Best possible natural ligands which were enlisted on NPACT website were screened ( aid of major drug likeness parameters - pkCSM) and docked with the 2OJG(Target protein) using autodock.

1. DRUG LIKENESS AND MOLECULAR
DOCKING ANALYSIS OF MAPK
INHIBITORS FROM THE NPACT
DATABASE
Department of Pharmacology
K.K. COLLEGE OF PHARMACY, CHENNAI 600122
Presented By
SAMSON RAJ Y SAVITHA C SANTHOSH M
Under the guidance of Prof. Dr. B. Premkumar, M. Pharm., Ph.D., Head of the Department

2. INTRODUCTION
2
Melanoma is identified as one of the most dangerous forms of the skin
tumor, having quick metastasizing, progression and a high burden of death.
Even though a significant figure of therapies has been established recently for the late-
stage melanoma cancer, this disease has not been defeated yet; resistance develops
through cancer heterogeneity
This study has been designed to explore the anticancer potential of NPACT ligands
against the Mitogen Activated Protein Kinase (MAPK) signaling in melanoma. The
hit compounds obtained in this study could play an important role in designing
personalized therapy against melanoma patients.

3. AIM
3
• To find the potential ligands for the paramount cancer type “Melanoma” (skin
cancer) with the target receptor being chosen from research articles and utilizing it
for computational screening of natural ligands submitted in NPACT site.
• In silico screening approaches has been applied to find the suitable ligand, which
can be treated for further studies.

4. OBJECTIVES
4
 Selection of the macromolecule
 Obtaining the 3D protein structure from protien data bank.
 Determination of grid specification (using Biovia discovery studio)
 Retrieval of natural ligands from NPACT site.
 Processing of ligands (using Marvin sketch).
 Analysis of ADMET properties (using pkCSM online tool).
 Performing docking (Autodock vina 1.2.3)
 Hierarchical determination of ligands.
 Tabulation and interpretation of results.

5. PLAN OF WORK
5

6. Determination of target receptor
6
• The transferase 2OJG [Mitogen-activated protein kinase 1] (UNIPORT ID: P28482)
was found one of the most commonly protein worked in Melanoma in correlation with
computational screening to a potential target protein.
• Additionally, 2OJG is single chain interacting protein and free from mutation, it is
more efficient to work.

7. Retrieval & Processing of protein
7
• The transferase Melanoma protein 2OJG is found to be submitted as crystal structure
of ERK2 (gene name) in complex with ‘19A’ and SO4.
• For effective docking additive molecules (19A, SO4, water) are removed from the
macromolecule and making the binding pocket available, by using Discovery studio-
Biovia.
• Required charge were added to the target protein (Kolmann charges) along with
addition of hydrogen bonds and pairing of polar bonds. Obtaining the protein as ready
to dock in PDBQT format.

8. Retrieval & Processing of ligands
8
• The NPACT ligand molecules were obtained and screened for potential ligands by
placing parameters such as Lipinski’s rule of five, Ghose filter, Veber filter,
Muegge Filter, Hepatotoxicity.
• Molecular descriptors were noted for the selected ligand molecules
• Ligand molecules were obtained from NPACT site in MOL2 format which was
converted to 3D structures in Marvin Sketch
• Followed by obtaining the ligands as PDBQT format using Auto dock Racoon
which included addition of torsion and charges to the selected ligands.

9. Analysis of ADMET properties
9
The sorted compounds were further analyzed for ADMET properties
• Absorption: Water solubility, Caco-2 permeability, Substrate of P glycoprotein,
• Distribution: Volume of distribution, Blood Brain Barrier permeability, CNS
permeability.
• Metabolism: CYP2D6 substrate, CYP3A4 substrate.
• Excretion: Total Clearance, Renal OCT2 substrate.
• Toxicity: AMES toxicity, Max. Tolerated dose (human), hERG I inhibitor, hERG II
inhibitor, Oral Rat Acute Toxicity, Hepatoxicity

10. Performing docking
10
• The ready to dock files (i.e. target protein and ligands in PDBQT format) were utilized
for docking.
• Grid configurations are determined with the aid of Native ligand of submitted protein
grid size was set to 60 x 60 x 60 x y z points with grid spacing (0.375) and the grid
center was designated at dimensions (x y z as 13.88, 13.865, 41.85 respectively)
• Ligand were bound with the protein with the aid of AutoDock vina 1.2.3.

11. Results
11
• Out of 1574 compounds 464 ligands were chosen molecular docking was carried out on
batch basis using AutoDock vina 1.2.3., Compounds were ranked on basis of least
binding energy.
• All the candidate compounds were subjected to pkCSM online tool to assess them for
their drug like properties, server in order to further validate the potential of drug
likeliness.

12. Selection of top ligands
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S.No Ligand Name RHB HBD HBA Mol. wt LogP
1 Sulfurentin 1 3 5 270.24 2.4196
2 Sumetrol 3 1 7 410.422 3.4089
3 Taiwaniaquinol A 2 1 4 344.451 4.6244
4 (-)- Epicatechin 1 5 6 290.271 1.5461
5 Parthenolide 0 0 3 248.322 2.762
6 Piceatannol 2 4 4 244.246 2.6794
7 Tectorigenin 2 3 6 300.266 2.5854
8 Strychnine 0 0 3 334.419 2.0925
9 Viscidulin II 3 3 7 330.292 2.594
10 Methoxypraecansone B 0 1 2 191.274 2.96802
Results for the compounds examined for Lipinski’s rule
• RHB - Rotatable Hydrogen Bond
• HBD - Hydrogen Bond Donor
• HBA - Hydrogen Bond Acceptor
• Mol. wt - Molecular weight
• LogP - Partition coefficient

13. 13
MOLECULAR
DESCRIPTOR
Sulfuretin Sumatrol Taiwaniaquinol
A
Epicatec
hin
Parthenolide Piceatannol Tectorigenin Strychnine Viscidulin Methoxypraecansone
ABSORPTION
Water solubility
(logmol/L)
-2.899 -4.852 -5.146 -3.117 -3.161 -3.227 -3.435 -3.445 -3.341 -5.591
Caco2
permeability
1.208 1.48 1.721 -0.283 1.71 0.878 -0.132 1.56 -0.003 1.064
Intestinal
absorption
(human)
89.347 94.671 93.195 68.829 97.599 88.197 84.511 99.843 81.929 95.845
P-glycoprotein
substrate (Y/N)
Yes No No Yes No Yes Yes Yes Yes No
Molecular descriptors for the top ligand molecules predicted using pkCSM online tool

14. 14
MOLECULAR
DESCRIPTOR
Sulfuretin Sumatrol Taiwaniaquinol
A
Epicatec
hin
Parthenolide Piceatannol Tectorigenin Strychnine Viscidulin Methoxypraecansone
DISTRIBUTION
VDSS(human,log
L/Kg)
0.479 -0.156 0.425 1.027 0.291 0.438 0.166 1.16 0.161 0.195
BBB permeability -0.706 -0.714 -0.678 -1.054 0.444 -0.776 -1.06 0.148 -1.31 -0.45
CNS permeability -2.203 -2.955 -1.673 -3.298 -3.007 -2.257 -2.404 -2.165 -3.403 -1.972
METABOLISM
CYP2D6 inhibitor No No No No No No No No No No
CYP3A4 inhibitor No Yes No No No Yes Yes Yes Yes Yes

15. 15
MOLECULAR
DESCRIPTOR
Sulfuretin Sumatrol Taiwaniaquinol
A
Epicatec
hin
Parthenolide Piceatannol Tectorigenin Strychnine Viscidulin Methoxypraecansone
EXCREATION
Total
clearance(logml/m
in/kg)
-0.042 0.244 0.235 0.183 1.162 0.004 0.132 0.965 0.237 0.305
Renal OCT2
substrate(Y/N)
No No No No Yes No No Yes No Yes
TOXICITY
AMES
Toxicity(Y/N)
No No No No No No No No No No
Max.tolerated
dose(human,logm
g/kg/day)
-0.185 0.348 -0.459 0.438 0.306 0.338 0.334 -0.535 0.354 0.53
HERG 1 inhibitor No No No No No No No No No No
HERG 2 inhibitor No No No No No No No Yes No Yes
Hepatotoxicity No No No No No No No No No No
Oral rat acute
toxicity (LD50)
2.42 2.476 2.577 2.482 2.096 2.529 2.33 2.798 2.195 2.535

16. 16
Docking results for the ligand molecule.
S.No Affinity (kcal/mol) rmsd l.b. rmsd u.b.
1 -11.81 0 0
2 -10.85 2.237 4.223
3 -10.53 2.91 9.898
4 -10.45 2.577 9.078
5 -9.832 3.711 5.898
6 -9.279 2.5 9.472
7 -9.052 3.558 8.705
8 -9.03 3.898 10.26
9 -8.783 2.414 10.46
Sulfuretin
S.No Affinity (kcal/mol) rmsd l.b. rmsd u.b.
1 -11.53 0 0
2 -10.64 2.225 4.179
3 -10.27 2.935 9.251
4 -9.865 2.592 9.035
5 -9.642 2.774 9.534
6 -8.717 3.873 1025
7 -8.687 3.273 8.622
8 -8.523 2.624 9.584
9 -8.222 2.741 9.712
Sumatrol

17. 17
A
Depiction of molecular interaction of target receptor (protein) on forming complexes with
sulfuretin, using LigPlot

18. 18
B
Depiction of molecular interaction of target receptor (protein) on forming complexes with
sumetrol using LigPlot

19. Conclusion
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• The current study investigated the potential of natural ligands as useful ligands in
melanoma drug discovery and druggable candidates are listed down.
• The filtered drug-like natural compounds (ADMET properties and Lipinski’s rule
of five) were taken forward to discover potential inhibitors against the target
protein.
• These outcomes endorsed that the most active candidates Sulfuretin, Sumetrol,
Taiwaniaquinol A may serve as useful lead compounds in the search for
promising anti-melanoma agents acting through MAPK-inhibition.

20. “
THANK YOU
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