Available online at http://www.urpjournals.com
International Journal of Agricultural and Food Science
Universal Research Publications. All rights reserved
ISSN 2249-8516
Original Article
Ajocin (Allicin+Ajoene) can inhibit the enzymatic activity of aflatoxin biosynthesis
in peanuts and prevent human carcinogenic exposure
Archana Prabahar1‡, Srividhya Vellingiri1, Sathishkumar Natarajan1†, Kalpana Raja1‡, Renganathan
Gandhimeyyan1, Bharathi Nathan*1
Email: [email protected], Telephone 91 - 9486775001
1
Department of Plant Molecular Biology and Biotechnology, Centre for Plant Molecular Biology, Tamil Nadu Agricultural
University, Coimbatore -641003, Tamil Nadu, INDIA
†
Kyung-Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 449-701, Republic of Korea.
‡
Data Mining and Text Mining Laboratory, Department of Bioinformatics, School of Life Sciences, Bharathiar University,
Coimbatore -641046, Tamilnadu, INDIA.
Archana Prabahar: [email protected], Srividhya Vellingiri: [email protected]
Sathishkumar Natarajan: [email protected], Kalpana Raja: [email protected]
Renganathan Gandhimeyyan: [email protected], Bharathi Nathan: [email protected]
Received 13 July 2012; accepted 30 July 2012
Abstract
Aflatoxin, a naturally occurring mycotoxin, produced by Aspergillus parasiticus infects peanuts and many other legumes.
Among 23 enzymatic reactions involved in aflatoxin biosynthesis, only 15 are identified so far. Versicolorin B synthase
(vbs) is the key enzyme involved in aflatoxin biosynthesis. It converts hydroxy versiconal acetate (VHA) to versicolorin B
(VERB), which is finally converted into aflatoxin in a series of reactions. We selected two naturally available compounds,
allicin and ajoene present in onion and garlic, to analyse their inhibitory effect on aflatoxin biosynthesis. Additionally, we
virtually derived a new compound called ajocin (allicin + ajoene) to study their synergistic effect in preventing aflatoxin
biosynthesis. We aim to compare the inhibitory action of these compounds against 15 known enzyme targets through
docking simulations using Autodock. The best inhibitory action is reported by ajocin against the key enzyme, vbs. In silico
docking studies thus confirm the enhanced inhibitory property of ajocin and reduce the risk of aflatoxin (carcinogen)
exposure to human.
© 2011 Universal Research Publications. All rights reserved
Keywords: Aflatoxin, Allicin, Ajoene, Mycotoxins, Peanuts, Contamination.
1.1 INTRODUCTION
The filamentous fungi A. flavus and A. parasiticus
belonging to genus Aspergillus, produce various secondary
metabolites such as aflatoxins and many other mycotoxins
by infecting the agricultural crops and stored grains [1].
Aflatoxins are the most toxic, potent carcinogenic
compounds causing aflatoxicosis and cancer in mammals.
These naturally occurring teratogenic compounds
contaminate many crops including peanuts, corn, cotton
and treenuts [2]. Aflatoxins toxicity on farm animals fed
with contaminated peanut meals had reported to numerous
death due to acute liver necrosis. In human, hepatitis B
infection and liver cancer are common in many parts of the
world where aflatoxin exposure is high [3]. The four major
aflatoxins are B1, B2, G1 and G2. Other significant
members include the two oxidative forms of aflatoxin B1
namely M1 and M2. All these polyketide-derived
81
furanocoumarin compounds are highly toxic to agricultural
crops, farm animals and human [4]. Aflatoxin
contamination in food may cause neural tube defects and
many primary cancers. Aflatoxin B1 in food and human
dietary exposure may contribute to the aetiology of human
chronic diseases. [5-6]
One of the active compounds of freshly crushed garlic
homogenates, Allicin is found to exhibit strong antifungal
effect. Other antimicrobial activities of allicin include
antibacterial, antiparasitic and antiviral effects. Allicin is
assumed to be the main component responsible for the
inhibition of aflatoxin formation [7]. Though the compound
is found in a number of onion types, the extract from garlic
shows comparatively greater antifungal activity [8]. Allicin
can be isolated from garlic cloves through steam
distillation. Pure allicin is a volatile molecule and poorly
miscible in aqueous solutions [9]. Synthetically, allicin can
International Journal of Agriculture and Food Science 2012, 2(3): 81-89
be obtained by mild oxidation of diallyl disulfide [10].
Another semi-synthetic method based on the supercritical
carbon dioxide extraction (SC-CO2) of onions and their
quantitative analysis by gas chromatography-mass
spectrometry evidenced the presence of 28 sulfur
compounds, including allicin [11]. A second garlic-derived
sulfur-containing compound, ajoene is reported to exhibit
antifungal activity towards A. niger. Ajoene is a derivative
of allicin that can be isolated from the methanolic extract of
garlic homogenate. Other antibiotic activities reported by
ajoene include antiviral and antiprotozoal activities [12].
Allicin and ajoene are reported to possess antifungal
activity. We virtually derived a third compound called
ajocin by combining the structural features of allicin and
ajoene to analyze the synergistic effect. Docking simulation
is the effective way to predict the potency and efficacy of
the three selected compounds in the inhibition of aflatoxin
synthesis.
Figure 1: 3D Structure of alicin
1.2. Materials and Methods
1.2.1. Enzymes involved in Aflatoxin Biosynthesis
Biosynthesis of aflatoxin is a multistep process starting
from the two substrates namely, acetyl CoA and malonyl
CoA [13-15]. Figure 1 depicts the steps involved in this
biosynthesis. Protein sequences of the fifteen enzymes
involved in aflatoxin biosynthesis were retrieved from
Swissprot (www.expasy.org) [16]. Templates for these
protein sequences were identified by performing BLAST
[17-19]. Based on the score, e-value and percentage
identity, best templates were identified for every individual
sequence. Structures of these enzymes were retrieved from
ModBase
(http://modbase.compbio.ucsf.edu/modbasecgi/index.cgi), a database of comparative protein structure
models [20].
Figure 3: 3D Structure of ajocin
by binding hydrophobic (CH3) probes to the protein, and
finds clusters of probes with the most favorable binding
energy. These clusters are placed in rank order of the
likelihood of being a binding site according to the sum total
binding energies for each cluster.
Figure 4: Representation of the docked model of vbs with
allicin
1.2.4. Selection of inhibitors for the aflatoxin
biosynthetic pathway enzymes
Inhibitors such as Allicin and Ajoene were selected for the
docking study. 2D and 3D structures of these inhibitors
were retrieved from Pubchem [22] compound database
(http://pubchem.ncbi.nlm.nih.gov/). Synergistic activity of
allicin and ajoene together may produce better activity and
enhance the inhibitory action towards aflatoxin
biosynthesis. In order to test this experimentally, we
combined the two molecules using Chemsketch and
derived an inhibitor, ajocin. Three dimensional structures
of allicin, ajoene and ajocin were shown in figure 1, 2 and 3
respectively. This inhibitor was designed based on the
literature survey of zimmu extract prepared by the
combination of allicin and ajoene compounds from onion
and garlic [15]. Since these compounds exhibited more
antifungal activity against aflatoxin species, we made an
Figure 2: 3D Structure of ajoene
attempt to test the ajocin compound to inhibit aflatoxin
1.2.2. Model Assessment and Validation
biosynthesis in peanut plants.
Models thus generated were subjected to model assessment 1.2.5.Physiochemical Properties and Drug likeness
using SAVS server (http://nihserver.mbi.ucla.edu/ SAVES /). prediction
Ramachandran Plot [21] generated from SAVS software Drug likeness may be defined as a complex balance of
was analyzed and the quality of the model was then various molecular properties and structure features which
checked.
determine whether particular molecule is similar to the
1.2.3. Active Site Prediction
known drugs. These properties mainly focus on
Q-SiteFinder (http://www.modelling.leeds.ac.uk/qsitefinder/) hydrophobicity, electronic distribution, hydrogen bonding
is a new method of ligand binding site prediction. It works
characteristics, molecule size, flexibility and presence of
International Journal of Agriculture and Food Science 2012, 2(3): 81-89
82
various pharmacophoric features which in turn influence
the behavior of molecule in a living organism, including
bioavailability, transport properties, affinity to proteins,
reactivity, toxicity, metabolic stability and many others.
1.2.6.Lipinski Rule (Rule of Five)
Lipinski rule is a rule of thumb to evaluate drug likeness, or
determine if a chemical compound with a certain
pharmacological or biological activity has properties that
would make it a likely orally active drug in humans.
Lipinski's rule says that, in general, an orally active drug
has no more than one violation of the following criteria:
 Not more than 5 hydrogen bond donors (nitrogen or
oxygen atoms with one or more hydrogen atoms)
 Not more than 10 hydrogen bond acceptors (nitrogen or
oxygen atoms)
 A molecular weight under 500 daltons
 An octanol-water partition coefficient log P of less than 5
The rule describes molecular properties important for a
drug's pharmacokinetics in the human body, including their
absorption, distribution, metabolism, and excretion
("ADME"). However, the rule does not predict whether the
compound is pharmacologically active [23-24].
Figure 5: Representation of the docked model of vbs with
ajoene.
Table 1: Enzyme involved in aflatoxin biosynthesis with its sequence alignment and structural quality assessment.
Template ID
Ramachandran Plot (Percentage of
Enzyme Name Swissprot ID
Percentage Identity
allowed regions)
adhA
P87017
1vl8
24.00%
85.5%
avfA
Q5VDD9
1xq6
16.00%
79.6%
avnA
Q12732
1nr6
19.00 %
88.8%
estA
Q5VD97
1ea5
14.00 %
81.2%
fas_2
Q00681
1e5m
25.00%
84.8%
norA
Q00258
1lqa
28.00%
89.7%
norB
Q5VDD0
1pz1
25.00%
89.3%
omtA
Q12120
1fp2
23.00 %
86.2%
omtB
Q5VDD8
1fp1
21.00 %
90.3%
ordA
Q5VDD6
1pq2
20.00 %
88.2%
pksA
Q5VDF2
1e5m
23.00%
84.1%
vbs
Q5VDD5
1cf3
30.00%
87.2%
ver-1
Q8J2U6
1ja9
50.00 %
91.8%
verA
Q5VDE2
1tqn
20.00%
87.8%
verB
Q5VDE0
1tqn
19.00%
86.9%
1.2.7. Bioactivity
Pharmacological or biological activity is an expression
describing the beneficial or adverse effects of a drug on
living matter. Activity depends critically on fulfillment of
the ADME criteria. Once a model is build, the bioactivity
of screened molecules may be then calculated as a sum of
activity contributions of fragments in these molecules. This
provides a molecule activity score (a number, typically
between -3 and 3). Molecules with the highest activity
score have the highest probability to be active. Lipinski rule
and Bioactivity of the small molecules were prescreened
83
using Molinspiration Tool. (http://www.molinspiration.com/
cgi-bin/properties)
1.2.8. ADME Screening
ADMET refers to the absorption, distribution, metabolism,
excretion, and toxicity properties of a molecule within an
organism. Optimizing these properties during early drug
discovery is crucial for reducing ADMET problems later in
the development process. One of the most daunting
hurdles a drug candidate must pass is having favorable
ADMET characteristics. Such early identification helps to
make research process more efficient and cost-effective by
International Journal of Agriculture and Food Science 2012, 2(3): 81-89
Figure 6: Representation of the docked model of vbs with
ajocin.
allowing us to eliminate compounds with unfavorable
ADMET characteristics. ADMET Descriptors in Discovery
Studio include models for intestinal absorption, aqueous
solubility, blood brain barrier penetration, plasma protein
binding, cytochrome P450 2D6 inhibition and
hepatotoxicity. ADMET screening is performed using
Discovery Studio TOMKAT.
1.2.9.Docking Studies
Docking studies were performed using Autodock 4.0. The
Graphical User Interface program "Auto-Dock Tools" was
used to prepare, run, and analyze the docking simulations.
Kollman united atom charges, solvation parameters and
polar hydrogens were added into the receptor PDB file for
the preparation of protein in docking simulation. AutoDock
[25-27] requires pre-calculated grid maps, one for each
atom type present in the flexible molecules being docked
and it stores the potential energy arising from the
interaction with rigid macromolecules. This grid must
surround the region of interest in the rigid macromolecule.
The grid box size was set at 60, 60 and 60 A° (x, y, and z)
to include all the amino acid residues that present in rigid
macromolecules. AutoGrid 4.0 Program, supplied with
AutoDock 4.0 was used to produce grid maps. The spacing
between grid points was 0.375 angstroms. The Lamarckian
Genetic Algorithm (LGA) [28] 23 was chosen search for
the best conformers. During the docking process, a
maximum of 10 conformers was considered. The
population size was set to 150 and the individuals were
initialized randomly. Maximum number of energy
evaluation was set to 500000, maximum number of
generations 1000, maximum number of top individual that
automatically survived set to 1, mutation rate of 0.02,
crossover rate of 0.8, Step sizes were 0.2 Å for translations,
5.0° for quaternions and 5.0° for torsions. Cluster tolerance
84
0.5 Å, external grid energy 1000.0, max initial energy 0.0,
max number of retries 10000 and 10 LGA runs were
performed. All the AutoDock docking runs were performed
in Intel(R) Xeon(R) CPU 5150 @ 2.66GHz, 2GB RAM in
Apple system. Autodock results were analyzed to study the
interactions and the binding energy of the docked structure.
1.3. Results and Discussion
1.3.1. Preparation of Receptor and Ligand molecules
Fifteen enzyme sequences involved in aflatoxin
biosynthetic pathway were retrieved from Swissprot.
BLASTP search was performed against PDB [29] database
in order to identify suitable templates with known
structures. Templates identified were listed in Table 1 with
their respective percentage identity. Models were retrieved
for the target proteins from the template structures using
Modbase [30-32]. These models were further refined by
performing energy minimization. Model assessment was
performed using SAVS. RAMPAGE analysis of the
selected models showed the presence of more than 80% of
amino acid residues in favored and allowed regions of
Ramachandran plot. Modeled proteins thus obtained were
used for the docking study [33-34]. Active sites of the
modeled proteins were identified using Q-Site finder and
listed in Table 2.
Based on the literature study, small molecules such as
allicin, ajoene and the combined compound ajocin were
chosen as inhibitors against aflatoxin biosynthetic enzymes.
1.3.2. Prescreening of Ligands for Lipinski rule /
ADMET / Bioavailability
The molecular weight, LogP value, number of donors and
acceptors for small molecule inhibitors Ac, Aj and Ajc
were generated using Molinspiration tool. All these three
molecules satisfied Lipinski rule. Bioactivities of these
molecules were also predicted. Molecules with the highest
activity score have the highest probability to be active.
Hence Ajc is found to be more active. Molecular
Properties and bioactivity were shown in Table 5.
ADME screening is performed using Discovery Studio
TOMKAT. ADMET descriptors like BBB (Blood Brain
Barrier) penetration and HIA (Human Intestinal
Absorption), CYP450 2D6, Lipinski rule of five, plasma
protein binding, aqueous solubility and hepatotoxicity were
found for these inhibitor molecules and tabulated in Table
6. All the three molecules satisfied ADMET characteristics
and hence could be used for biological process.
1.3.3. Protein – Ligand Docking
Molecular docking simulation studies were performed for
aflatoxin biosynthetic enzymes individually with
prescreened inhibitors namely allicin, ajoene and ajocin
using AutoDock 4.0. Docked structures were analyzed and
the interacting residues were identified. Residues
interacting in the docking study were found to reside in the
active site of the macromolecules which is well depicted in
Table 4. The residues interacting at the active site regions
were highlighted in the Table 2. Docked structures were
ranked based on the binding energy. Binding energy of the
best conformation for each of the enzyme with the inhibitor
molecules were tabulated in Table 3. The hydrogen bonds
formed during the interaction were listed in Table 4 along
with the hydrogen bond distance.
International Journal of Agriculture and Food Science 2012, 2(3): 81-89
Table 2: Enzymes and its active sites Active sites predicted by docking interactions are high-lighted for each of the target
enzyme molecule.
Enzyme
adhA
avanA
avfA
estA
fas2
norA
norB
omtA
omtB
ordA
pksA
vbs
ver1
85
Active Sites
Site 1
GLY22, GLY23, ALA24, SER25,GLY26, ILE27, GLY28, LEU29, ALA45, ASP46, ILE47, GLN48,
GLU51, CYS70, ASP71, VAL72, THR73, SER74, HIS113, ILE114, LEU115, ALA 116, VAL136,
MET172, GLY173, SER174, TYR187, LYS191, PRO217, ILE220, THR222, PRO223, LEU224, ILE225
Site 5
TYR95 , ALA296, GLY297, THR300, LEU304, PRO360, VAL361, GLN364, SER365, ILE368,
LEU389, GLN391, PRO428, PHE429, SER430, ILE431, ARG434, ASN435, CYS436, ILE437
Site 4
LEU7, GLY8, ALA9, THR10, LEU34, VAL35, ARG36, GLY61, THR62, SER63, THR64, LEU69,
VAL83, ALA84, GLN85, ASN86, SER88, PRO89, LEU94, SER98
Site 2
THR40, LEU41, ALA42, THR43, HIS85, GLY86, GLY87, GLY88, TRP89, VAL90, MET91, GLY92,
GLY93, VAL94, GLU116, TYR117, ARG118, LYS119, TYR125, ALA128, ALA 155, ASP229, ASP230,
THR233, TYR248
Site 3
GLY87, GLY88, SER154, ALA155, ASN158, ALA183, PRO184, ILE185, HIS188, ASP190, ALA191,
LEU244, LYS246, ILE249, TYR283, PRO284, SER287
Site 1
ALA975, PHE976, SER978, ALA979, LEU993, SER994, GLU995, ILE1072, LYS1073, ILE1079,
VAL1137, GLN1138, LEU1139, LEU1140, CYS1141, ARG1142, GLY1143, ALA1146, LEU1151,
PRO1152, ILE1153, TYR1154, GLY1155, ILE1156, ILE1157, LEU1263, ASN 1264, GLY1265,
LEU1267, GLN1268, ASP1271, THR1272
Site 1
GLY33, THR34, MET35, SER36, PHE37, ASN39, GLY40, TRP41, ASP69, TYR74, GLN75, TRP232,
GLY233, VAL234, LEU235, GLY236, ARG237, GLY238, ALA243, GLU244, GLU245, ARG248 ,
GLU249, LYS252, GLN283, VAL296, ILE297, GLY298, GLY299, ARG300, ASN308
Site 1
GLY28, ALA29, MET30, THR31, PHE32, PHE40, ASP64, THR65, TYR69, LYS96, HIS 143 , TRP144,
SER173, ASP174, GLN199, PRO226, TYR227, GLY228, VAL229, LEU230, ASN231, GLN232,
GLY233, ILE298, VAL299, GLY300, VAL301
Site 1
VAL155, THR156, GLY157, ALA160, LEU161, VAL162, GLY163, LEU206, PHE207, ASP208,
PHE221, ASP222, LEU223, GLY224, MET225, GLY226, GLY227, THR228, ALA230, ASP252,
VAL253, GLY254, GLY255, GLY256, ARG257, GLY258, HIS259, LEU260, ASP 277, LEU278,
VAL281, ILE282, ILE298, ARG313, SER314, ILE315, HIS317, ASP318, TYR 319, VAL368, VAL369,
THR371, LEU372
Site 1
GLY192, HIS193, LEU194, MET195, ALA196, GLU198, ARG199, PRO200, SER202, GLY228,
GLY229, GLY230, PHE231, GLY232, GLN233, GLN234, ARG289, HIS290, HIS293, ASP320, GLU321,
MET322, THR336, MET340, GLU348, GLN376
Site1:
LEU116, ALA117, MET132, GLU135, ILE136, MET179, MET180, VAL182, ALA183, TYR184,
TYR186, THR187, SER270, ILE271, ARG274, LEU291, THR294, ASN295, GLU297, PHE298,
VAL299, GLY301, GLY302, SER303, THR306, CYS440, PRO441, GLY442, ARG 443, VAL445,
THR446, LYS449
Site 6:
MET91 , SER366, PRO367, LEU368, GLY369, ALA370, HIS372, PRO393, ASN394, ILE395, PHE398,
PHE431, VAL432, PHE433, GLY434, PHE435, ARG438, ILE439, CYS440
THR496, ASP499, TRP500, THR503, ASN504, ALA542, CYS543, GLY581, LEU582, LYS584,
GLY585, PHE587, TYR597, TYR604, SER640, MET642, SER643, GLU644, SER645, MET646,
THR647, ARG648, GLN654, HIS678, GLY679, THR680, GLY681, THR682, VAL684, GLY685,
ASP686, VAL688, GLU689, HIS721, GLU723, PHE790, SER791, ALA792, ALA793, GLY794,
GLY795, ASN796
Site 1
VAL80, GLY81, GLY82, GLY83, THR84, ALA85, ILE104, GLU105, ALA106, GLY107, TRP141,
TYR144, TYR158, MET159, GLN160, GLY161, LYS162, THR163, GLY165, GLY166, SER167,
THR168, THR314, LEU315, VAL316, SER350, ALA351, GLY352, VAL353, MET354, ARG355,
SER356, GLN358, ASN511, TYR578, ALA579, GLY580, ASP612, ALA 613
Site 10
GLN390, ASP391, THR392, ALA492, LEU493, MET494, TYR577, ALA579, GLY580, VAL581,
PHE618, ALA619, ILE620, ASP621, GLY622, PRO624
Site 1
THR15, GLY16, ALA17, GLY18, ARG19, GLY20, ILE21, GLY22, ALA23, ASN39,TYR 40, ALA41,
International Journal of Agriculture and Food Science 2012, 2(3): 81-89
verA
verB
HIS42, SER43, ALA46, ASN94, ALA95, GLY96, ILE97, VAL98, THR142, SER 143, SER144, ASN145,
THR146, HIS155, TYR158, LYS162, VAL186, ALA187, PRO188, GLY189, ALA190, ILE191, THR193,
ASP194, MET195, PHE196, VAL199, SER200, TYR 203
Site 1
ARG78, LEU81, VAL84, MET90, MET91, THR92, TRP98, ARG102, PHE109, THR152, SER155,
ILE156, VAL159, VAL160, TRP184, THR185, VAL211, MET212, TYR243, LEU267, ARG268, VAL
269, PHE270, LEU271, PHE272, ALA273, GLY274, ARG275, THR 277, THR278, PRO344, SER345,
SER347, MET348, ARG352, ALA414, PHE415, GLU416, PRO419, ARG420, SER421, CYS422,
ILE423, GLY424, GLN425, LEU427, ALA428, GLU431, LEU432
Site 2
ARG78, LEU81, GLU145, THR148, THR149, TRP184, THR185, PHE187, ARG275, ASN276, THR277,
SER279, SER280, TYR284, PRO344, SER347, MET348, ARG349, GLU350, GLY351, ARG352,
TRP373, ARG413, GLU416, GLY475, GLY477, ALA479, HIS480
Site 1
PHE135, ILE178, SER181, ILE182, LEU185, LYS267, MET294, VAL295, ALA297, GLY 298, SER299,
THR301, THR302, ALA305, LEU306, ILE351, LEU355, TYR358, PRO359, ALA360, VAL361, HIS391,
GLN429, PRO430, TRP431, SER432, CYS438, ILE439, GLY 440, ARG441, LEU443, ALA444,
TYR445, GLU447, VAL448
Site 3
VAL171, GLU300, THR301, ALA303, SER304, ALA305, SER307, GLY308, ALA309, THR354,
LEU355, TYR358, PRO359, ALA360, PHE471, GLN474, ILE479, TRP480, ALA481, LYS482,
ARG483, GLU484, LEU485
Table 3: Enzymes and its binding energy with small molecules.
Binding Energy
Enzyme
Allicin
Ajoene
-3.9
-5.89
adhA
-4.04
-4.87
avanA
-4.24
-4.58
avfA
-4.81
-6.09
estA
-4.36
-3.76
fas2
-4.59
-4.99
norA
-3.85
-5.35
norB
-3.7
-4.78
omtA
Ajocin
-8.3
-7.23
-6.06
-4.58
-1.26
-6.13
-6.58
-6.42
omtB
-3.35
-4.24
-6.41
ordA
-4.12
-4.48
-6.71
pksA
-3.25
-1.85
54.7
vbs
-4.37
-4.7
-7.74
ver1
-3.84
-4.94
-6.77
verA
verB
-4.21
-4.24
-4.91
-5.06
-7.49
-7.54
Table 4: Interaction of the enzymes with small molecules Hydrogen bond between aflatoxin biosynthetic enzymes and
ligands with its binding distance
Enzyme
Binding Energy
Ajoene
Allicin
Hydrogen bond
adhA
avanA
avfA
Lys 191:
HZ1:Mol1:03
ARG 434:HH21
LIG1:O
ALA 84: HN LIG1:O
estA
GLY86:HN LIG1: O
TYR283: HH LIG1:S
fas2
GLN1268:HE22
LIG1:O
86
Combined Compound
Binding
Hydrogen bond
Distance
Binding
Distance
Hydrogen bond
Binding
Distance
2.055
AdhA:ILE220:HN:Mol1:O
10
1.786
HIS113:HE2:lig1:O
1.920
1.951
ALA792:HN:mol1:O3
2.183
ARG434:HH21:lig1:O
1.986
2.035
1.943
GLN85:HN:MOL1:O10
2.121
1.938
LYS119:HZ3:MOL1:O10
1.834
ALA84:HN:Lig1:O
EstA:GLY86:HN:Lig1:
O
EstA:TYR283:HN:Lig1:
S
Fas2:GLN1268:HE22:LI
G1:O
2.042
1.856
SER132: HG:MOL1:O10
1.710
International Journal of Agriculture and Food Science 2012, 2(3): 81-89
2.034
2.081
1.91
norA
norB
omtA
omtB
GLU245:OE1
LIG1:SGLY298:HN
LIG1:O
GLY228:HN:MOL1:
O3
HIS259:HN:MOL1:O
3
SER202:HG:MOL1:O
3
1.827
GLY233:HN:MOL1:O10
1.852
GLU298:HN: LIG1:O
1.921
2.108
GLN232:HN:MOL1:O10
2.121
LYS96:HZ2:LIG1:O
2.225
2.184
ARG313:HH11:MOL1:O10
1.859
ARG313:HH11:LIG1:O
1.705
1.872
HIS290:HD1:MOL1:O10
1.835
THR336:HG1:LIG1:O
1.943
ALA370:HN:MOL1:O10
HIS371:HE2:MOL1:O10
SER366:HG:LIG1:O
2.046
THR729:OG1:LIG1:S
2.999
1.961
2.032
ordA
LYS449:HZ3:MOL1:
O3
1.827
pksA
ALA792:HN:mol1:O3
2.166
HIS721:NE2:MOL1:S5
vbs
ILE620:HN:MOL1:03
1.885
GLN160:HE22:MOL1:S5
SER167:HN:MOL1:O10
ver1
ILE191:HN:MOL1:03
ARG78:HH12:MOL1:
03
ALA481:HN::MOL1:
03
1.922
GLY22:HN:MOL1:O10
2.2
1.93
1.739
GLY424:HN:MOL1:O10
1.970
ILE439:HN:MOL1:O10
verA
verB
2.044
2.87
2.172
GLN160: HE22:LIG1:O
ALA172: HN :LIG1:O
ILE191:HN:LIG1:O
2.022
1.931
2.071
ALA479:HN:LIG1:O
1.778
1.757
GLY440:HN:LIG1:O
1.933
Table 5: Molecular Properties and Bioactivity
Descriptors
Allicin
Ajoene
Ajocin
MOLECULAR PROPERTIES
miLogP
2.064
0.026
2.725
TPSA
17.071
20.228
40.456
natoms
9
13
22
MW
162.279
235.419
406.77
nON
1
1
2
nOHNH
0
1
2
nviolations
0
0
0
nrotb
5
8
17
volume
145.506
210.596
375.404
BIOACTIVITY PREDICTION
GPCR ligand
-1.93
-0.94
-0.29
Ion channel modulator
-2.02
-0.94
-0.51
Kinase inhibitor
-2.58
-1.50
-0.68
Nuclear receptor ligand
-2.59
-1.41
-0.29
Table 6: ADME Properties
Ligand
Log P
BBB
Hepatotoxicity
Ajoene
Alicin
Ajocin
2.32
1.77
4.87
0.23
0.66
0.69
0
0
0
Hepatotoxicity
Probability
0.35
0.32
0.16
The activity of Ajc compund was found to be higher than
Ac and Aj compounds in most of the enzyme inhibitory
process. VERB synthase gene (vbs) plays the key role in
the synthesis of aflatoxin. Based on the literature study, the
87
CYP2D
6
0
0
0
CYP2D6
Probability
0.14
0.02
0.44
PSA_2D
Solubility
20.82
20.82
40.63
-1.59
-1.08
-2.99
function of vbs gene is found to convert VHA to VERB in
A. parasiticus. This is a key step in aflatoxin formation
since it closes the bisfuran ring of aflatoxin. This ring is
required for binding to DNA and gives aflatoxin its mode
International Journal of Agriculture and Food Science 2012, 2(3): 81-89
of action as a mutagen. Despite other enzymes involved in
the aflatoxin synthesis, vbs was found to be the most
important target for enzyme inhibition to control aflatoxin
synthesis. Docking studies were performed with these
genes against the inhibitors allicin, ajoene and ajocin. The
action of the inhibitor ajocin was best proved in the
inhibition of vbs gene with a binding energy of -7.74 by the
formation of two hydrogen bond interactions at the active
site region. This enzyme inhibitory process is required to
stop aflatoxin synthesis and to prevent peanut infection.
Based on our in silico study and literature studies of zimmu
compound in sorghum plants, we suggest that ajocin
compound would contribute the maximum effect to inhibit
the fungal infection caused by aflatoxins in peanut plants
by preventing aflatoxin synthesis.
1.4. Conclusion:
In this work, a molecular docking simulation study was
carried out for all the fifteen enzymes involved in the
aflatoxin biosynthesis using the inhibitors allicin, ajoene
and ajocin. Enzyme inhibitory process was required to stop
the synthesis of aflatoxins. From this in silico study and
previously reported experimental data in literature, we
found that vbs gene present in the aflatoxin biosynthetic
pathway is known to be responsible for the production of
aflatoxins. Hence, we conclude that ajocin would be the
best possible component to inhibit the vbs gene which
controls the biosynthesis of aflatoxins and prevent human
carcinogenic exposure.
Acknowledgments
Authors would like to thank the faculties of Department of
Plant Pathology, Centre for Plant Protection Studies, Tamil
Nadu Agricultural University for providing information
about Zimmu extract derived from alicin and ajoene which
remained as an initiative for this In silico docking study.
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