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1
Dr. Sachchidanand
(HOD) Pharmacoinformatics Dept.
NIPER Hajipur.
Dr. Shailendra S. Chaudhaery
Lecturer Pharmacoinformati...
Tuberculosis (TB) is the chronic infectious disease caused
by infection with species belonging to the Mycobacterium
tuberc...
The classical symptoms of active TB infection are:
Chronic cough
Blood-tinged sputum
Fever
 Night sweats
Weight loss
...
Global burden & epidemiology of TB
• It is estimated that about 8.7 million new cases of TB (13% co-infected with
HIV) and...
Category Name of drug Mechanism of action
First line drugs
Isoniazid Inhibition of fatty acid synthesis
Rifampicin Inhibit...
6
Different targets for Tuberculosis
5. FtsZ (Filamentous temperature-sensitive protein Z ) Drug Target for Tuberculosis.
...
7
1.It is an essential protein for bacterial cell division.
2. This protein is highly conserved, and identified in many ba...
8
Filamentous temperature-sensitive protein Z (FtsZ)
 FtsZ is the key protein of bacterial cell division, filament-formin...
Cell envelope
DNA
FtsZ ring
10
Mechanism of action of FtsZ
11
Benzimidazoles: A new class of anti-TB drugs
 Benzimidazole nucleus is a constituent of bioactive heterocyclic
compoun...
To build 2D-QSAR model to derive important physicochemical
properties related to FtsZ inhibitors.
To build Gaussian base...
• Ligand based drug design Structure based drug design
Methodology
Docking2D-QSAR Pharmacophore
Virtual screening
Novel dr...
1.QSAR
A quantitative structure-activity relationship (QSAR) correlates
molecular properties to some specific biological a...
Collection Of Dataset:
Total 59 compounds were collected from the literature, and drawn
using Marvin Sketch program.
Bio...
16
Comp No. R1R2N R3 MIC((µg/ml) pMIC
1 0.63 5.78
2 6.25 4.82
3 100 3.63
4 50 3.96
5 0.06 6.77
Benzimidazole series compou...
17
MLR PLS
Methods
Training set Test set Training set Test set
r2
1. Random
method
0.936 0.732 0.870 0.696
2. Diversed
met...
Log (1/C) = 0.415 + 0.407 * (ALogP)- 3.268e-003 * ( Molecular_Weight ) +
0.1909 * (Num_H_Donors ) + 0.3852 * (Num_H_Accept...
Graph of 2D-QSAR (Random based -MLR)
19
Training set-48 (r2 = 0.936) Test set-11 (r2 = 0.732 )
20
The value of AlogP = 1.6-5.6
Number of HBA = 2-5
Number of HBD = 2-4
Polar surface area = 0.141-0.292
Number of rotatab...
3D-QSAR (Gaussian Based Method)
Alignment of molecule Docking
based
Pharmacophore
based
Atom based
method
3D-QSAR exploits...
22Training set-45 (r2 = 0.844 ) Test set -11 (r2 = 0.682)
Compound (Random
based )
r2
Training set (48 comp.) 0.844
Test s...
Training set-48 (r2 = 0.839) Test set -11 (r2 = 0.667)
Scatter Plot Analysis
3D-QSAR GRAPH(Gaussian-based method
Compound ...
24
COUNTER MAP OF 3D-QSAR (CoMFA)
Field-Based QSAR-Steric
Field-Based QSAR-Electrostatic
NH1
N
Electropositive
Electronega...
25
COUNTER MAP OF 3D-QSAR (CoMSIA)
Gaussian Based QSAR-Steric
POSITIVER3
26
NR3 POSITIVE NEGATIVE
NH1 NElectropositive Electronegative
Gaussian Based QSAR-Hydrophobic
Gaussian Based QSAR-Electros...
27
Gaussian Based QSAR-HBD
N
R3
POSITIVE
NEGATIVE
O
R3
NEGATIVE
POSITIVE
Gaussian Based QSAR-HBA
28
HBA
HBD
Steric
Hydrop
hobic
Electropositive
Electronegative
Summary of the 3D-QSAR
2.pharmacophore
A pharmacophore is the ensemble of steric and electronic features
that is necessary to ensure the optimal ...
Pharmacophore result
Features Rank Direct Hit Partial Hit Max Fit
01 YZHH 116.621 1111111111 0000000000 4
02 YZHH 116.121 ...
31
A. (Most active Compound-5) B. (Least active compound-3)
Alignment of the most potent compound-5 & least active compoun...
Docking
32
PDB ID= 1RLU Resolution=2.08 R-value=0.182 pH=5.6
LIGAND-C10 H16 N5 O13 P3 S
5'-GUANOSINE-DIPHOSPHATE-
MONOTHIO...
Structure of FtsZ protein
Nucleotide
binding site
GTP γ Thiophosphate
34
Active site residues PDB:1RLU, Ligand-Protein Complex
Molecular structure view
35
Ligand Interaction Diagram
36
RMSD VALUE=0.726
DOCKING SCORE= -8.93
Docking of the substrate on the same active site
Co-crystalised ligand Docked lig...
37
Compound Name
Biological Activity
(pMIC) Docking Score
Compound-5 6.77 -8.11
Compound-39 6.38 -7.32
Compound-43 6.1 -7....
38
Interaction of Most active compound-5 & Least active compound-3
Compound-5
Compound-3
Compound-3
Compound-5
39
Virtual screening workflow
Database ( Asinex database with
100000 molecules)
Filter (Lipinski rule of 5)
Shape based
sc...
40
Compound
number
Compound name Fit value
Hit 1 1-(3-Benzyl-2-butyl-5-methyl-3H-imidazo[4,5-
b]pyridin-6-yl)-3-(3-chloro-...
41
Hit 3
Hit 1 Hit 2
Hit 4 Hit 5
Hit molecules from Virtual screening
42
Conclusion
The study of 2D-QSAR of FtsZ inhibitors concluded that the
descriptor ALogP, HBA, HBD, PSA are important fo...
43
 The docking study concluded that Glu136, Arg140, Phe180
and Asp184 are important residues for ligand binding. Asp184
...
REFERENCES
2. Leung AKW, White EL, Ross LJ, Reynolds RC, DeVito JA, Borhani
DW. Structure of Mycobacterium tuberculosis Ft...
45
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Exploration of a potential FtsZ inhibitors as new scaffolds by Ligand and Structure based drug design methods for development of novel anti-tubercular drugs

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Multi-drug resistant Mtb is a major worldwide health problem. Therefore, it is need to develop new antibiotics with novel modes of action to overcome this emerging resistance problem.
FtsZ (Filamentous temperature-sensitive protein Z ) Drug Target for Tuberculosis
FtsZ is the key protein of bacterial cell division, filament-forming GTPase and a structural homologue of eukaryotic tubulin.

It interacts with membrane-associated proteins FtsA and ZipA and assembles into a ring like structure at the midcell, this ring is known as Z-ring.

The formation of the Z-ring is facilitated by the ability of FtsZ to bind to GTP, which enables polymerization of FtsZ, resulting in the creation of straight protofilaments.

It is the first protein to move to the division site, and is essential for recruiting other proteins that produce a new cell wall between the dividing cells. So it is an emergent target for new antibiotics.

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Exploration of a potential FtsZ inhibitors as new scaffolds by Ligand and Structure based drug design methods for development of novel anti-tubercular drugs

  1. 1. 1 Dr. Sachchidanand (HOD) Pharmacoinformatics Dept. NIPER Hajipur. Dr. Shailendra S. Chaudhaery Lecturer Pharmacoinformatics Dept. NIPER Hajipur. Under the supervision of : PAVAN KUMAR M.S.(Pharm.) Pharmacoinformatics 4th semester, NIPER, Hajipur. Presented by : Exploration of a potential FtsZ inhibitors as new scaffolds by Ligand and Structure based drug design methods for development of novel anti-tubercular drugs
  2. 2. Tuberculosis (TB) is the chronic infectious disease caused by infection with species belonging to the Mycobacterium tuberculosis (Mtb). Mtb is slow-growing bacterium, which remains in dormant state for long period of time in the host, thus they are resistant to the effect of antibiotics. TB typically attacks the lungs but can also affect other parts of the body. 2 M.tuberculosis Introduction
  3. 3. The classical symptoms of active TB infection are: Chronic cough Blood-tinged sputum Fever  Night sweats Weight loss Fatigue and finger clubbing Classical Symptoms of TB
  4. 4. Global burden & epidemiology of TB • It is estimated that about 8.7 million new cases of TB (13% co-infected with HIV) and 1.4 million people died from TB in 2011. • Recent statistics from WHO estimate that there are approximately 9.2 million new tuberculosis (TB) cases every year with a global mortality rate of 23%.
  5. 5. Category Name of drug Mechanism of action First line drugs Isoniazid Inhibition of fatty acid synthesis Rifampicin Inhibition of protein synthesis Pyrazinamide Disrupt plasma membrane, disrupt energy metabolism. Ethambutol Inhibition of arabinogalactan synthesis Streptomycin Inhibition of protein synthesis Second line drugs Fluoroquinolones Inhibit Nucleic Acid Synthesis Cycloserine Inhibition of cell wall synthesis Capreomycin Inhibition of protein synthesis Para aminosalicylic acid Inhibition of folic acid and iron metabolism Ethionamide Inhibition of fatty acid synthesis Aminoglycosides (Amikacin/kanamycin) Inhibition of protein synthesis 5 Antitubercular drugs
  6. 6. 6 Different targets for Tuberculosis 5. FtsZ (Filamentous temperature-sensitive protein Z ) Drug Target for Tuberculosis. Serial No. Drug Target Antibiotic class Drug name Mechanism of action 1. DNA gyrase Fluoroquinolones Moxifloxacin, gatifloxacin Inhibition of protein synthesis 2. RNA polymerase Rifamycins Rifapentine Inhibits DNA-dependent RNA polymerase activity 3. Ribosome Oxazolidinones Linezolid, PNU-100480, AZD-5847 Inhibition of protein synthesis 4. ATP synthase Diarylquinoline TMC-207 Depletion of membrane energy  Multi-drug resistant Mtb is a major worldwide health problem. Therefore, it is need to develop new antibiotics with novel modes of action to overcome this emerging resistance problem.
  7. 7. 7 1.It is an essential protein for bacterial cell division. 2. This protein is highly conserved, and identified in many bacteria. 3. It is not present in higher eukaryotes organism, So it shows that FtsZ inhibitors should not be toxic to human cells as well as high eukaryotes. FtsZ is an emergent target
  8. 8. 8 Filamentous temperature-sensitive protein Z (FtsZ)  FtsZ is the key protein of bacterial cell division, filament-forming GTPase and a structural homologue of eukaryotic tubulin.  It interacts with membrane-associated proteins FtsA and ZipA and assembles into a ring like structure at the midcell, this ring is known as Z-ring.  The formation of the Z-ring is facilitated by the ability of FtsZ to bind to GTP, which enables polymerization of FtsZ, resulting in the creation of straight protofilaments.  It is the first protein to move to the division site, and is essential for recruiting other proteins that produce a new cell wall between the dividing cells. So it is an emergent target for new antibiotics.
  9. 9. Cell envelope DNA FtsZ ring
  10. 10. 10 Mechanism of action of FtsZ
  11. 11. 11 Benzimidazoles: A new class of anti-TB drugs  Benzimidazole nucleus is a constituent of bioactive heterocyclic compounds and structural isosters of naturally occurring nucleotides, which allows them to interact easily with the biopolymers of the living system.  The 2,5,6-trisubstituted Benzimidazoles series of compound taken for study having biological activity ranging from 0.06 to 100 µg/ml, from the literature. Benzimidazole structure
  12. 12. To build 2D-QSAR model to derive important physicochemical properties related to FtsZ inhibitors. To build Gaussian based 3D-QSAR model for FtsZ inhibitors.  Development of best 3D-Pharmacophore model using Hip-hop based method. Analysis of important interaction involve in FtsZ protein using docking-based approach. Analysis of Hits obtained through virtual screening using pharmacophore-based Approach and docking-based Approach. Aim and objective
  13. 13. • Ligand based drug design Structure based drug design Methodology Docking2D-QSAR Pharmacophore Virtual screening Novel drug molecule 3D-QSAR
  14. 14. 1.QSAR A quantitative structure-activity relationship (QSAR) correlates molecular properties to some specific biological activity in terms of an equation. Collection of compound from literature Descriptor Generation Feature Selection Model Construct Model Validation Model Development flow chart Ligand based approach
  15. 15. Collection Of Dataset: Total 59 compounds were collected from the literature, and drawn using Marvin Sketch program. Biological Assay:FtsZ polymerization inhibitory assay Biological activity( MIC) ranging from the 0.06 – 100 µg/ml. MIC is converted to pMIC values. Imported to the QSAR Module of Discovery studio Software. Series of compounds 1. The 2,5,6-trisubstituted benzimidazoles. , october,2011 Journal of Medicinal Chemistry , September 2011 2D-QSAR Reference: Kumar, K.; Awasthi, D.; Lee, S.-Y.; Zanardi, I.; Ruzsicska, B.;Knudson, S.; Tonge, P. J.; Slayden, R. A.; Ojima, I. Novel trisubstituted benzimidazoles, targeting Mtb FtsZ, as a new class of antitubercular agents. J. Med. Chem. 2011, 54, 374−381.
  16. 16. 16 Comp No. R1R2N R3 MIC((µg/ml) pMIC 1 0.63 5.78 2 6.25 4.82 3 100 3.63 4 50 3.96 5 0.06 6.77 Benzimidazole series compounds which are used in QSAR study
  17. 17. 17 MLR PLS Methods Training set Test set Training set Test set r2 1. Random method 0.936 0.732 0.870 0.696 2. Diversed method 0.933 0.608 0.849 0.607 Division of Training and Test set Best 2D-QSAR Model was generated through Random based and MLR method. Number of molecules in Training set = 48 Number of molecules in Test set = 11
  18. 18. Log (1/C) = 0.415 + 0.407 * (ALogP)- 3.268e-003 * ( Molecular_Weight ) + 0.1909 * (Num_H_Donors ) + 0.3852 * (Num_H_Acceptors) - 0.1666 * (Num_RotatableBonds) - 0.5808 * (Num_Rings) - 1.433e-002 * ( Num_AromaticRings) + 10.36 * (Molecular_Fractional Polar Surface Area) 2D-QSAR Model
  19. 19. Graph of 2D-QSAR (Random based -MLR) 19 Training set-48 (r2 = 0.936) Test set-11 (r2 = 0.732 )
  20. 20. 20 The value of AlogP = 1.6-5.6 Number of HBA = 2-5 Number of HBD = 2-4 Polar surface area = 0.141-0.292 Number of rotatable bonds = 4-12 Number of Rings = 1-3 Conclusion of 2D-QSAR Benzimidazole scaffolds
  21. 21. 3D-QSAR (Gaussian Based Method) Alignment of molecule Docking based Pharmacophore based Atom based method 3D-QSAR exploits the three-dimensional properties of the ligands to predict their biological activities using chemometric techniques. It has served as a valuable predictive tool in the design of pharmaceuticals.
  22. 22. 22Training set-45 (r2 = 0.844 ) Test set -11 (r2 = 0.682) Compound (Random based ) r2 Training set (48 comp.) 0.844 Test set (11 comp.) 0.682 Total compounds:59 3D-QSAR GRAPH( Field-based method) Scatter Plot Analysis
  23. 23. Training set-48 (r2 = 0.839) Test set -11 (r2 = 0.667) Scatter Plot Analysis 3D-QSAR GRAPH(Gaussian-based method Compound (Random based ) r2 Training set (48 comp.) 0.839 Test set (11 comp.) 0.667 Total compounds:59
  24. 24. 24 COUNTER MAP OF 3D-QSAR (CoMFA) Field-Based QSAR-Steric Field-Based QSAR-Electrostatic NH1 N Electropositive Electronegative POSITIVER3
  25. 25. 25 COUNTER MAP OF 3D-QSAR (CoMSIA) Gaussian Based QSAR-Steric POSITIVER3
  26. 26. 26 NR3 POSITIVE NEGATIVE NH1 NElectropositive Electronegative Gaussian Based QSAR-Hydrophobic Gaussian Based QSAR-Electrostatic
  27. 27. 27 Gaussian Based QSAR-HBD N R3 POSITIVE NEGATIVE O R3 NEGATIVE POSITIVE Gaussian Based QSAR-HBA
  28. 28. 28 HBA HBD Steric Hydrop hobic Electropositive Electronegative Summary of the 3D-QSAR
  29. 29. 2.pharmacophore A pharmacophore is the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger its biological response. Model Development flow chart Input-2D/3D molecules Structure CHARMm forcefield and Minimization Diverse Conformation generation Generation of Hypothesis validation
  30. 30. Pharmacophore result Features Rank Direct Hit Partial Hit Max Fit 01 YZHH 116.621 1111111111 0000000000 4 02 YZHH 116.121 1111111111 0000000000 4 03 YZDH 115.918 1111111111 0000000000 4 04 RYZH 115.363 1111111111 0000000000 4 05 RYZH 115.363 1111111111 0000000000 4 06 RYZH 115.159 1111111111 0000000000 4 07 RYZH 115.159 1111111111 0000000000 4 08 YZDH 114.740 1111111111 0000000000 4 09 YZHA 114.621 1111111111 0000000000 4 10 YZHA 114.621 1111111111 0000000000 4 Hy-ali Hy HBD: Hydrogen bond donar (D) HBA: Hydrogen bond acceptor (H) HY: Hydrobhobic (Z) Hy-ali: Hydrobhobic aliphatic (Y) HBA HBD
  31. 31. 31 A. (Most active Compound-5) B. (Least active compound-3) Alignment of the most potent compound-5 & least active compound-3.
  32. 32. Docking 32 PDB ID= 1RLU Resolution=2.08 R-value=0.182 pH=5.6 LIGAND-C10 H16 N5 O13 P3 S 5'-GUANOSINE-DIPHOSPHATE- MONOTHIOPHOSPHATE Structure based approach Preparation of protein Preparation of ligand
  33. 33. Structure of FtsZ protein Nucleotide binding site GTP γ Thiophosphate
  34. 34. 34 Active site residues PDB:1RLU, Ligand-Protein Complex Molecular structure view
  35. 35. 35 Ligand Interaction Diagram
  36. 36. 36 RMSD VALUE=0.726 DOCKING SCORE= -8.93 Docking of the substrate on the same active site Co-crystalised ligand Docked ligand
  37. 37. 37 Compound Name Biological Activity (pMIC) Docking Score Compound-5 6.77 -8.11 Compound-39 6.38 -7.32 Compound-43 6.1 -7.07 Compound-14 5.78 -6.27 Compound-30 5.36 -6.61 Compound-37 5.42 -6.81 Compound-7 5.09 -6.62 Compound-38 4.86 -6.33 Compound-48 4.76 -6.27 Compound-18 4.59 -6.22 Compound-21 4.48 -5.50 Compound-3 3.56 -4.98 The important amino acid residue are Glu136, Arg140, Phe 180, Asp184. Docking of the selected FtsZ Inhibitors
  38. 38. 38 Interaction of Most active compound-5 & Least active compound-3 Compound-5 Compound-3 Compound-3 Compound-5
  39. 39. 39 Virtual screening workflow Database ( Asinex database with 100000 molecules) Filter (Lipinski rule of 5) Shape based screening (ROCS) Retrieval of protein information from PDB (PDB ID 1RLU) Protein preparation Pharmacophore-based virtual screening 5 Hit Compounds Receptor grid generation500 Compounds 78000 Compounds Docking studies Novel Hit Molecules
  40. 40. 40 Compound number Compound name Fit value Hit 1 1-(3-Benzyl-2-butyl-5-methyl-3H-imidazo[4,5- b]pyridin-6-yl)-3-(3-chloro-phenyl) 3.65885 Hit 2 Pentanoic acid {2,5-dimethoxy-4-[3-(tetrahydro- furan-2-ylmethyl)-thioureido]-ph 3.54616 Hit 3 N-[2-(5-Methyl-furan-2-yl)-1H-benzoimidazol- 5-yl]-butyramide_35 3.54553 Hit 4 Cyclopropanecarboxylic acid {2,5-diethoxy-4- [3-(tetrahydro-furan-2-ylmethyl)-th 3.53696 Hit 5 1-[2-Butyl-3-(4-chloro-benzyl)-5-methyl-3H- imidazo[4,5-b]pyridin-6-yl]-3-(3-chl 3.51725 Virtual screening Result Analysis Table : Compound name and fit value of Hits molecules
  41. 41. 41 Hit 3 Hit 1 Hit 2 Hit 4 Hit 5 Hit molecules from Virtual screening
  42. 42. 42 Conclusion The study of 2D-QSAR of FtsZ inhibitors concluded that the descriptor ALogP, HBA, HBD, PSA are important for antitubercular activity. 3D-QSAR models were interpreted in the form of contour maps, concluded that steric field contribution is higher as compared to other field intensities. The contour map of steric, hydrophobic, electro-negative, electro-positive, HBA at R3 position and electro-positive, HBD at R1 and R2 position responsible for increased in antitubercular activity. The best hypothesis with four point pharmacophore concluded that the best model generated is the 3rd number hypothesis i.e. compound-5 (YZDH).
  43. 43. 43  The docking study concluded that Glu136, Arg140, Phe180 and Asp184 are important residues for ligand binding. Asp184 also provides essential H-bond interactions for favourable ligand binding. And Asp184 should be taken in drug design targeting the FtsZ. Virtual screening method from Asinex database resulted in identification of five new Hits, as potential Hits for testing of novel inhibitors, these hits may responsible for the antitubercular activity. So finally we concluded that FtsZ is an emergent target for the development of new antitubercular drugs.
  44. 44. REFERENCES 2. Leung AKW, White EL, Ross LJ, Reynolds RC, DeVito JA, Borhani DW. Structure of Mycobacterium tuberculosis FtsZ reveals unexpected, G protein-like conformational switches. J Mol Biol 2009, 342(3), 953–970. 3. Margalit DN, Romberg L, Mets RB, et al. Targeting cell division: small- molecule inhibitors of FtsZ GTPase perturb cytokinetic ring assembly and induce bacterial lethality. [Erratum to document citedin CA141:271048]. Proc Natl Acad Sci USA 2004, 101(38), 13969. 4. Scheffers D-J, de Wit JG, den Blaauwen T, Driessen AJM. GTP hydrolysis of cell division protein FtsZ: evidence that the active site is formed by the association of monomers. Biochemistry 2002, 41(2), 521–529. 1. K. Kumar, et al. Discovery of anti-TB agents that target the cell-division protein FtsZ, Future Med Chem, 2010, 2 (8), 1305-1323.
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