quercetin is limited by its low oral
bioavailability. This necessitates the molecular modifica-
tion of quercetin to enhance its pharmacological properties.
Thus, probing into the binding of quercetin and its
analogues to the HpDdl is an interesting proposition in the
framework of structure-based drug discovery. In this con-
text, in silico ligand–protein docking study could be
instrumental in comprehending and predicting molecular
recognition, explication of the binding modes and pre-
dicting binding affinity (Morris and Lim-Wilby, 2008).
Furthermore, it has been estimated that nearly 50 % of
drugs fail because of unacceptable efficacy, which includes
poor bioavailability as a result of ineffective intestinal
absorption and undesirable metabolic stability (Kennedy,
1997). It has also been estimated that up to 40 % of drug
candidates have failed in the past because of safety issues
(DiMasi, 1995). In view of this aspect, we have also carried
out ADME–toxicity (ADME–Tox) prediction (absorption,
distribution, metabolism and toxicity) of quercetin and its
analogues used in our study to evaluate the properties
crucial to the final clinical success of a drug candidate.
Experimental
Protein preparation
The three-dimensional crystal structure of Ddl from Hp
(Wu et al., 2008a, b) (PDB ID: 2PVP) determined by X-ray
crystallography was retrieved from the Protein Databank
Bank (http://www.rcsb.org/) was imported in the Molegro
Virtual Docker (MVD) (Molegro APS). The coordinates of
the dimeric crystallographic structure of Ddl is complexed
with water molecules. Considering that the monomers are
identical and independent of each other, only monomer A
from the enzyme, we selected to perform our molecular
docking studies. This procedure reduced the computation
time by half. Also, the co-crystallized water molecules
were removed from both the monomers using the software
Molegro Virtual Docker (MVD�) (Molegro APS; Thomsen
and Christensen, 2006).
Compound retrieval and chemical similarity search
The 2D structure of quercetin (CID5280343) was retrieved
from the NCBI PubChem database (Bolton et al., 2008;
Wang et al., 2010). In addition, we performed a chemical
structure search of quercetin in the NCBI PubChem data-
base (Bolton et al., 2008; Wang et al., 2010) to retrieve the
related compound and analogues. The search parameters
were set at 95 % similarity subjected to Lipinski rule of
five filters (Lipinski, 2000; Lipinski et al., 1997).
The 2D structure of quercetin (CID5280343) and the
compounds retrieved from the PubChem compound search
at 95 % similarity (Bolton et al., 2008; Wang et al., 2010)
were converted to their corresponding three-dimensional
structures using the ChemOffice (2010) (CambridgeSoft
Corporation 2010) for our docking purposes. The energy of
these compounds were optimized using MM2 force field
methods (Burkert and Allinger, 1982) and saved as SYBL
mol2 files using ChemOffice (2010) (CambridgeSoft
Corporation 2010).
Computation
For docking purposes, the active site residues (Leu308 and
Tyr311), responsible for the distinctive enzymatic activity
(Wu et al., 2008b) were set as the search space. The active
site residues of search space were set inside a restriction
sphere of radius 13 A (X 6.05, Y -16.68, Z 22.58) using
MVD� (Molegro APS).
Bond flexibility of the compounds was set along with
the side chain flexibility of the protein for search space
(Leu308 and Tyr311), set with a tolerance of 1.10 and
strength of 0.90 for docking simulations. RMSD threshold
for multiple cluster poses was set at 2.00 A (Yadava et al.,
2011). The docking algorithm was set at a maximum
iteration of 1,500 with a simplex evolution size of 50 and a
minimum of 10 runs were performed for each compound.
The best pose of each compound was selected for the
subsequent ligand–protein interaction energy analysis.
Molecular docking was carried out using Molegro Virtual
Docker (Molegro APS). MVD is molecular visualization
Med Chem Res
123
and molecular docking software based on a differential
evolution algorithm; the solution of the algorithm takes into
account the sum of the intermolecular interaction energy
between the ligand and the protein and the intramolecular
interaction energy of the ligand. The docking energy scoring
function is based on the modified piecewise linear potential
(PLP) with new hydrogen bonding and electrostatic terms
included. Full description of the algorithm and its reliability
as compared to other common docking algorithm can be
found in literature (Thomsen and Christensen, 2006).
Enzymatic inhibition and antibacterial assay conducted
by Wu et al. (2008a)
The authors would like to highlight the reported (Wu et al.,
2008a) fact that quercetin exhibited 50 % lower inhibitory
concentration (IC50) (Table 1) and inhibitor binding con-
stant (Ki) values than apigenin against both the HpDdl and
Escherichia coli DdlB. The inhibitor binding constant, Ki
values were found to be 4.3 and 31.0 lM, respectively, for
quercetin and apigenin, competitive for ATP. The two
additional hydroxyls on the flavone skeleton of quercetin in
structure might facilitate its inhibitory activity and binding
affinity to Ddl. However, Wu et al. (2008a) have proposed
that the differential hydrophilicity (and consequently dif-
ference in penetration potency into the bacterial cell wall)
of quercetin and apigenin may be ascribed for the former’s
poorer anti-Hp activity in comparison to the moderate MIC
(25 lg mL-1) of the latter (Table 2). These necessitate the
molecular modification of quercetin to enhance its phar-
macological properties. Thus, these inhibition study data
dictated our decision to restrict the screening of quercetin
alone and the analogues.
ADME–Tox prediction
In addition, ADME–Tox predictions for the top docking
hits were calculated using ACD/I-Lab 2.0 (Advanced
Chemistry Development, Inc.). ACD/I-Lab 2.0 is a web-
based service that provides instant access to spectral and
chemical databases, and predicts properties including
physicochemical, ADME, toxicity characteristics. The
LD50 mouse (intraperitoneal, oral, intravenous, subcuta-
neous) and probability of health effect of blood, cardio-
vascular system, gastrointestinal system, kidney, liver and
lung were predicted for the top docking hits and compar-
ative analysis were performed.
Results and discussion
Quercetin (3,30,40,5,7-pentahydroxyflavone) is an ubiqui-
tously distributed polyphenolic flavonoid of the plant
kingdom, present as a secondary metabolite (Murakami
et al., 2008). It is pertinent to mention that flavonoids
contain a basic skeleton of diphenylpropane (C6–C3–C6).
Quercetin is commonly found as O-glycosides in which at
least one hydroxyl group is substituted by various types of
sugars. Hydroxyl groups at the 30 and 40-positions in the
B-ring (the so-called catechol group) and the 3-position in
the C-ring are responsible for a number of bioactivities
(including the free radical scavenging of DPPH) of quer-
cetin. In this report, we have highlighted the inhibition of
HpDdl by quercetin and its analogues.
Molecular docking was carried out and the top poses
docked at the active site region of the protein are shown in
Table 3. In post-docking analysis, it is observed that
compounds ID 9818879, 6477685, 21633676, 25202270,
6477683, 25202413 and 10636768 have higher rerank
score (in terms of negative energy) than quercetin and
compound ID 10359384, 44258703 have lesser rerank
score than quercetin as shown in Table 3. The rerank score
is a linear combination of E-inter (steric, van der Waals,
hydrogen bonding, electrostatic) between the ligand and
the protein, and E-intra (torsion, sp2–sp2, hydrogen bond-
ing, van der Waals, electrostatic) of the ligand weighted by
pre-defined coefficients (Thomsen and Christensen, 2006).
We have also carried out a detailed analysis of the top
poses in terms of ligand–protein interaction energy. The
ligand–protein interaction energy analysis (both electro-
static and H-bond energy) was calculated in order to get a
better understanding of the variations between the binding
mode of each compound and the molecular factors
responsible for the activity.
Table 1 Enzymatic inhibition of quercetin against Helicobacterpylori D-alanine:D-alanine ligase (HpDdl) and Escherichia coli DdlB
(EcDdlB) (Wu et al., 2008a)
Enzyme Inhibitor IC50 (lM)
HpDdl Quercetin 48.5 ± 4.3
Apigenin 132.7 ± 14.4
EcDdlB Quercetin 19.9 ± 1.8
Apigenin 163.0 ± 26.2
Table 2 Minimum inhibitory concentrations (MICs) of quercetin
against H. pylori and E. coli (Wu et al., 2008a)
Compound MIC (lg mL-1)
H. pylori strain E. coli strain
SS1 43504 JM109 25922
Quercetin 200 100 300 300
Apigenin 25 25 200 [200
Med Chem Res
123
Table 3 Molecular docking poses score for the top ten docking hits
S. no. Compound ID Residue Interaction
energy
(kcal mol-1)a
Interaction
dist. (A)
H-bond
energy
(kcal mol-1)b
MolDock
scorecRerank
scored
1 9818879
O
O
O
O
O
O
O
H
H
H
H
H
H
H
H
H
H
H
H
H
H
Arg286 -2.42 2.82 -15.65 -100.64 -82.13
Arg286 -1.87 3.09
His96 -0.59 2.89
Glu101 -2.5 2.66
Ile304 -1.52 2.48
Ser307 -2.5 2.69
Gly306 -2.5 2.63
Tyr311 -0.37 2.34
2 6477685
O
O O
O
O
O
H
H
H
HH
HH
H
H H
H
H Glu13 -2.5 3.1 -21.93 -92.79 -77.86
Glu101 -2.5 3.01
Ser177 -0.48 3.45
Glu233 -1.18 3.19
Glu233 -2.5 3.1
Glu233 -2.49 3.1
Arg286 -1.46 3.13
Arg286 -2.5 2.6
Ile304 -1.32 2.99
Tyr311 -2.5 2.83
Tyr311 -2.5 3.03
3 21633676
O
O
O
O
O
O
O
H
HH
HH
H
H
H
H
H
HH Gly306 -1.10 3.24 -13.19 -78.9 -75.25
Ser177 -1.03 3.39
Asn302 -2.5 2.63
Ser177 -0.62 3.43
Glu101 -2.5 2.84
His96 -1.28 2.79
Glu101 -2.5 2.73
Ile304 0.08 2.29
Tyr311 -2.31 3.14
4 25202270
O
O
OO
O
O
-O
H
H
H
H
H
HH
H
H
H
H Arg286 -2.5 2.85 -10.09 -83.32 -72.68
Gly306 -1.35 3.30
Glu233 -2.5 2.69
Tyr311 -2.5 3.06
Glu101 -2.5 2.95
Ile304 -1.25 2.45
Med Chem Res
123
Table 3 continued
S. no. Compound ID Residue Interaction
energy
(kcal mol-1)a
Interaction
dist. (A)
H-bond
energy
(kcal mol-1)b
MolDock
scorecRerank
scored
5 6477683
O
O
O
O
O
H
H
H
H
H
H
HH
H
H
H
H Arg286 -2.07 3.1 -18.67 -83.18 -72.39
Arg286 -2.47 2.93
Glu13 -2.5 3.1
His96 -0.6 3.48
Glu233 -1.04 3.14
Glu233 -2.5 3.01
Tyr311 -2.5 2.82
Glu233 -2.5 3.1
Tyr311 -2.5 2.71
6 25202413
O
O
O
O
-O
O
O
H
H
H
H
H
H
H
H
H
HH Gly306 -0.83 3.26 -9.66 -77.19 -70.35
Ser177 -1.48 3.3
Asn302 -2.5 2.62
Tyr311 -0.83 2.4
Ser177 -0.35 3.48
His96 -1.37 3.06
Glu101 -2.49 2.6
Ile304 -1.25 2.45
7 10636768O
O
O O
O
HH
H
H
H
H
H
H
H
H
H
H
Arg286 -0.31 3.05 -8.32 -82.89 -70.27
Tyr311 -2.5 2.61
Asn302 0.46 2.25
Ser307 -1.89 3.22
Glu101 -1.21 3.18
Glu101 -2.45 3.1
Asn302 -0.39 3.52
8 Quercetin
O
O
OO
O
O
OH
H
H
H
H
H
H
H
H
H
Arg286 -0.59 2.85 -12.11 -78.31 -70.1
Arg286 -0.82 2.75
Asn302 -1.82 2.76
Arg286 -2.41 2.81
Arg286 -1.82 3.11
His96 -0.55 2.95
Glu101 -2.5 2.62
Ile304 -1.6 2.49
9 10359384
O
O
O
O
O
O O
O
HH
H
H
H
H
H
H
H
H
HH Arg286 -0.94 3.41 -14.88 -75.13 -69.23
Gly306 -1.3 3.3
Ser177 -1.02 3.4
Asn302 -1.62 2.5
Tyr311 -2.41 3.12
Glu233 -2.5 2.7
Tyr311 4.79 1.75
His96 -1.11 2.95
Glu101 -2.49 3.10
Ile304 0.29 2.27
Med Chem Res
123
Table 3 also enlists the ligand–protein interaction
energy calculation including the residues present, their
interaction distances and interaction energy. The top poses
were found to be docked into the active site of the search
space exhibiting both bonded and non-bonded interaction.
The top three docking hits CID9818879, CID6477685 and
CID21633676 having higher rerank score showed common
molecular interaction with Glu101, Ile304, Tyr311 resi-
dues. Moreover, the interaction with the non-conserved
residues in the active site residue Tyr311 was found to be
dominating in the top ten docking hits. Here only the
docking poses have been shown for the top three com-
pounds (CID9818879, CID6477685 and CID21633676)
and quercetin in Figs. 1a–c, 2a–c, 3a–c and 4a–c. The
snapshots show the variation in the conformational modes
of the molecules under consideration. The authors would
like to highlight a pertinent issue at this juncture. Natural
products like quercetin are capable of binding to multiple
targets primarily due to their mode of generation as pro-
posed by Ji et al. (2009). During the eventual steps in the
biosynthesis of the quercetin molecule, different syntheta-
ses with diverse architecture and molecule binding cavities
are involved. The differential target binding modes of the
quercetin molecule seems to be preconditioned by the
varied interactions with the enzyme moieties during the
former’s biosynthesis. The inhibition of phosophatidyli-
nositol-3-kinase (with kinase-like fold), 3-hydroxyisobu-
tyryl-CoA hydrolase (with ClpP/crotonase fold) and helix-
turn-helix type transcriptional regulator (with tetracycline
repressor-like fold) by quercetin marshals in support of this
fact. Core structure of quercetin seems to inherit a spec-
trum of binding groups and certain level of flexibility that
facilitates its interaction with a plethora of unintended
proteins with similar ligand binding cavities.
The binding of these compounds at the active site region
reveals the binding affinity of these compounds is better
than quercetin. Furthermore, we have calculated the cor-
relation between molecular weight and the hydrogen
bonding energy for the top docking hits resulting in a very
low correlation value of -0.23 indicating that the predicted
hydrogen bonding energy was mainly due to specificity and
not due to molecular size.
It is pertinent to mention that in vitro and in vivo experi-
ments often lay the foundation stone and complements/
confirms computational and docking analyses. In this milieu,
as noted previously, Wu et al. (2008b) have kinetically and
structurally characterized the Ddl of Hp strain SS1 (HpDdl).
The documented apparent Km of ATP (0.87 lM), Km1
(1.89 mM) and Km2 of D-Ala (627 mM), and kcat
(115 min-1) at pH 8.0 vouched for its relatively weak
binding affinity and poor catalytic activity against the sub-
strate D-Ala in vitro. Although, HpDdl from Hp has been
shown to exhibit low catalytic efficiency in vitro, it still
retained its function as Ddl in vivo. By kinetic character-
ization of several mutants in the active region and crystal
structure analysis of the enzyme, the structurally unique 310-
helix has been envisaged for the unusual low activity. Con-
sidering the inhibitory action of quercetin, it has been
Table 3 continued
S. no. Compound ID Residue Interaction
energy
(kcal mol-1)a
Interaction
dist. (A)
H-bond
energy
(kcal mol-1)b
MolDock
scorecRerank
scored
10 44258703
O
O
O
O
O
OO
H
H
H
H
H
H
H
H
HH
H
HArg286 -2.5 2.65 -15.8 -74.47 -64.94
Arg286 -1.65 3.1
Arg286 -0.17 3.1
Tyr311 -1.99 3.2
Ser177 -0.77 2.52
Glu233 -2.5 2.9
Glu101 -2.5 3.1
Asn302 -0.81 2.98
His96 -1.34 2.83
Glu101 -2.49 2.62
Ile304 -0.33 2.34
a The total interaction energy between the pose and the proteinb Hydrogen bonding energyc MolDock score is derived from the PLP scoring functions with a new hydrogen bonding term and new charge schemes (Thomsen and
Christensen, 2006)d The rerank score is a linear combination of E-inter (steric, van der Waals, hydrogen bonding, electrostatic) between the ligand and the protein,
and E-intra (torsion, sp2–sp2, hydrogen bonding, van der Waals, electrostatic) of the ligand weighted by pre-defined coefficients. (Thomsen and
Christensen, 2006)
Med Chem Res
123
Fig. 1 a Predicted bonded interactions (green dashed lines) between
CID 9818879 (green) and His96, Glu101, Arg286, Ile304, Gly306,
Ser307 and Tyr311 residues of HpDdl enzyme and b predicted non-
bonded electrostatic interaction between CID 9818879 and the
residues at the active site. c Binding mode of CID 9818879 (green)
to HpDdl (Color figure online)
Fig. 2 a Predicted bonded interactions (green dashed lines) between
CID6477685 (green) and Glu13, Glu101, Ser177, Glu233, Arg286,
Ile304 and Tyr311 residues of HpDdl enzyme and b predicted non-
bonded electrostatic interaction between CID6477685 and the resi-
dues at the active site. c Binding mode of CID6477685 (green) to
HpDdl (Color figure online)
Med Chem Res
123
Fig. 3 a Predicted bonded interactions (green dashed lines) between
CID 21633676 (green) and His96, Glu101 Ser177, Asn302, Ile304,
Gly306 and Tyr311 residues of HpDdl enzyme and b predicted non-
bonded electrostatic interaction between CID 21633676 and the
residues at the active site. c Binding mode of CID 21633676 (green)
to HpDdl (Color figure online)
Fig. 4 a Predicted bonded interactions (green dashed lines) between
quercetin (green) and His96, Glu101, Arg286, Asn302 and Ile304
residues of HpDdl enzyme and b predicted non-bonded electrostatic
interaction between quercetin and the residues at the active site.
c Binding mode of quercetin (green) to HpDdl (Color figure online)
Med Chem Res
123
reported that quercetin is a reversible inhibitor that is com-
petitive with the substrate ATP of Hpdl (Wu et al., 2008a).
Using D-cycloserine (DCS) as a positive inhibitor, the
inhibitor binding constant, Ki of ATP has been calculated (by
double reciprocal and secondary plots) to be 4.3 lM, while
IC50 of quercetin has been found to be 48.5 ± 4.3 lM.
Furthermore, surface plasmon resonance biosensor tech-
nology has been suitably used to determine the in vitro
binding affinity, KD (12.9 lM) of quercetin to HpDdl. The
afore-stated reports highlight the mode of binding of quer-
cetin to HpDdl to be competitive with respect to ATP. Thus,
we have restricted our present docking study under the pre-
text of available experimental data on the competitive mode
of inhibition of HpDdl by quercetin. The authors would,
however, like to mention at this juncture that the molecular
docking simulation work on the competitive inhibition of
ATP by quercetin and apigenin is under progress and shall be
reported in the subsequent communication.
Table 4 represents the Lipinski rule of five parameters
for the top docking hits and all the compounds used in our
study does not violate the rule of five to be an orally active
compound. Lipinski et al. (1997, 2000) described ‘rule of
five’ which impose limitation on the logP (the logarithm of
octanol/water partition coefficient), molecular weight and
the number of hydrogen bond acceptors and donors. The
rule states that most ‘drug-like’ molecules have logP \ 5,
molecular weight \500, number of hydrogen bond accep-
tors \10 and number of hydrogen bond donors \5. Mol-
ecules violating more than one of these rules may have
problems with bioavailability. From Table 4, it is also
observed that the hydrogen bond acceptors of the
CID9818879, CID6477685, CID21633676, CID25202270,
CID6477683, CID25202413 and CID10636768 are more
than that of quercetin ranging from 5 to 8 hydrogen bond
acceptors which could be an important factor because of
the hydrophobic nature of these compounds and hence
showing better affinity and interaction with HpDdl enzyme
as compared to quercetin. Moreover, it is also observed that
these seven compounds have lower topological surface
area (TPSA) values than quercetin suggesting that these
compounds have better oral bioavailability than quercetin
(the oral bioavailability is inversely proportional to topo-
logical polar surface area) (Freitas, 2006).
More specific analysis of pharmacological parameters
was inspected from ADME–Tox evaluation which is shown
in Table 5. Table 5 depicts some specific parameters related
to absorption, distribution, metabolism, excretion and tox-
icity for the top ten docking hits. In general, all compounds
presented advantages and disadvantages when compared to
each other. However, CID6477683 showed better bioavail-
ability (\70) as compared to quercetin and other compounds.
This compound could be a lead molecule.
We have also carried out a comparative analysis on
LD50 mouse and the probability of health effects which is
shown in Figs. 5 and 6. From Fig. 5, it is observed that
LD50 mouse (oral) of CID9818879, CID44258703,
CID10636768 are higher as compared to quercetin, while
CID6477685, CID21633676, CID25202270, CID6477683,
CID25202413, CID10636768, CID44258703 showed more
or less similar behaviour of LD50 mouse (intraperitoneal,
oral, intravenous, subcutaneous) with quercetin. Further-
more, the comparative analysis on health effects from
Fig. 6 reveals CID10359384 chances of health effect on
blood, gastrointestinal system and lung, CID6477685
chances of health effect on blood, cardiovascular and
kidney and CID6477683 chances of health effect on blood,
cardiovascular and liver. Interestingly, CID44258703,
CID25202413, CID10636768, CID9818879, CID25202270
and CID21633676 show better health effects in compari-
sons to quercetin.
These compounds which showed better health effect
could be a lead molecule or a novel class with enhanced
pharmacological properties. In addition, the analogues
showed higher absorption rate in comparison to quercetin
but with similar distribution rate with quercetin which is
shown in Table 5.
Conclusion
The present molecular docking simulation studies on the
analogues of quercetin (forming favourable interaction
with the active site residues) vouched for their inhibitory
action against HpDdl. A total of seven analogues of quer-
cetin showed favourable interaction better than quercetin,
exhibiting common molecular interaction with Glu101,
Ile304, Tyr311 residues of HpDdl. Furthermore, the com-
pounds used in this study do not violate the Lipinski rule of
five to qualify as orally active drugs. These analogues have
lower TPSA values than quercetin suggesting that the
former compounds have better oral bioavailability than the
Table 4 Lipinski rule of five filter including TPSA for the top poses
used in our study
S. no. Compound ID HBA HBD Mol wt xlogP3 Rot B TPSA
1 9818879 7 4 330.29 1.6 4 116
2 6477685 6 4 312.27 2 2 107
3 21633676 7 4 316.26 1.9 2 116
4 25202270 7 3 315.25 3.1 2 119
5 6477683 5 3 296.27 3.3 2 87
6 25202413 7 3 315.25 3.1 2 119
7 10636768 5 3 284.26 3.2 2 87
8 5280343 (quercetin) 1 5 302.23 1.5 1 127
9 10359384 8 5 332.26 1.5 2 137
10 44258703 7 4 316.26 1.9 2 116
Med Chem Res
123
Ta
ble
5A
DM
E–
To
xp
red
icti
on
par
amet
ers
for
the
top
po
ses
calc
ula
ted
for
the
top
ten
do
ckin
gh
its
AD
ME
–T
ox
98
18
87
96
47
76
85
21
63
36
76
25
20
22
70
64
77
68
32
52
02
41
31
06
36
76
8Q
uer
ceti
n1
03
59
38
44
42
58
70
3
So
lub
ilit
ya
H2O
(mg
mL
-1)
0.7
60
.05
60
.03
50
.05
80
.03
50
.05
80
.11
0.1
40
.10
0.2
8
Lo
gD
bp
H1
.7(s
tom
ach
)1
.55
2.4
71
.87
2.1
53
.12
2.0
22
.57
1.8
21
.91
.51
Lo
gD
bp
H4
.6(d
uo
den
um
)1
.56
2.4
81
.87
2.1
53
.13
2.0
22
.58
1.8
21
.91
.5
Lo
gD
bp
H6
.5(j
eju
nu
m,
ileu
m)
1.5
42
.46
1.8
52
.13
3.1
32
2.5
71
.79
1.8
71
.43
Lo
gD
bp
H7
.4(b
loo
d)
1.4
32
.34
1.7
32
3.1
31
.85
2.5
1.6
31
.71
1.0
7
Lo
gD
bp
H8
.0(c
olo
n)
1.1
62
.07
1.4
71
.68
3.1
21
.51
2.3
11
.25
1.3
50
.48
%o
ral
bio
avai
lab
ilit
yc
[3
0[
30
[3
0[
30
\7
0[
30
[3
0[
30
[3
0[
30
Ab
sorp
tio
nd
(cm
s-1)
4.3
69
10
-4
6.8
79
10
-4
5.4
91
0-
46
.18
91
0-
48
.19
10
-4
5.8
29
10
-4
7.9
29
10
-4
3.3
79
10
-4
3.5
89
10
-4
3.9
79
10
-4
Dis
trib
uti
on
e(L
kg
-1)e
0.6
80
.62
0.6
20
.63
1.6
20
.60
.81
0.6
0.5
90
.59
LD
50f m
ouse
(mg
kg
-1,
intr
aper
ito
nea
l)2
70
42
01
50
40
03
20
57
01
40
45
08
60
16
0
LD
50f m
ouse
(mg
kg
-1,
ora
l)2
00
08
00
85
06
40
63
05
50
16
00
67
06
10
17
00
LD
50f m
ouse
(mg
kg
-1,
intr
aven
ou
s)3
10
19
01
10
29
04
42
90
14
03
50
30
03
20
LD
50f m
ouse
(mg
kg
-1,
sub
cuta
neo
us)
39
01
80
12
01
30
10
01
20
24
01
60
99
16
0
Am
este
stg
0.8
80
.66
0.9
40
.80
.55
0.7
80
.66
0.8
50
.82
0.8
7
Pro
b.
of
blo
od
effe
cth
0.2
90
.76
0.5
20
.79
0.8
0.3
10
.44
0.3
40
.84
0.4
3
Pro
b.
of
card
iov
ascu
lar
syst
emef
fect
h0
.73
0.6
70
.69
0.6
90
.76
0.6
90
.78
0.8
0.6
70
.69
Pro
b.
of
gas
tro
inte
stin
alsy
stem
effe
cth
0.6
20
.46
0.6
40
.64
0.4
20
.64
0.4
50
.72
0.9
80
.68
Pro
b.
of
kid
ney
effe
cth
0.7
80
.87
0.5
50
.77
0.7
10
.77
0.8
20
.79
0.7
60
.55
Pro
b.
of
liv
eref
fect
h0
.27
0.7
80
.30
.35
0.8
40
.35
0.3
50
.30
.35
0.2
9
Pro
b.
of
lun
gef
fect
h0
.41
0.3
80
.37
0.3
70
.42
0.3
70
.36
0.3
80
.76
0.3
7
aC
alcu
late
sco
mp
ou
nd
’sso
lub
ilit
yin
ab
uff
erat
asp
ecifi
edp
Hv
alu
eb
Cal
cula
tes
log
arit
hm
of
the
app
aren
to
ctan
ol–
wat
erp
arti
tio
nco
effi
cien
tD
atv
ario
us
pH
cE
stim
ates
the
pro
bab
ilit
yo
fa
com
po
un
ds
bio
avai
lab
ilit
yb
ein
gab
ov
e3
0an
d7
0%
dE
stim
ates
hu
man
jeju
nu
mp
erm
eab
ilit
ye
Cal
cula
tes
the
app
aren
tv
olu
me
of
dis
trib
uti
on
for
aco
mp
ou
nd
inL
kg
-1
fE
stim
ates
LD
50
val
ue
inm
gk
g-
1af
ter
intr
aper
ito
nea
l,o
ral,
intr
aven
ou
san
dsu
bcu
tan
eou
sad
min
istr
atio
nto
mic
eg
Tes
tfo
ras
sess
ing
mu
tag
enic
pro
per
ties
of
the
com
po
un
ds
hE
stim
ates
pro
bab
ilit
yo
fb
loo
d,
gas
tro
inte
stin
alsy
stem
,k
idn
ey,
liv
eran
dlu
ng
effe
ctat
ther
apeu
tic
do
sera
ng
e
Med Chem Res
123
latter. Moreover, the ADME–Tox evaluation reveals one of
the analogue (CID64776834) to have better bioavailabil-
ity (\70) than the rest of the docked compounds used
in the study. In addition, CID44258703, CID25202413,
CID10636768, CID 9818879, CID25202270 and
CID21633676 showed better health effects and higher
absorption rate in comparisons to quercetin. These could be
possibly exploited as lead molecules or a novel class with
enhanced pharmacological properties. Though experimen-
tal studies are required to mark it as a lead compound for
the development of novel inhibitor, we conclude that
molecular docking studies of quercetin and its analogues at
active site residue of DdL enzyme of Hp would aid and
support in experimental testing of the compound as quer-
cetin is limited by its low oral bioavailability and low MIC.
Acknowledgments The authors thank Department of Biotechnol-
ogy (DBT), Government of India for providing Bioinformatics
Infrastructure Facility and Vice-Chancellor, Tezpur University for
support in carrying out this research work. Rocktotpal Konwarh also
acknowledges the receipt of his Senior Research Fellowship from
DBT, New Delhi.
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