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Mol2Net, 2015, 1(Section B), pages 1-19, Proceedings 1
http://sciforum.net/conference/mol2net-1
Mol2Net
Homology Modeling, Molecular Dynamic Simulation and
in Silico Screening of Activator for the Intensification of
Human Sirtuin Type 1 (SIRT1) by novel 1, 3, 4-
Thiadiazole Derivatives-A Potential Antiaging Approach
Sudipta Saha, Amit Rai, Mahendra Singh, Vinit Raj *, Durgesh Kumar and Anil Kumar Sahdev
Department of Pharmaceutical Sciences, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Rai
Bareli Road, Lucknow 226025
* Author to whom correspondence should be addressed; E-Mail: raj.vinit24@gmail.com;
Tel.: +918859383897.
Published: 4 December 2015
Abstract: Sirtuin type-1(SIRT1) is a regulator of various biosynthetic pathways via activation of
peroxisome proliferator-activated receptor-γ and interacting with adenosine-mono-phosphate
kinase. SIRT1 is the important target for various neurodegenerative, cancer and metabolic
disorders as well as aging medicine. Keeping in view of the above fact, we considered novel
1,3,4-thiadiazole derivatives series for SIRT1 screening, which was performed through virtual
screening, homological modeling, docking and computational studies. On the basis of available
molecular structure in protein data bank of SIRT1 protein, we calculated the interaction energy
designed molecules. The interaction energy of designed compound VR3 closely better than
resveratrol (̴ 6.4 kcal/mol). Among of them the VR 3 shown the best conformation fitting
stability in the binding site of SIRT1 predicted by MD (Molecular dynamics) simulation for
2.5ns. Therefore, the designed compounds have good binding affinities to SIRT1 target, would
serve better lead compound for antiaging screening for future drug design perspective.
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.
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Keywords Homology modeling, Molecular docking, ADMET prediction, actives substrate
binding domain of SIRT1, Antiaging
1. Introduction
SciForum
Mol2Net, 2015, 1(Section B), pages 1-19, Proceedings 2
http://sciforum.net/conference/mol2net-1
In the last few years, sirtuin (SIRT) has
become a large attention to scientific
communities for developing lead optimization.
Interesting in this protein family is to its crucial
role in genomic instability, telomere attrition,
epigenetic alterations, loss of proteostasis,
deregulated nutrient sensing and mitochondrial
dysfunction (López-Otín, Blasco, Partridge,
Serrano, & Kroemer, 2013). SIRT1
downregulates pro-inflammatory factors like
p53[2-3] and nuclear factor-kappa B (NF-κB),
whereas upregulates peroxisome proliferator-
activated receptor-gamma coactivator 1 alpha
(PGC-1α)(Amat et al., 2009; Wareski et al.,
2009) and forkhead box class O transcription
factors (FOXOs). SIRT 1 is the main target for
diverse pharmacological properties including
neurodegenerative disorders, cancer and
metabolic disorders as well as aging medicine
(Pallàs et al., 2009).
Because of that, the discovery of SIRT1
activator is an important target for drug
discovery. In this study, we focused on
developing a new scaffold which can be able to
have potent activation effect. During searching
of new lead for SIRT1 target, we used 1, 3, 4-
thiadiazole moiety as it has one hydrogen
binding domain and two-electron donor system.
The previous literature survey suggested that 1,
3, 4-thiadiazole is the important pharmacophore
than other isomers for binding to the receptor and
it has multiple pharmacological actions as well.
This ring exhibited antimicrobial (Demirbas,
Karaoglu, Demirbas, & Sancak, 2004;
Karegoudar et al., 2008), anticancer (Chou et al.,
2003), antanxiety, anti-depressant (Clerici et al.,
2001), anti-oxidant properties (Martinez et al.,
1999), anticonvulsant activity (Yusuf, Khan,
Khan, & Ahmed, 2013) and antitubercular
activities (Alegaon et al., 2012). Thiadiazole ring
expressed diverse biological activities, might be
due to the presence of =N-C-S moiety (Oruç,
Rollas, Kandemirli, Shvets, & Dimoglo, 2004).
In view of the above fact, the question arose
whether 1, 3, 4-thiadiazole might be an important
activator for SIRT1 target. To prove this
hypothesis, homology modeling was performed
using one or more known protein structures that
are resembling to the structural sequence of
SIRT1. Later, all these sequences collapsed
together to reach the desired template sequence.
Finally, docking studies was carried out between
newly designed protein and prepared ligand to
get interaction energy.
After that, the pharmacokinetics parameters
(ADME, BBB and toxicity) were also measured
with that designed compound to rule out whether
these compounds might be suitable for in vivo
biological system. We hypothesized that these
compounds may be a lead target for antiaging as
a SIRT1 agonist and also suitable for in vivo
screening in the future.
Material and methods
In this present study, National Centre for
Biotechnology Information (NCBI)
(http://www.ncbi.nlm.nih.gov) and Protein Data
Bank (PDB) (http://www.rcsb.org) were used as
chemical sources. The software which was used
to experiment, tabulated in table (1). Resveratrol,
VR1, VR2, and VR3 structures were drawn
through Chemdraw Ultra 10.0 figure (1) and
their geometry was optimized six times with
Gauss view 5.0. However, these structures
represent a minimum energy optimization,
selected for in-silico study and 3-D structure of
sirtuin type-1 Protein structure was not available
in the PDB and NCBI Protein database. The 3-D
structure of the protein was prepared through the
run blast of protein on the database of PDB that
had shown the description of sequences
Mol2Net, 2015, 1(Section B), pages 1-19, Proceedings 3
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producing significant alignments of query cover
and identity (37 and 97%, respectively). Finally,
prepared 3-D protein structure was used for
homological modeling figure (2).
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Table 1 Softwares used for modeling and their Purposes
Softwares
Purposes
Chemdraw Ultra 10.0 and open
babel GUI
For drawing the chemical
structure and convert into PDB
format
Argus Lab software,Gauss view
5.0
For optimizing the geometry
of derivatives
1: 3-D pssm sever, phyre 2.0
server, easy moduller 2.0, swiss
pdb viewer (spdbv 4.1.0),
Modbase server and saves server
plot.
For the homological modeling
of Sirtuin type 1
Autodock 4.0,Discovery studio
and autodock vina
For docking studies
Molinspiron software toolkit,
Med Chem Designer and Lasar
toxicity prediction service
For characterization of the
derivatives
Mol2Net, 2015, 1(Section B), pages 1-19, Proceedings 5
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Amino acid sequences of Sirtuin type 1
Protein
GenBank: AAD40849.2
GenPept Graphics
>gi|7555471|gb|AAD40849.2|AF083106_1
sirtuin type 1 [Homo sapiens]
MADEAALALQPGGSPSAAGADREAASSPA
GEPLRKRPRRDGPGLERSPGEPGGAAPERE
VPAAARGCPGAAAAALWREAEAEAAAAG
GEQEAQATAAAGEGDNGPGLQGPSREPPL
ADNLYDEDDDDEGEEEEEAAAAAIGYRDN
LLFGDEIITNGFHSCESDEEDRASHASSSDW
TPRPRIGPYTFVQQHLMIGTDPRTILKDLLP
ETIPPPELDDMTLWQIVINILSEPPKRKKRK
DINTIEDAVKLLQECKKIIVLTGAGVSVSCG
IPDFRSRDGIYARLAVDFPDLPDPQAMFDIE
YFRKDPRPFFKFAKEIYPGQFQPSLCHKFIA
LSDKEGKLLRNYTQNIDTLEQVAGIQRIIQC
HGSFATASCLICKYKVDCEAVRGDIFNQVV
PRCPRCPADEPLAIMKPEIVFFGENLPEQFH
RAMKYDKDEVDLLIVIGSSLKVRPVALIPSS
IPHEVPQILINREPLPHLHFDVELLGDCDVII
NELCHRLGGEYAKLCCNPVKLSEITEKPPR
TQKELAYLSELPPTPLHVSEDSSSPERTSPPD
SSVIVTLLDQAAKSNDDLDVSESKGCMEEK
PQEVQTSRNVESIAEQMENPDLKNVGSSTG
EKNERTSVAGTVRKCWPNRVAKEQISRRL
DGNQYLFLPPNRYIFHGAEVYSDSEDDVLS
SSSCGSNSDSGTCQSPSLEEPMEDESEIEEFY
NGLEDEPDVPERAGGAGFGTDGDDQEAIN
EAISVKQEVTDMNYPSNKS
Homology modeling
SIRT1 was modeled by using Easy Modeler
software and self-developed commends. The
amino acid sequence of human SIRT1 was
retrieved from Gen Bank (accession number:
AF083106.2) in NCBI (Frye, 1999). It consists
of 747 amino acids, among which residues 250-
500 belong to SIRT1. The SIRT1 was then
subjected to a PSI-BLAST search in order to
identify the homologous proteins. 3-D pssm and
phyre 2.0 servers were shown to produce a
number of potential templates and identify better
templates for sequences, were observed through
distant relationships to any solved structure. Easy
modeler software and self-developed command
was used to generate the nine probabilities of
query. Ramachandran plot checked and viewed
in the swiss pdb reviewer 4.1.0.
Validation of the modeled structure
The backbone conformation of the modeled
structure was calculated by analyzing the phi (Φ)
and psi (ψ) torsion angles using Saves server and
evaluations of the modeled 3-D structure of
sirtuin was performed using PROCHECK by
calculating the Ramachandran plot. This plot
represented the distribution of the Φ and ψ angles
for the amino acid residues.
Docking studies
Docking study of desiged compounds were
performed with anti aging, molecular targets,
namely SIRT1 by using Autodock 4.0 along with
Autodock Vina. Before the docking study, we
identified the active site domain with the help of
Dog P/Castp active Site recognizer server of
protein, wherein the legend showed the best
configuration figure (3). Keeping in view active
site amino acid sequence, Grid box was set.
Their binding affinity (kcal/mol) and count of
probable hydrogen bonds also evaluated in the
similar experiment.
Prediction of pharmacokinetic properties
The designed compounds assessed for
pharmacokinetic properties through medchem
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designer software. Later, the pharmacokinetic
parameters of the lead molecules analyzed,
including their absorption, distribution,
metabolism and excretion (ADME), using
Molinspiration property online calculator
(Bratislava). The percentage of absorption
(%ABS) was calculated using TPSA by the
following formula: % ABS = 109-
(0.345×TPSA) (Zhao et al., 2002). Oral
bioavailability and blood brain barrier (BBB)
penetration of all compounds have performed by
the ACD/ Lab-I online server.
Bioactivity prediction and Toxological
comparative studies
For prediction of bioactivity and toxicological
properties of titles compounds evaluated by
Molinspiration property online calculator and the
Lasar toxicity prediction server. The designed
derivatives and original drug bioactivity
predictions had been compared along with some
selected activity GPCR (G-Protein coupled
receptor).
MD Simulation study
Molecular dynamics simulation has been
performed for the higher affinity complex with
the help of Yasara tools. Hence, the complex
placed in a cubic box and filled with solvent
(HOH) by applying AMBER 99 force field,
temperature 298 K that controlled through
rescale velocities and pressure reached 1.000 bar
these parameters were applied in order to check
the stability of their complex. figure (4) shows
the solvated structure when visualized in MD.
After energy minimization of the solvated and
electroneutral system its Potential Energy had
been analyzed and plotted by using Sigma Plot
11.0 tools. MD simulation was run for 2.5ns. The
following parameter evaluated, including: (i)
Complex binding energy vs time which indicated
that complex stability under the MD simulation
figure(5), (ii) Potential energy of complex with
respective time figure (6) and (iii) Average
RMSD (Root Mean Square Deviation) graph
which indicated convergence of the simulated
structure towards an equilibrium state with
respect to a reference structure (starting
structure) figure (7).
Results and discussion
Homology modeling of SIRT1 and its
evaluation
Before going to interaction energy analysis
through docking studies, it is necessary to
prepare an amino acid sequence of targeted
SIRT1 in our experiment. Homology modeling
and database searching are the key essential part
for lead optimization. Total three molecules were
used for this study and resveratrol were used as
positive control.
For homology modeling, 3-D pssm and phyre 2.0
produced a larger number of potential templates.
The selected templates were LJ8F, LMA3, LS5P,
2RCR, LQ2W, LICI, UEK, LRIA, LETP and
LOGY with identity 39, 31, 28, 21, 18, 18, 17,
19, 16 and 16%, respectively. These best
identical templates were downloaded from the
protein data bank (PDB) with consideration of
the x-ray diffraction and resolution (R). R value
was within the range (not more than 3.0 A0, and
0.5 obs). Again, Easy modeler software and self-
developed commend were used to generate the
nine probabilities of query or model. The best
dope score was selected which resided with
phi\psi out of core regions. Later these data were
checked through Ramachandran plot and viewed
Mol2Net, 2015, 1(Section B), pages 1-19, Proceedings 7
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in the swiss pdb reviewer 4.1.0. Very few
numbers of amino acid laid outside the core
region. Therefore, loop modeling was done by
the mod loop server and checked the output
through the Ramachandran plot.
Validation of the modeled structure
The modeled structure of SIRT1 was calculated
by analyzing the phi (Φ) and psi (ψ) torsion
angles using the Saves server software and
evaluations of the modeled 3D structure of
certain were performed using PROCHECK by
calculating the Ramachandran plot figure (8) and
table (2). The percentage Φ and ψ angles were
92.3% of core residues, whereas this percentage
was 0.2% for the disallowed residues.
Docking studies
Docking study of designing compounds was
performed with antaging molecular targets
SIRT1. We detected the active site domain with
the help of DogP active Site recognizer server of
protein where the legend showed the best
configuration. Later, Grid box was set according
to an active site sequence of amino acid. Their
binding affinity (kcal/mol) and count of probable
hydrogen bonds were evaluated table (3) through
docking studies. Docking images of resveratrol,
VR1, VR2 and VR3 with the target receptors was
shown in figure (9). All Compounds exhibited
good binding properties with SIRT1 receptor
(affinity value -6.4, -7.0, -7.3 and -7.7 kcal/mol
and 1, 0, 0, and 0, H-bonds, respectively for
resveratrol, VR1, VR2, and VR3). Addition, the
interaction of ligand to the receptor has
concluded that PRO 419, GLU410 and VAL 412
common essential amino acids, which may be
involved in enhancing the efficacy of SIRT1.
Hence, this observation could be attributed as
potential antigens with SIRT1 mimetic/facilitator
mode of action.
Prediction of ADME properties
The ADME properties of the designed
compounds were assessed by evaluating their
physicochemical properties using the medchem
designer software. Their molecular weights were
<500 Da; they had <5 hydrogen bond donors and
<10 hydrogen bond acceptors, and logP values of
<5 table (4). These properties are within the
acceptable range of Lipinski’s rule of five.
Furthermore, the pharmacokinetic parameters of
the lead molecules were analyzed, including their
ADME using Molinspiration property online
calculator and Lasar toxicity prediction server.
For the designed compounds, the partition
coefficient (QPlogPo/w) and water solubility
(QPlogS) values, the %ABS for the compounds
ranged from approximately 80 to 95%. These
pharmacokinetic parameters are well within the
acceptable range defined for human use, thereby
indicating their potential as drug-like molecules.
All designed compounds had shown the better
BBB penetration power table (5) and the CNS
active properties of all compounds were shown
in the graph figure (10). Oral bioavailability of
all designed compounds were calculated
theoretically which lied in accepting a range
(more than 70%). BBB penetration and oral
bioavailability essential key for the
pharmacokinetic profile of the compound to
enhance the pharmacological activity. These all
theoretically parameter of the designed
compounds supported our hypothesis.
MlogP, Moriguchi estimation of logP. S+ log P
logP calculated using Simulations Plus’ highly
accurate internal model; S+logD, logD at user-
specified pH (default 7.4), based on S+logP;n-
OHNH donor, Number of Hydrogen bond donor
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protons; M_NO, Total number of Nitrogen and
Oxygen atoms; T_PSA, Topological polar
surface area in square angstroms; Rule Of Five,
Lipinski’s Rule of Five: a score indicating the
number of potential problems a structure might
have with passive oral absorption;miLog P,
logarithm of compound partition coefficient
between n-octanol and water; log D, logarithm of
compound distribution coefficient; n-ROTB,
number of rotatable bonds; MV, molecular
volume; n-ON acceptor, number of Hydrogen
bond acceptor protons.
Bioactivity prediction and Toxological
comparative studies
In this study, for prediction of bioactivity and
toxicological properties of titled compounds was
also determined in our study. From all calculated
parameters, it can be observed that all titled
compounds expressed less affinity to GPCR (G-
Protein coupled receptor) ligand, ion channel
modulator, kinase inhibitor, nuclear receptor
ligand, protease enzyme inhibitor and the
toxicological as compared to resveratrol. All this
investigation suggested that activation of SIRT1
through our compounds has great importance for
providing neuroprotection in various
neurodegenerative disorders including the
temporal lobe epilepsy (TLE) (Shetty, 2011).
The Bioactivity and Toxological data are given
in table (6) and (7).
Computational details
A computational study for prediction of docking,
energy minimization and ADMET properties of
title compounds was performed. From all these
parameters, it can be observed that all titled
compounds exhibited a good ADMET and BBB
properties. None of the compounds violated
Lipinski,s parameters, making them potentially
promising agents for antiaging durg. From the
MD simulation study of compound VR 3 shown
the stability of complex at 2.3ns with average
energy -388.981 kcal/mol. In addition, the
complex didn't show more fluctuation in
potential energy in respectively with time.
Whereas, binding energy of compound at 0 ps
time was founded -189.469 Kcal/mol which
increased -645.930 kcal/mol at 700 ps under MD
simulation, whereas in the figure (5) the complex
exhibited the fluctuation before the 800ps. Later,
the complex shows the stability near 800 ps with
-400(kcal/mol) compound binding energy.
However, the RMSD of the backbone structure
shown the stability near 200 ps. These findings
suggested that the complex structure of VR 3
with SIRT1 shown the best stable fitting
(affinity) in the MD simulation study.
.
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Figure 3: Amino acid present in the active site are labeled with green
.
.
Figure 4: Shows solvated structure visualized in MD simulation. Here red color represents the solvent.
graph after Energy minimization step.Protein shown in greenish-yellow-red-blue color and ligand
shown as white-sky in blue color.
.
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Figure 5: Shown the Time vs compound binding energy, which indicated that the energy stabilized at
1000 ps
Root mean square distance(RMSD) of the backbone structure
Time (ps)
0200 400 600 800 1000 1200
RMSD (nm)
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Time (ps) v s RMSD (nm)
Figure 6: Root mean square distance (RMSD) of the backbone of the structure simulated over 2.5
nanoseconds.
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Figure 7: Shows Time (1200ps) Vs Potential Energy which indicated that very small fluctuation
observed
Figure 8: Protein structure validation: (A) Modeled structure of the SIRT1obtained from Easy
Modeler. The structure is shown in secondary structure mode using Pymol. (B) Ramachandran plot for
the modeled SIRT1. The most favored regions are colored red; additional allowed, generously allowed
and disallowed regions are shown as yellow, light yellow and white fields, respectively.
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Table 2: Ramachandran plot statistics for the 3D model of SIRT1, calculated using PROCHECK
Parameter
Value
Core %
92.3
Allowed %
7.4
Disallowed %
0.2
General %
0.2
.
Table 3: Binding affinities of standard and designed compounds
Ligand
Receptor
Affinity(
Kcal/Mol)
Amino acids involved in interactions
H- bonds
Pi
bonds
Resveratrol
Sirtuin
type1
(SIRT1)
-6.4
GLN A 361, GLY A 364, SER A 365,
ALA A 367, LYS A 408, GLU A 410, ILE
A 411, VAL A 412, PHE A 413, GLU A
416, ASN A 417, LEU A 418, PRO A 419,
GLN A 421
1
4
VR1
Sirtuin
type1
(SIRT1)
-7.0
ARG A 466, ASP A 481, GLN B 641, TYR
B 642, LEU B 643, ILE B 651, PHE B 652,
HIS B 653, GLY B 654, ALA B 655, GLU
B 656,TYR B 658, SER B 659
0
3
VR2
Sirtuin
type1
(SIRT1)
-7.3
ILE A 360, GLN A 361, GLY A 364, LYS
A 408, GLU A 410, ILE A 411, VAL A
412, GLU A 416, ASN A 417, PRO A 419,
GLN A 421
0
7
VR3
Sirtuin
type1
(SIRT1)
-7.7
ILE A 359, ILE A 360, GLN A 361, GLY
A 364, LYS A 408, GLU A 410, ILE A
411, VAL A 412, GLU A 416, ASN A
417, PRO A 419, GLN A 421, PHE A 422
0
7
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Figure 9: Docking images (a) Resveratrol, (b) VR 1, (c) VR 2 and (d) VR 3 with SIRT1, the green
color dotted line shows hydrogen bonding and yellowish, light blue or whitish dotted line show Pi
Donor, Acceptor and Alkyl bond respectively with amino acids involved in binding poses.
.
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Table 4. The theoretical ADME properties of resveratrol and all designed 1,3,4-thiadiazole
derivatives.
S. No.
Rule
Resveratrol
VR 1
VR 2
VR 3
1.
S+ log P
−2.0 to
6.5)
2.907
4.679
2.892
2.454
2.
S +log D
−
2.897
4.679
2.892
2.454
3.
M log P
−
2.402
3.987
2.917
2.585
4.
T_PSA
−
60.690
47.370
64.160
67.240
5.
n-OHNH donor
<5
3.000
0.000
2.000
1.000
6.
M_NO.
−
3.000
4.000
4.000
5.000
7.
Rule of 5
≤ 1
0.000
0.000
0.000
0.000
8.
%ABS (% of
absorption)
_
88.07
92.66
86.87
85.81
9.
MV
−
206.922
329.474
265.301
253.064
10.
n-ON acceptor
<10
3
4
4
5
11.
n-ROTB
−
2
5
3
2
12.
M. Wt.
< 500
228.249
371.465
298.343
306.347
.
Table 5: BBB penetration of Resveratrol, VR1, VR2 and VR3
Parameter
Resveratrol
VR1
VR2
VR3
Rate of blood penetration
Log PS
-1.6
-1.1
-1.1
-1.1
Extent of brain penetration
Log PB
0.37
-0.15
-0.15
-0.15
Brain/plasma equilibration rate
Log (PS*FU brain)
-2.5
-2.9
-3.0
-3.0
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Figure 10: The BBB penetration power of the all active compounds (a, b, c, d, represents the
following drug profile graph as resveratrol, VR1, VR2 and VR3 respectively). All graphs represent
two region 1.CNS inactive 2.CNS active
Table 6: Score of bioactivity prediction of resveratrol and thiadiazole derivatives
S.No.
Receptors
Resveratrol
VR 1
VR 2
VR 3
1.
GPCR ligand
-0.20
-0.37
-0.46
-0.48
2.
Ion channel Modulator
0.02
-0.68
-0.79
-0.90
3.
Kinase inhibitor
-0.20
-0.11
-0.09
0.04
4.
Nuclear receptor ligand
0.01
-0.16
-0.39
-0.74
5.
Protease inhibitor
-0.42
-0.39
-0.55
-0.94
6.
Enzyme inhibitor
0.02
-0.24
-0.25
-0.32
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Table 7: Topological comparative studies of resveratrol and thiadiazole derivatives
S.No.
DSSTox toxicity origin
Resveratrol
VR 1
VR 2
VR 3
1.
DSSTox Carcinogenic Potency DBS
MultiCellCall: non-carcinogen
0.0127
0.0136
0.0123
0.00723
2.
DSSTox Carcinogenic Potency DBS
Mutagenicity: non-mutagenic
0.162
0.101
0.00678
0.00723
3.
DSSTox Carcinogenic Potency DBS Rat: non-
carcinogen
0.0517
0.0614
0.0417
0.0495
4.
Kazius-Bursi Salmonella mutagenicity: non-
mutagenic
0.089
0.0335
0.0534
0.0419
5.
FDA v3b Maximum Recommended Daily
Dose mmol: 0.0152722115276765
0.136
0.106
0.0834
0.0884
6.
DSSTox Carcinogenic Potency DBS
SingleCellCall: non-carcinogen
0.011
0.0463
0.0126
0.0131
7.
EPA v4b Fathead Minnow Acute Toxicity
LC50_mmol: 0.00359162218026281
0.207
0.184
0.203
0.19
8.
DSSTox ISSCAN v3a Canc: carcinogen
0.121
0.0869
0.243
0.000
9.
DSSTox Carcinogenic Potency DBS Hamster:
non-carcinogen
0.137
0.237
0.179
0.131
10.
DSSTox Carcinogenic Potency DBS Mouse:
non-carcinogen
0.0661
0.0146
0.105
0.0692
Animals 2015, 5, 1-x manuscripts; doi:10.3390/ani50x000x
Conclusion
Homology modeling approach was used in our study to developed 3D structure of SIRT1. According
to exist literature and analysis of the results from the our research of the homology modeling, docking
and computational study indicated that the designed novel 1,3,4-thiadiazole derivatives has a potent
activation effect on SIRT1 receptor at potential antigen target as well as treatment of a number of life
threading diseases. All compounds displayed significant binding affinity compared with resveratrol.
Among of them compound VR 3 has shown significant efficacy as well as the complex stability in the
MD simulation. The other parameters like toxicity, ADME, oral bioavailability and BBB penetration
of all designed compounds showed similar trends. The docking study data strongly support the
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assumption that SIRT1 may be involved in antiaging activity of 1, 3, 4-thiadiazole derivatives.
However, the interaction of compounds with the receptor has concluded that PRO 419, GLU410 and
VAL 412 common essential amino acids those may be involved in enhancing the efficacy of SIRT1.
Thus, all data compared with the Resveratrol drug supported our antiaging hypothesis as a SIRT1
agonist (Kelly, 2010) .Hence, this observation could be attributed that the compound VR3 among of
them as potential antiaging with SIRT1 mimetic/facilitator mode of action.
However, further studies, like synthesis, in-vivo evaluation and mechanism of action of these
compounds are necessary to support this hypothesis. These titled compounds emerged as a lead for
SIRT1 drug screening for future.
Conflict of interest statement
We wish to confirm that there are no known conflicts of interest associated with this publication.
Acknowledgements
The authors would like to express their gratitude to Babasaheb Bhimrao Ambedkar University (a
Central University) Lucknow for providing the software and research data facilities.
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