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Molecular Docking of Known Carcinogen 4- (Methyl-nitrosamino)-1-(3-pyridyl)-1-butanone (NNK) with Cyclin Dependent Kinases towards Its Potential Role in Cell Cycle Perturbation

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Cell cycle is maintained almost all the times and is controlled by various regulatory proteins and their complexes (Cdk+Cyclin) in different phases of interphase (G1, S and G2) and mitosis of cell cycle. A number of mechanisms have been proposed for the initiation and progression of carcinogenesis by abruption in cell cycle process. One of the important features of cancer/carcinogenesis is functional loss of these cell cycle regulatory proteins particularly in CDKs and cyclins. We hypothesize that there is a direct involvement of these cell cycle regulatory proteins not only at the genetic level but also proteins level, during the initiation of carcinogenesis. Therefore, it becomes significant to determine inconsistency in the functioning of regulatory proteins due to interaction with carcinogen 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Hence, we investigated the interaction efficiency of NNK, against cell cycle regulatory proteins. We found a different value of ΔG (free energy of binding) among the studied proteins ranging between -3.29 to -7.25 kcal/mol was observed. To validate the results, we considered Human Oxy-Hemoglobin at 1.25 Å Resolution, [PDB_ID:1HHO] as a +ve control, (binding energy -6.06 kcal/mol). Finally, the CDK8 (PDB_ID:3RGF) and CDK2 (PDB_ID:3DDP) regulatory proteins showing significantly strong molecular interaction with NNK -7.25 kcal/mol, -6.19 kcal/mol respectively were analyzed in details. In this study we predicted that CDK8 protein fails to form functional complex with its complementary partner cyclin C in presence of NNK. Consequently, inconsistency of functioning in regulatory proteins might lead to the abruption in cell cycle progression; contribute to the loss of cell cycle control and subsequently increasing the possibility of carcinogenesis.
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Molecular Docking of Known Carcinogen 4-
(Methyl-nitrosamino)-1-(3-pyridyl)-1-butanone
(NNK) with Cyclin Dependent Kinases towards Its
Potential Role in Cell Cycle Perturbation
Mohd Haneef1, Mohtashim Lohani1*, Anupam Dhasmana2, Qazi M.S. Jamal1, S.M.A. Shahid1
& Sumbul Firdaus3
1Department of Biosciences, Integral University, Lucknow-226026, UP, India; 2Department of Bioengineering, Integral University,
Lucknow-226026, UP, India; 3Department of Physics, Integral University, Lucknow-226026, UP, India; Mohtashim Lohani - Email:
mlohani@rediffmail.com; Phone: 0522-2890812, 2890730, 3296117, 6451039; *Corresponding author
Received June 14, 2014; Revised July 02, 2014; Accepted July 07, 2014; Published August 30, 2014
Abstract:
Cell cycle is maintained almost all the times and is controlled by various regulatory proteins and their complexes (Cdk+Cyclin) in
different phases of interphase (G1, S and G2) and mitosis of cell cycle. A number of mechanisms have been proposed for the
initiation and progression of carcinogenesis by abruption in cell cycle process. One of the important features of
cancer/carcinogenesis is functional loss of these cell cycle regulatory proteins particularly in CDKs and cyclins. We hypothesize
that there is a direct involvement of these cell cycle regulatory proteins not only at the genetic level but also proteins level, during
the initiation of carcinogenesis. Therefore, it becomes significant to determine inconsistency in the functioning of regulatory
proteins due to interaction with carcinogen 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Hence, we investigated the
interaction efficiency of NNK, against cell cycle regulatory proteins. We found a different value of ∆G (free energy of binding)
among the studied proteins ranging between -3.29 to -7.25 kcal/mol was observed. To validate the results, we considered Human
Oxy-Hemoglobin at 1.25 Å Resolution, [PDB_ID:1HHO] as a +ve control, (binding energy -6.06 kcal/mol). Finally, the CDK8
(PDB_ID:3RGF) and CDK2 (PDB_ID:3DDP) regulatory proteins showing significantly strong molecular interaction with NNK -7.25
kcal/mol, -6.19 kcal/mol respectively were analyzed in details. In this study we predicted that CDK8 protein fails to form functional
complex with its complementary partner cyclin C in presence of NNK. Consequently, inconsistency of functioning in regulatory
proteins might lead to the abruption in cell cycle progression; contribute to the loss of cell cycle control and subsequently
increasing the possibility of carcinogenesis.
Keywords: Cell cycle, Carcinogen NNK, Check points, CDKs, Oncoinformatics and Z-Dock.
Background:
Cell cycle is controlled by various regulatory proteins (check
points) for proper cell division. Infact, cell division of cell is
divided into two stages: mitosis (M) and interphase (I)
including G1, S and G2 transition phases [1]. The transition
from one phase to another phase occurs in an orderly fashion
and is regulated by different type of cellular proteins,
particularly cyclin-dependent kinases (CDKs-family of
serine/threonine protein kinases) and cyclins are
activated/deactivated at specific points. Therefore, CDKs
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induce downstream processes by phosphorylating the
regulatory proteins in cell cycle [2]. However, different types of
cyclins D (cyclin D1, cyclin D2 and cyclin D3) are essential for
the regulation of cell cycle. Further, cyclins bind to CDKs and
form CDK-cyclin complex. This complex/s is/are very
important for phase progression in cell cycle [3]. The functional
activity of that CDK-cyclin complex is induced conformational
changes due to phosphorylation in conserved threonine and
tyrosine residues of CDKs part. Thus the complex (Cdk+Cyclin)
enhances or suppresses the binding efficiency of their specific
cyclin partner [4]. In case of cancer, mutations have been
observed in genes encoding CDKs, Cyclins (D1, D2, D3, A, H, T
& C), CDK-activating enzymes (CAK), CKI and CDK-substrates
[5]. Consequently, changes in functioning of CDKs activity to
formed Cdk+Cyclin complex, an important part of many
cancers, as well as other disease states, generally through
elevated and/or inappropriate activation [6]. One of the
probable ways of loss of cell cycle control could be disruption
of these regulatory proteins CDKs (check points) by a direct
interaction with chemical/carcinogen. In the present study, we
investigated the possible molecular interactions between CDKs,
cyclins with potent cigarette smoke carcinogen 4-
(Methylnitrosamino)-1-(3-Pyridyl)-1-butanone (NNK).
NNK is a yellow crystalline compound with a molecular
formula C10H13N3O2, (Mol Weight = 207.2316) derived from
tobacco alkaloids (nitrosamine) as a potent carcinogen [7]. The
concentrations of NNK in tobacco substances such as 1-20
μg/g, 20-310 ng/cigarette and ≤26 ng/m3 in snuff, cigarette
mainstream smoke, and in indoor air respectively [8]. The
existences of substantial amount of NNK in tobacco products
play a very significant role as a main cause of cancer in
population [9]. Ultimately, NNK is considered as a major
contributor as well as risk factor to lung carcinogenesis [10-11].
NNK and its metabolite NNAL, has already been reported to
molecular interaction with DNA repair proteins primarily by
our group [12].
We analyzed the molecular interaction, based on binding
efficiency of potent carcinogen NNK against CDKs (check
points) and cyclins involved in the cell cycle process. In case of
cancer, cells develop an autonomous set of instructions against
normal rules, leading to uncontrolled, undifferentiated growth
and proliferation called an abnormal condition of a cell known
as carcinogenicity. The carcinogen NNK and its metabolite
NNAL directly bind with DNA repair proteins to make DNA
adduct. Therefore, possibility exists that NNK may directly
interact with these regulatory protein (CDKs, cyclin) and affect
the functional activity of CDK-cyclin complex in cell cycle
regulation. The carcinogen NNK induced cell cycle abruption
may in turn result in hastened DNA replication, with a
compromised proofreading by DNA pol/RNA pol. This
process in a whole may give rise to daughter cell with loads of
mutations in their DNA. Therefore we designed this study to
investigate and determine whether the carcinogen NNK, apart
from directly causing damage to regulatory proteins, is also
capable of affecting their functionalities in term of binding
efficiency of CDKs to their specific cyclins partner (A, C, D, D1,
D2, D3, E, and H) for proper signal to execute cell cycle phases
normally.
Methodology:
Preparation of receptor- protein structures
The 3D structures of check point proteins (CDKs & Cyclins)
involved in cell cycle regulation were obtained from PDB
(Protein Data Bank) and some other proteins retrieved from
MODBASE server Table 1 (see supplementary material).
MODBASE is a queryable database of annotated protein
structures models, theoretically calculated models, which may
contain significant errors, not experimentally determined
structures [13]. Published protein structures were edited, to
remove HETATM by using Discovery Studio Visualizer
(Version 2.5.5). And Chimera was used for energy
minimization, removal of steric collision (forces) with the
steepest descent steps 1000, steepest descent size 0.02 Å,
conjugated gradient steps 1000 and gradient step size 0.02 Å for
the conjugate gradient minimization [14-15]. Protein structure
visualization and image generation were performed using
PyMOL software (DeLano Scientific, Palo Alto, CA).
Figure 1: (A) 4-(Methylnitrosamino)-1-(3-pyridyl)-1-
butanonePubChem Compound ID- 47289,ChemSpider ID-
43038; (B) 3RGF:CDK8 interact with carcinogen NNK; (C)
3DDP:CDK2 interact with carcinogen NNK
Preparation of ligand structure
Potent cigarette smoke carcinogen NNK (4-
(Methylnitrosamino)-1-(3-pyridyl)-1- butanone) ligand file was
retrieved in (dot).mol format Table 2 (see supplementary
material) (Figure 1) from latest version of Chem Spider
Chemical Database. This file format could not directly use by
Autodock (4.0) tool [16]. Thus that file was finally converted it
into.(dot)pdb file format using DS Visualizer (version 2.5.5), so
it easier to comprehend chemoinformatics and molecular
mechanics of ligand and different receptor proteins interacting
molecules. Further, ligand was submitted for minimization by
using Chimera (version 1.5.3) with Genetic Algorithm Steps
2000 and 0.5 grid units Optimized [17].
In silico Studies
In silico studies were performed by Autodock version 4.0 suit
with Cygwin interface tool [18-19]. We selected molecular
docking methods for CDKs and NNK interaction, followed by
retrieving the best conformations of check point (CDKs) as
regulatory proteins and carcinogen (NNK), on the basis of
binding energy value (kcal/mol). First of all, we marked all
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water molecules (H2O) in proteins then removed from targeted
protein structure, before apply docking performance. Then only
hydrogen atoms were added to all target proteins. After that
Kollman united charges and salvation parameters were applied
to selected regulatory proteins (CDKs and cyclins). Gasteiger
charge also was charged to ligand (NNK). Then defined Grid
box was set to cover the maximum part (including target site)
of selected protein for ligand interaction. The value was set to
standard 60×60×60 Å in X, Y and Z coordinate of grid point
with default value of grid points spacing 0.375 Å. Lamarckian
Genetic Algorithm (LGA) was applied for receptor protein and
ligand for flexible docking calculations [20]. The LGA
parameters like population size (ga_pop_size), energy
evaluations (ga_num_generation), mutation rate, crossover rate
and step size were set to 150, 2500000, 27000, 0.02, 0.8 and 0.2 Å,
respectively. The LGA runs were set to as standard 50 runs. We
observed all 50 conformations of selected proteins with ligand
complex were analyzed for the interaction orientations
including binding energy of the docked structure using
Discovery Studio Visualizer version 2.5.5.
Protein-Protein Interaction analysis
The interacting regulatory proteins (CDKs) and cyclins were
found using STRING 9.0 database that predict, interacting
proteins against your query. We found interface residues in
CDKs & Cyclins using PDBe PISA, an interactive tool for the
exploration of macromolecular (protein, DNA/RNA and
ligand) interfaces [21]. The Discovery studio 2.5 was used for
Zdock (Dock Proteins) and Zdock score obtained from Protein-
Protein interactions (Cdk+Cyclin) as well as from Protein-
Complex (NNK+CDK & Cyclin) interaction.
Z dock calculations
Zdock is one of the successful suites that have shown great
prediction abilities in Critical Assessment of Predicted
Interactions (CAPRI) [22]. Zdock uses a fast Fourier transform
to search all possible binding modes for the proteins,
evaluating based on shape, desolvation energy, and
electrostatics. The top 2000 predictions from Zdock where they
are minimized by CHARMM with create fixed atom constraint
in backbone of protein and again create Harmonic restraint in
selected protein for improving the energies and eliminate
clashes. ZDOCK is an initial stage rigid body molecular
docking algorithm that uses a fast Fourier transform (FFT)
algorithm to improve performance for searching in
translational [23]. All of the available structures from NMR
were used to calculate docking poses and the structures
obtained were subjected to energy minimization using, smart
minimize algorithm (Max steps 200, RMS gradient 0.01) in the
program D.S. 2.5. The resulting highest values of score were
used as appropriate conformational pose with Zdock score
value Table 3 (see supplementary material).
Results:
The ligand structure NNK (4-(Methylnitrosamino)-1-(3-
Pyridyl)-1-butanone) as a potent cigarette smoke carcinogen
described in this study was retrieved from latest version of
Chem Spider Chemical Database in (dot).mol format (Table 2
& Figure 1A) with PDB-ID: 1B17. The structures of the cell
cycle regulatory proteins CDKs (CDK8:3RGF, CDK2:3DDP,
CDK7:1UA2, CDK6:1BLX, CDK9:3BLH & CDK4:3G33)
obtained from PDB and other complementary partner protein
cyclin (cyclin A: 1JSU, cyclin H: 1KXU, cyclin D3: 3G33, cyclin
D: 2W9F, cyclin C: 1ZP2 and cyclin T: 3BLR) were retrieved
from MODBASE server. Table 1 & Table 4 (see supplementary
material) show the docked score (binding energy) of ligand
NNK against cyclin dependents kinases (CDKs),
complementary partner proteins cyclins. While Figure 1 B & C
were indicated the docking images with best molecular
interaction orientation. In this in silico study, the docking scores
(binding energy) of CDK8 and CDK2 regulatory proteins
against the carcinogen NNK, -7.25 kcal/mol & -6.19 Kcal/mol
respectively were more than that of positive control Human oxy-
hemoglobin binding energy -6.06 kcal/mol. The residues ARG 150,
ASP 151, LEU 152, LYS 153, THR 196, PHE 197, TRP 198, TYR
199, ARG 200, ALA 201, LEU 204, TYR 211, ILE 215 and ALA
219 of CDK8 and ILE 10, VAL 18, ALA 31, LYS 33, VAL 64, PHE
80, GLU 81, PHE 82, LEU 83, HIS 84, GLN 85, ASP 86, LEU 134
and ALA 144 of CDK2 actively participate in molecular
interaction with NNK as shown in Table 5 (see supplementary
material). The residues ARG, ASP, LEU, LYS, TYR, VAL and
ALA from all regulatory proteins (CDKs) commonly interacted
with carcinogen NNK through hydrogen bonds. The functional
integrity with interaction efficiency of CDKs once bound to
NNK was also evaluated by using Zdock method. We obtained
Zdock-scores of CDK8 with its corresponding partner cyclin C
was 30.12, and of CDK2 with its corresponding partner cyclin A
was 21.62. Whereas, the complex CDK8+NNK when docked
with its partner cyclin C showed Zdock score 19.12, and
CDK2+NNK when docked with its partner cyclin A showed
Zdock score 21.34. Reduction in the Zdock score of CDK8 after
it forms complex with NNK may represent a loss in its capacity
to bind with partner cyclin C, once bound with NNK. Whereas,
regulatory protein CDK2 doesn’t show significant change in
Zdock value, after binding with NNK as shown in (Table 3).
Discussion:
An insilico approaches were applied to evaluate protein-ligand
and protein-protein interaction (PPI) for identifying possible
targets of carcinogen (NNK) amongst cyclin dependent-kinases
(CDKs) as well as with their respective partner cyclins. In order
to gather in-depth knowledge on, an important issue that how
the cigarette smoke carcinogen NNK interfere the mechanism
of signals through CDK complex (Cdks+Cyclins) in
proliferation, cell division and in abruption of cell cycle. In this
study, we characterized and identified the molecular
interaction of NNK with all regulatory proteins (CDK2, CDK4,
CDK6, CDK7, CDK8, and CDK9) and with their respective
partner cyclins (Cyclin A, Cyclin E, Cyclin D, D1, D2, D3,
Cyclin C, Cyclin H, and Cyclin T) by using Autodock and
Zdock (protein-protein interaction) methods. To validate our in
silico study, we considered Human oxy-hemoglobin at 1.25 Å
resolution, [PDB ID-1HHO] as a +ve control, which showed
binding energy of -6.06 kcal/mol, as the binding of Human oxy-
hemoglobin with NNK has previously been quantified in
tobacco users and is considered as a biochemical marker for
uptake of tobacco specific nitrosamines [24]. The docking
outputs indicated that potent cigarette smoke carcinogen NNK
shows the binding efficiency (∆G) against cell cycle regulatory
enzymes/proteins (CDKs) ranging, -3.95 to -7.25 kcal /mol
(Table 1). Simulations depicted that two regulatory proteins
CDK8 (3RGF) & CDK2 (3DDP) showed better potential to bind
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carcinogen NNK as compared to +ve control, i.e. -7.25 & -6.19
Kcal/Mol (Figure 1 B & C) respectively. During the protein-
ligand interaction, the amino acid residues of CDK8 involved in
interaction with ligand (NNK) were identified as ARG150,
ASP151, LEU152, LYS153, THR196, PHE197, TRP198, TYR199,
ARG200, ALA201, LEU204, TYR211, ILE215, and ALA219.
While, in case of protein CDK2 the amino acid residues, namely
ILE10, VAL18, ALA31, LYS33, VAL64, PHE80, GLU81, PHE82,
LEU83, HIS84, GLN85, ASP86, LEU134, and ALA144 were
found to be involved efficiently in the interaction with ligand
NNK. In fact, amino acid residues, particularly ARG, ASP,
LEU, LYS, TYR, VAL and ALA of both CDK8 and CDK2 were
found to be essential for the interaction of carcinogen NNK
(Table 5). This molecular interaction between CDKs and NNK
is validated on the basis of their binding energy (∆G) obtained
from best docked conformations. It has been reported that
Cyclin Dependent Kinase-8 is actively involved in the regulation
of mRNA transcription and considered as a potent oncogene in
colon carcinogenesis. In addition, mutated or amplified CDK8
with increased expression is a common observation during a
variety of human cancers [25]. However, we further explored
the interaction impact of NNK on CDK8 (3RGF) in term of
binding efficiency towards its respective partner cyclin C. For
this purpose, we applied an in silico Zdock method for
calculating Zdock score of protein-protein interaction (PPI)
between CDK8 Vs Cyclin C complex and further compared it
with protein complex interaction (CDK8+NNK Vs Cyclin C) by
using Discovery studio 2.5. Results clearly depicted that
significant loss of binding energy of CDK8 Vs Cyclin C from
30.12 (PPI) to 19.12 for NNK bound with CDK 8 complex Vs
Cyclin C (PCI) at coordinates X -17.585, Y 11.939 & Z 17.689.
These results predicted that binding of NNK at the active site of
CDK8 strongly interferes with the natural binding of cyclin C to
the active site of CDK8, rendering it unable to form functional
complex (CDK8+cyclin C). It has been reported that CDK8
positively regulates transcription, by directly phosphorylating
p53 and histone H3, or by facilitating assembly of Pol II
elongation complex [26]. The CDK8-cyclin C complex is the
part of RNA Polymerase II, which regulate the transcription of
general transcription initiation factor IIH (TFIIH), controlling
the basal transcription machinery. Failing the formation of
CDK8-cyclin C complex may eventually will result in failure of
the transcriptional regulation of a member of RNA-Pol-II
dependent genes [27]. CDK8 plays significant role in regulating
cell cycle progression [28] And CDK 8-cyclin C complex
abnormality has many times reported to result in
tumorogenesis [29]. When we performed same Zdock study
with other protein CDK2, it was observed that NNK binding
with the CDK2 didn’t significantly interfere natural binding of
its partner cyclin A Table 3. Previously QMS Jamal, et al., 2012
presented Zdock based analysis to determine the
loss/incapability in formation of functional complex of
regulatory proteins in DNA repair pathways after its binding
with a concerned chemical/carcinogen [12]. The binding of
NNK with CDK8 therefore, may be an important event in
carcinogenesis caused by cigarette smoke carcinogen and
should be studied in depth.
Conclusion:
In silico study explores the interaction of CDK8 (cyclin
dependent kinase-8) with NNK, a widely inhaled potent
cigarette smoke carcinogen among the young generation of
population. CDK8, cyclin C and its complex (CDK8+cyclin C)
are the key mediator of cell cycle progression which play a vital
role in cell cycle perturbation due to potential interaction of
cigarette smoke carcinogen NNK. The hydrogen bonds and
certain amino acid residues ARG, ASP, LEU, LYS, TYR, VAL
and ALA play a key role in the correct positioning of CDKs
within the active site of NNK to permit docking interaction.
The effect of molecular interaction of NNK on the binding with
CDKs is elucidated. Study indicated the loss of functional
complex of these enzymes/regulatory proteins (Cdk+Cyclin),
which probably could be a reason for perturbation in cell cycle
process resulting in occupied active site of CDKs by NNK.
Furthermore, our study suggests that carcinogens (NNK)
positively alter the mechanisms of cell cycle progression
pathways and enzymes functioning could be affected by
carcinogens. Computer based structural analysis of bio
macromolecules and their molecular interactions (ligand and
protein) could play an important role in assessment of risk to a
number of diseases including cancer. At last but not least, a
deep analysis is needed to elucidate the perturbation of cell
cycle mechanisms with best suitable techniques and tools. In
vivo and In vitro validation is needed to authenticate insilico
results obtained from this study.
Conflict of Interest:
We have no conflict of interest with anybody working in the
area and among the authors in the manuscript.
Acknowledgement:
We expressed our sincere gratitude to Prof. S.W. Akhtar, the
Hon’ble Vice-Chancellor of Integral University Lucknow, U. P.
India for his support and for providing necessary facility.
Special thanks to Dr. (Er.) Mohd Haris Siddiqui (Director &
HOD Bioengineering), Dr Shazi Shakil, Dr. Salman Akhtar, Dr.
S.M.D. Rizvi, Mohd. Kalim Khan, Er. Adnan Ahmad and all
faculty members of department of Bioenigering, Integral
University, Lucknow, India for the valuable suggestions and
important critical comments.
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Edited by P Kangueane
Citation: Haneef et al. Bioinformation 10(8): 000-000 (2014)
License statement: This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for
non-commercial purposes, provided the original author and source are credited
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Supplementary material:
Table 1: Cyclin Dependent Kinases (Cdks) docked with carcinogen NNK, results obtained by Autodock 4.0
S.N.
PDB_ Id
Name of
Proteins
Binding Energies
(Kcal/Mol)
Estimated Inhibition
Constant (µm)
Reference r.m.s.d.*
1
3RGF
CDK8
-7.25
4.84
24.50
2
3DDP
CDK2
-6.19
28.79
34.47
3
1UA2
CDK7
-6.02
38.41
34.41
4
1BLX
CDK6
-5.68
68.61
73.14
5
3BLH
CDK9
-4.29
711.60
56.20
6
3G33
CDK4
-3.95
128.00
71.85
*rmsd: root mean square deviation
Table 2: Fact of Potent Carcinogen NNK
Carcinogen
IUPAC Name
Mol. Formula
Mol. Weight
Smiles
NNK
4-(Methylnitrosamino)-1-(3-pyridyl)-1-
butanone
C10H13N3O2
207.22912 g/mol
CN(CCCC(=O)C1=CN=CC=C1)N
=O
Table 3: ProteinProtein Interaction of Cyclin Dependent-Kinases (CDKs) and their Respective Regulatory Proteins (Cyclins)
S. N.
PDB _Id
Name of Proteins
Name of Regulatory
Proteins
Coordinates of Docked Pose
(X, Y, And Z Respectively )
Zdock
Score
Protein-Complex
* Zdock Score
1
3RGF
CDK8
Cyclin C
-17.585, 11.939 & 17.689
30.12
19.12
2
3DDP
CDK2
Cyclin A
13.731, 29.461 & 8.810
21. 62
21.34
*Complex contain NNK+CDKs Vs Cyclins
Table 3 4: Regulatory Proteins (Cyclins) Docked with Carcinogen NNK, Results Obtain By Autodock 4.0
S.N.
Pdb_Id
Name of Proteins
Binding Energies (Kcal/Mol)
Estimated Inhibition Constant (µm)
Reference r.m.s.d.
1
1JSU
Cyclin A
-6.00
40.24
55.67
2
1KXU
Cyclin H
-5.68
68.93
93.16
3
3G33
Cyclin D3
-5.35
119.87
67.33
4
2W9F
Cyclin D
-4.98
222.64
88.45
5
1ZP2
Cyclin C
-4.85
285.63
98.37
6
3BLR
Cyclin T
-3.29
3880.0
39.84
Table 5: Interacting Amino Acid Residues Involved in Complexes formation with NNK in Cell Cycle Regulatory Proteins
S.N
Pdb _Id
Name of
Proteins
Number of
Hydrogen
Bonds
Residues Involved In
Hydrogen Bonding
Amino Acid Residues
Involved In Interaction
With NNK Carcinogen
Common Interacting A.
A. Residues In
Regulatory Proteins
1
3RGF
CDK8
6
TYR 199: N- - -PHE
197: O
TRP 198: N- - -O 15:
NNK
TYR 199: N- - -N 14:
NNK
TYR 199: N- - -O 15:
NNK
ALA 201: N- - -N 10:
NNK
TYR 211: OH- - -O 7:
NNK
ARG 150, ASP 151, LEU
152, LYS 153, THR 196,
PHE 197, TRP 198, TYR
199, ARG 200, ALA 201,
LEU 204, TYR 211, ILE 215,
ALA 219.
ARG, ASP, LEU, LYS,
TYR, VAL, ALA,
2
1BLX
CDK6
7
ARG 144: HN- -
OD1:ASP201
LEU 146: HN - -O15:
NNK
ARG 186: HH11O7:
NNK
ARG
TYR 185, ARG 186, ALA
187, VAL 190, TYR 196,
VAL 200, ASP 201, SER
204.
open access
ISSN 0973-2063 (online) 0973-8894 (print)
Bioinformation 10(8): 526-532 (2014)
532
© 2014 Biomedical Informatics
186: HH12OD1:ASP
145
SER 204: HNO : VAL
200
SER 204: HG N 14:
NNK
SER 204: HN-O 15:
NNK
3
1UA2
CDK7
2
ARG 179: N- - -O7:
NNK
ALA 180: N- - -O15:
NNK
ARG 136, ASP 136, LEU
138, LYS 139, THR 175,
TYR 178, ARG 179, ALA
180, LEU 183, VAL 194,
ALA 198.
4
3BLH
CDK9
5
SER115: N- - -O: ALA
111
SER115: N- - -O: GLY
112
ALA152: N - - - O: LYS
151
ALA152: N - - - O 15:
NNK
TYR 194: OH- - - OE1:
GLU 221
ALA 111, LEU 114, SER
115, LYS 151, ALA 152,
ALA 153, TYR 194, GLU
221, ARG 225, SER 226,
PRO 227.
5
3G33
CDK4
9
LYS 35: HZ1- - -
ASP158:OD1
LYS 35: HZ1- - -
ASP158:OD2
LYS 35: HZ1- - -N 14:
NNK
LYS 35: HZ1- - -O 15:
NNK
LYS 35: HZ3- - -
ASP158:OD1
LYS 35: HZ1- - -
ASP158:OD2
ASN 140: HD21- - -
O:ASP140
LYS 142: HZ3- - - -N10:
NNK
ALA 197: HN - - -O:
ASN 145
GLY 57, ALA 16, TYR 17,
LYS 35, ASP 140, LYS 142,
GLU 144, ASN 145, LEU
147, ALA 157, ASP 158.
6
3DDP
CDK2
6
LYS 33: NZ- - -OE1:
GLU 51
LYS 33: NZ- - -N 14:
NNK
LYS 33: NZ- - -O 15:
NNK
LEU 83: N- - - O 7:
NNK
GLN 85: N- - -O: ILE
135
ILE 135: N- - -O: 85
GLN
ILE 10, VAL 18, ALA 31,
LYS 33, VAL 64, PHE 80,
GLU 81, PHE 82, LEU 83,
HIS 84, GLN 85, ASP 86,
LEU 134, ALA 144.
... Currently, we have immense information on how tobacco consumption has direct implications in cancer, specially lung, head and neck, stomach, liver, and pancreatic cancers [5,6]. 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is one of the main components in tobacco that plays a major role in the causation of cancer [6]. ...
... Currently, we have immense information on how tobacco consumption has direct implications in cancer, specially lung, head and neck, stomach, liver, and pancreatic cancers [5,6]. 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is one of the main components in tobacco that plays a major role in the causation of cancer [6]. NNK and its derivative, 4-(methylnitrosamino)-1-(3-pyridyl)-1butanol (NNAL), binds with the DNA and forms DNA adducts, the resultant of which may lead to genetic mutations followed by the deregulation of normal cellular processes [6,7]. ...
... 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is one of the main components in tobacco that plays a major role in the causation of cancer [6]. NNK and its derivative, 4-(methylnitrosamino)-1-(3-pyridyl)-1butanol (NNAL), binds with the DNA and forms DNA adducts, the resultant of which may lead to genetic mutations followed by the deregulation of normal cellular processes [6,7]. ...
Article
Full-text available
Cancer is the second deadliest disease listed by the WHO. One of the major causes of cancer disease is tobacco and consumption possibly due to its main component, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). A plethora of studies have been conducted in the past aiming to decipher the association of NNK with other diseases. However, it is strongly linked with cancer development. Despite these studies, a clear molecular mechanism and the impact of NNK on various system-level networks is not known. In the present study, system biology tools were employed to understand the key regulatory mechanisms and the perturbations that will happen in the cellular processes due to NNK. To investigate the system level influence of the carcinogen, NNK rewired protein–protein interaction network (PPIN) was generated from 544 reported proteins drawn out from 1317 articles retrieved from PubMed. The noise was removed from PPIN by the method of modulation. Gene ontology (GO) enrichment was performed on the seed proteins extracted from various modules to find the most affected pathways by the genes/proteins. For the modulation, Molecular COmplex DEtection (MCODE) was used to generate 19 modules containing 115 seed proteins. Further, scrutiny of the targeted biomolecules was done by the graph theory and molecular docking. GO enrichment analysis revealed that mostly cell cycle regulatory proteins were affected by NNK.
... A mix standard solution containing 0.1 mg of each of the seven N-nitrosamines was prepared from the stock according to the method established by Wang et al. [13] Individual reference standards of NDBA, NDEA, NDMA and NPYR were also prepared separately for identification purposes. [11] Molecular docking studies Quantitative molecular docking studies were performed to determine the degree of N-nitosamine-S100A2 interaction and the affinity of the N-nitrosamines for the S100A2 protein. All computations were completed online at www. dockingserver. ...
... This leads to failure of the functional complex and its complementary derivatives, cell differentiation and node metastasis, and cell cycle progression, and eventually induces carcinogenesis. [10,11] ...
... Nitrosamines may act on the S100A2 protein, causing conformational changes in the protein that alter its normal functioning. [10,11] The current research is the culmination of a sequence of earlier studies. In 1950 Walker and Arvidsson [16] demonstrated that there was excess iron in the diet from iron utensils used during the preparation of traditional beer. ...
Article
Background. Before the 1930s, squamous cell carcinoma (SCC) of the oesophagus was almost unknown among black South Africans. From the 1930s the annual frequency rose. A dietary cause was sought, the staple diet of black people having changed from sorghum to maize (corn), with traditional beer being brewed from maize. Carcinogenic N-nitrosamines in traditional beer were suggested as a cause of SCC of the oesophagus, with Fusarium moniliforme, a corn saprophyte, thought to play a role. Objectives. To confirm the presence of N-nitrosamines in traditional beer and demonstrate a mechanism for the oncogenesis of oesophageal carcinoma. Methods. Analysis by high-performance liquid chromatography was conducted for the identification of nitrosamines in traditional beer samples, and molecular docking studies were employed to predict the affinity between N-nitrosamines and the S100A2 protein. Results. Carcinogenic N-nitrosamines were identified in all six samples of traditional beer examined (N=18 analyses), and docking studies confirmed a high affinity of the nitrosamine N-nitrosopyrrolidone with the S100A2 protein. This may result in the altered expression of the S100A2 protein, leading to tumour progression and prognosis. Conclusion. It is suggested that carcinogenic N-nitrosamines in traditional beer are a major factor in the causation of SCC of the oesophagus in black South Africans. N-nitrosamines have been shown to produce cancer experimentally, but there has not been conclusive epidemiological evidence that N-nitrosamines are carcinogenic to humans. This study is the first to demonstrate the potential link between N-nitrosamines and a human tumour.
... Introduction: SARS-CoV2, first reported in December 2019 in Wuhan as COVID-19 causing respiratory illness, rapidly evolved into a pandemic owing to its very high infectivity. There is insufficient evidence about if and how smoking affects the risk of COVID-19 infection, and the reports on whether smoking increases or reduces the risk of respiratory infections, are Jamal et al.; JPRI, 33(22B): 12-21, 2021; Article no.JPRI.66711 13 contradictory. ...
... Grid box to cover spike protein's target site was set to 40x40x40 Å in X, Y, and Z coordinate of a grid point with the values of 174.72, 247.475, and 219.195 respectively, and the values for a grid box to cover ACE2 target site was 60x60x60 Å followed by X, Y and Z coordinate of a grid point with the values of 80.401, 70.616 and 45.36 respectively for grid center with the default value of grid points spacing 0.375 Å. Lamarckian Genetic Algorithm (LGA) scoring function was used to perform ligand-receptor docking calculations [14]. The default LGA (10 runs) parameters mainly population size (ga_pop_size), energy evaluations (ga_num_generation), mutation rate, crossover rate and step size were set to 150, 2500000, 27000, 0.02, 0.8 and 0.2 Å, respectively [15]. After the execution of docking steps, the obtained 3D conformations of nicotine/metabolites-proteins complexes were generated for further analysis. ...
... Introduction: SARS-CoV2, first reported in December 2019 in Wuhan as COVID-19 causing respiratory illness, rapidly evolved into a pandemic owing to its very high infectivity. There is insufficient evidence about if and how smoking affects the risk of COVID-19 infection, and the reports on whether smoking increases or reduces the risk of respiratory infections, are Jamal et al.; JPRI, 33(22B): 12-21, 2021; Article no.JPRI.66711 13 contradictory. ...
... Grid box to cover spike protein's target site was set to 40x40x40 Å in X, Y, and Z coordinate of a grid point with the values of 174.72, 247.475, and 219.195 respectively, and the values for a grid box to cover ACE2 target site was 60x60x60 Å followed by X, Y and Z coordinate of a grid point with the values of 80.401, 70.616 and 45.36 respectively for grid center with the default value of grid points spacing 0.375 Å. Lamarckian Genetic Algorithm (LGA) scoring function was used to perform ligand-receptor docking calculations [14]. The default LGA (10 runs) parameters mainly population size (ga_pop_size), energy evaluations (ga_num_generation), mutation rate, crossover rate and step size were set to 150, 2500000, 27000, 0.02, 0.8 and 0.2 Å, respectively [15]. After the execution of docking steps, the obtained 3D conformations of nicotine/metabolites-proteins complexes were generated for further analysis. ...
Article
Full-text available
Introduction: SARS-CoV2, first reported in December 2019 in Wuhan as COVID-19 causing respiratory illness, rapidly evolved into a pandemic owing to its very high infectivity. There is insufficient evidence about if and how smoking affects the risk of COVID-19 infection, and the reports on whether smoking increases or reduces the risk of respiratory infections, are contradictory. Therefore, the current study was designed to determine the effects of nicotine consumption on the infectivity of COVID-19. Methods: We performed in silico computer simulation-based study. The structures of SARS-CoV2spike ectodomain, and its receptor ACE2, were obtained from PDB. The structure of nicotine and its metabolites NNK and NNAL were obtained from the PubChem chemical database. After optimization, they were interacted using AutoDock 4.2, to see the effect of nicotine, NNK, or NNAL presence on the docking of viral spike protein to its receptor ACE2. Results: ACE2 vs spike protein interaction results were used as a control (ZDOCK score 1498.484, with four hydrogen bonds). The NNK+ACE2 vs spike protein docking formed 10 hydrogen bonds with the highest ZDOCK score of 1515.564. NNAL+ ACE2 vs spike protein interaction formed eleven hydrogen bonds with the ZDOCK score of 1499.371. Nicotine+ACE2 vs spike protein docking showed the lowest ZDOCK score of 1496.302 and formed 8 hydrogen bonds. Whereas, NNK+spike vs ACE2 interaction had a ZDOCK score of 1498.490 and formed eight hydrogen bonds. NNAL+spike vs ACE2 docking formed eleven hydrogen bonds with a ZDOCK score of 1498.482. And Nicotine+spike vs ACE2 interaction showed a ZDOCK score of 1498.488 and formed 9 hydrogen bonds. Conclusions: The binding of nicotine to either spike of virus or its receptor ACE2 is not affecting the viral docking with the receptor. But binding of NNK, a metabolite of nicotine, is facilitating the viral docking with its receptor indicating that smoking may increase the risk of COVID-19 infection.
... The default LGA parameters population size (ga_pop_size), energy evaluations (ga_num_generation), mutation rate, crossover rate and step size were set to 150, 2500000, 27000, 0.02, 0.8 and 0.2 Å, respectively. Finally, 10 LGA runs were executed (Morris et al., 2009;Haneef et al., 2014). After LGA execution, the spike protein and drug compounds complexes were prepared after extraction of the total binding energy and inhibition constants data from the AutoDock output files. ...
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Colorectal cancer is a life-threatening malignancy of high invasive death worldwide, despite improvements in conventional chemotherapy. Hence, there is an urgent need for new and effective anti-colorectal chemotherapeutic agents. Kaempferitrin can inhibit colon cancer cell viability, apoptotic induction, and reactive oxygen species (ROS) generation in a concentration-dependent way. Kaempferitrin had a cytotoxic and anti-proliferative effect on HT-29 colorectal cancer cells at a dosage of 30 µM, regulate apoptotic cell death, cell proliferation, and the cellular antioxidant system. Hyperoxia, cellular stress, ROS, and oxidative damage are the regulators of cancer cells. Increased sensitivity towards oxidative stress is caused by a change in redox state caused by enhanced ROS generation. In HT-29 cells, kaempferitrin also activated caspase-3 and enhanced the ratios of cleaved PARP protein expression, triggering caspase-dependent death. These kaempferitrin-induced processes also correlate with PI3/AKT, implying downstream targeting of the cancer cell pathway. These findings suggest that kaempferitrin could be a promising therapeutic agent for the treatment of CRC, since it inhibits CRC cell proliferation and induces apoptosis via the PI3K/AKT signalling pathway
... The default LGA parameters population size (ga_pop_size), energy evaluations (ga_num_generation), mutation rate, crossover rate and step size were set to 150, 2500000, 27000, 0.02, 0.8 and 0.2 Å, respectively. Finally, 10 LGA runs were executed (Morris et al., 2009;Haneef et al., 2014). After LGA execution, the spike protein and drug compounds complexes were prepared after extraction of the total binding energy and inhibition constants data from the AutoDock output files. ...
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Full-text available
The human-to-human transmitted respiratory illness in COVID-19 affected by the pathogenic Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), which appeared in the last of December 2019 in Wuhan, China, and rapidly spread in many countries. Thereon, based on the urgent need for therapeutic molecules, we conducted in silicobased docking and simulation molecular interaction studies onrepurposing drugs, targeting SARS-CoV-2 spike protein. Further, the best binding energy of doxorubicininteracting with virus spike protein (PDB: 6VYB) was observed to be 6.38kcal/moland it was followed by exemestane and gatifloxacin. The molecular simulation dynamics analysis of doxorubicin, Reference Mean Square Deviation (RMSD), Root Mean Square fluctuation (RMSF), Radius of Gyration (Rg), and for-mation of hydrogen bonds plot interpretation suggested, a significant deviation and fluctuation of Doxorubicin-Spike RBD complex during the whole simulation period. The Rg analysis has stated that the Doxorubicin-Spike RBD complex was stable during 15,000–35,000 ps MDS. The results have suggested that doxorubicin could inhibit the virus spike protein and prevent the access of the SARS-CoV-2to the host cell. Thus,in-vitro/in-vivo research on these drugs could be advantageous to evaluate significant molecules that control the COVID-19 disease
... Finally, 10 LGA runs were executed (Morris et al. 2009;Haneef et al. 2014). After LGA execution, the spike protein and drug compounds complexes were prepared after extraction of the total binding energy and inhibition constants data from the AutoDock output files. ...
Article
Full-text available
The human-to-human transmitted respiratory illness in COVID-19 affected by the pathogenic Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), which appeared in the last of December 2019 in Wuhan, China, and rapidly spread in many countries. Thereon, based on the urgent need for therapeutic molecules, we conducted in silico based docking and simulation molecular interaction studies on repurposing drugs, targeting SARS-CoV-2 spike protein. Further, the best binding energy of doxorubicin interacting with virus spike protein (PDB: 6VYB) was observed to be -6.38 kcal/mol and it was followed by exemestane and gatifloxacin. The molecular simulation dynamics analysis of doxorubicin, Reference Mean Square Deviation (RMSD), Root Mean Square fluctuation (RMSF), Radius of Gyration (Rg), and formation of hydrogen bonds plot interpretation suggested, a significant deviation and fluctuation of Doxorubicin-Spike RBD complex during the whole simulation period. The Rg analysis has stated that the Doxorubicin-Spike RBD complex was stable during 15000-35000ps MDS. The results have suggested that doxorubicin could inhibit the virus spike protein and prevent the access of the SARS-CoV-2 to the host cell. Thus, in-vitro/in-vivo research on these drugs could be advantageous to evaluate significant molecules that control the COVID-19 disease.
... Although molecular docking studies have been previously conducted on CDK, to our knowledge this is the first time that an extensive protein-ligand docking simulation and scoring function development have been carried out focused exclusively on CDK crystallographic structures [8][9][10][11][12][13][14][15][16][17]. The main goal of the present work is to integrate the structural and binding affinity data to build scoring functions targeted to the CDK system. ...
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Prolonged cigarette smoking causes even more deaths from other diseases than from lung cancer. In developed countries, the absolute age-sex-specific lung cancer rates can be used to indicate the approximate proportions due to tobacco of deaths not only from lung cancer itself but also, indirectly, from vascular disease and from various other categories of disease. Even in the absence of direct information on smoking histories, therefore, national mortality from tobacco can be estimated approximately just from the disease mortality statistics that are available from all major developed countries for about 1985 (and for 1975 and so, by extrapolation, for 1995). The relation between the absolute excess of lung cancer and the proportional excess of other diseases can only be approximate, and so as not to overestimate the effects of tobacco it has been taken to be only half that suggested by a recent large prospective study of smoking and death among one million Americans. Application of such methods indicates that, in developed countries alone, annual deaths from smoking number about 0.9 million in 1965, 1.3 million in 1975, 1.7 million in 1985, and 2.1 million in 1995 (and hence about 21 million in the decade 1990-99: 5-6 million European Community, 5-6 million USA, 5 million former USSR, 3 million Eastern and other Europe, and 2 million elsewhere, [ie, Australia, Canada, Japan, and New Zealand]). More than half these deaths will be at 35-69 years of age: during the 1990s tobacco will in developed countries cause about 30% of all deaths at 35-69 (making it the largest single cause of premature death) plus about 14% of all at older ages. Those killed at older ages are on average already almost 80 years old, however, and might have died soon anyway, but those killed by tobacco at 35-69 lose an average of about 23 years of life. At present just under 20% of all deaths in developed countries are attributed to tobacco, but this percentage is still rising, suggesting that on current smoking patterns just over 20% of those now living in developed countries will eventually be killed by tobacco (ie, about a quarter of a billion, out of a current total population of just under one and a quarter billion).