Molecular Modeling Study of Some β-Ketoacyl-acyl Carrier Protein Synthase III Inhibitors as Antibacterial Agents

 

Kumawat Deepak*, Goswami Raksha, Pathak Saurabh, Gupta Durgesh Kumari,  Dwivedi Sunil Kumar,

Chaturvedi S. C.

Oriental College of Pharmacy and Research, Indore (M.P.) 453555

Sri Aurobindo Institute of Pharmacy, Indore (M.P.) 453555

*Corresponding Author E-mail: deepakkumawat.pch@gmail.com

 

ABSTRACT:

Fatty acid biosynthesis (FAB) is an essential metabolic process for prokaryotic organisms and is required for cell viability and growth. β-Ketoacyl-acyl carrier protein (ACP) synthase III also known as FabH or KAS-III plays an essential and regulatory role in bacterial FAB. β-ketoacyl-acyl carrier protein synthase III (FabH) is an emerging target for the development of novel antibacterial agent. FabH enzyme is the key to discovering inhibitors with broad-spectrum antibacterial activity. The discovery of FabH inhibitors is now of special interest in the treatment of bacterial infection. These FabH inhibitors demonstrated significant antibacterial activity and as such have the potential to be novel and potent antibacterial agents. Pharmacophore modeling and CoMFA analysis was done to identify the pharmacophoric features and CoMFA contour maps required for FabH inhibitory activity. The result obtained from pharmacophore modeling of of benzoylaminobenzoic acid derivatives indicated the necessity of hydrogen bond acceptor at the first position of benzoic acid ring and hydrophobic group at 3-position of the benzene ring. The CoMFA contour maps of benzoylaminobenzoic acid derivatives showed that the green contour around the benzene ring attached to phenoxy benzene ring indicated that bulky substituent at the R1   may increase activity like phenyl etc. and green contour around the amino benzoic acid part of the scaffold resulted enhance the antibacterial activity.

 

KEYWORDS: FabH, KAS III, ACP, CoMFA, Benzoylaminobenzoic acid derivatives.

 

 


 

INTRODUCTION:

Fatty acid biosynthesis (FAB) is an essential metabolic process for prokaryotic organisms and is required for cell viability and growth.1 β-Ketoacyl- acyl carrier protein (ACP) synthase III also known as FabH or KAS III plays an essential and regulatory role in bacterial fatty acid biosynthesis.2,3 The enzyme initiates the fatty acid elongation cycles4,5 and is involved in the feedback regulation of the biosynthetic pathway via product inhibition.6 one of the most attractive biochemical pathways that could be targeted for new antibacterial agents is the fatty acid biosynthesis (FAS). This pathway has been demonstrated to be essential for the bacteria cell survival.7 β-ketoacyl-acyl carrier protein synthase III (KAS III) which is encoded by the FabH gene.8, 9 The 3D structures of KAS III and co-complex structures with their inhibitors have been identified in different bacteria by x-ray crystallography. The first x-ray structure of KAS III was identified in E. coli by Rock et al. by using a coenzyme A (CoA) substrate.10

 

Infectious diseases caused by bacteria affect millions of people and are leading causes of death worldwide treatment of infectious diseases still remains an important and challenging problem because of a combination of factors including emerging and increasing number of multi drug resistant microbial pathogens. Considering the antimicrobial resistance phenomenon as one of the greatest challenges in modern medicine system discovery of new substances with potential effectiveness against several pathogenic microorganisms becomes highly desirable.11,12

 

The emergence of bacterial resistance to most of all antibiotics poses a threat to health care and novel therapeutics is needed. Now days the research has been focused on discovered and development of new antibacterial agents with novel target. Therefore it represents a promising target for the design of novel antimicrobial agents.13

 

In this research work various kinds of compounds were design by pharmacophore mapping and CoMFA (drug design methods).

 

MATERIAL AND METOD:

Compounds and Biological Data:

Compounds 1-46 which can inhibit FabH receptor were taken from the literatures and served as the training set and test set in the pharmacophore modeling.14 The structures and inhibitory activities of the compounds are listed in Fig. 1. The chemical structures were drawn in CHEM-Draw software and saved in SYBYL mol2 format. All the 2D structures were converted to 3D structures by SYBYL X-1.2.1 software.

 

Modeling tool:

All the pharmacophore model was generate by using the GALAHAD module of SYBYL X 1.2.1 software and CoMFA molecular modelling studies were performed using TRIPOS module of SYBYL-X 2.1.1 software. The tasks were running on Intel R core-2 Duo RAM: 4GB Memory: 560GB Graphic card: NBIDIA under the Windows Vista 32 bit system.

 

Pharmacophore studies:

The pharmacophore studies were performed using GALAHAD module of SYBYL-X 2.1.1 software.

 

 

All the 46 molecules are separated into training and test set by Galahad software. 37 molecules served as the training and 9 compounds served as test set.Genetic algorithm with linear assignment of hyper molecular alignment of datasets (GALAHAD) was used to generate the Pharmacophore models.Tamplet based Molecular alignment was done by GALAHAD module all the compounds in the training set were prepared and minimization procedure was implemented using the MMFF94 force-field. GALAHAD was run for 80 generations with a population size of 100 and all feature.

 

Table 1. Diethylsulfonamide Derivatives as Inhibitors of FabH

 

 

Compd

R1

R2

IC50

PIC50

13a

Br

H

1.6

5.796

13b

Ph

H

1.6

5.796

13c

Br

Me

160

5.796

13d

OMe

H

11.4

4.943

13e

F

H

8.4

5.079

13f

 

H

2.2

5.658

13g

 

H

6.1

5.215

13h

 

H

2.1

5.678

 

Table 2. [3-Phenoxybenzoylamino] benzoic Acid Derivatives as Inhibitors of FabH: Para-Substitutions on the B Ring

 

 

 

 

 

 

 

Compd

R1

IC50

PIC50

15

H

2.7

5.569

23a

F

3.8

5.420

23b

Br

1.1

5.959

23c

 

185

3.733

23d

 

 

25

 

 

 

4.602

23e

 

 

38

 

4.420

 

23f

 

 

3.2

 

5.495

23g

 

 

1.2

 

5.921

23h

 

0.29

6.538

23i

 

0.27

6.569

24a

 

 

22

 

 

4.658

24b

 

0.11

6.959

24c

 

 

0.056

 

7.252

 

 

Table 3. Aromatic Substitutions on the Para Position of [3-Phenoxybenzoylamino] benzoic Acid Derivatives

 

 

 

Compd

R1

IC50

PIC50

24d

4-CF3

0.096

7.018

24e

4-Me

0.16

6.796

24f

4-CO2

2.1

5.678

24g

4-OH

0.41

6.387

24h

4-Oet

0.22

6.658

24i

4-SO2

0.028

7.553

24j

3-iPr

0.79

6.102

24k

3-OCF3

0.47

6.328

24l

3-Me-4-F

0.24

6.620

24m

3-Cl-4-F

0.57

6.244

24n

3,4-di-F

0.33

6.481

24o

3-Me-4-Cl

0.25

6.602

24p

2,4-di-F

0.16

6.796

 

Table 4. [3-Phenoxybenzoylamino] benzoic Acid Derivatives as Inhibitors of FabH: Modifications of Ring A and C

 

 

 

Compd

A

C

IC50

PIC50

28a

 

 

Ph

10.1

 

4.996

28b

 

Ph

43.0

4.367

28c

 

Ph

290

 

 

3.533

28d

 

 

Ph

˃1000

3.00

28e

 

 

Ph

 

6.0

5.222

28f

 

 

Ph

 

0.41

6.387

28g

Ph

4-Pyr

10

5.00

28h

Ph

3-CO2H-Ph

3.7

5.432

28i

Ph

4- CO2H-Ph

4.4

5.356

28j

Ph

4-F-Ph

5.0

5.301

 

Table 5. Potent Inhibitors of FabH: Effect of Adding OH Ortho to the Carboxylic Acid

 

 

Compd

R1

R2

IC50

PIC50

31

Br

OH

0.062

7.208

33

Ph

OH

0.004

8.398

 

CoMFA:

The CoMFA molecular modelling studies were performed using TRIPOS module of SYBYL-X 2.1.1 software.

 

The compounds of benzoylaminobenzoic acid derivatives were randomly divided into training set and a test set by SYBYL-X 2.1.1. The training set comprises 40 compounds and test set comprises of 6 compounds. CoMFA models were developed using 40 compounds as training set, and externally validated using 6 compounds as test set. The CoMFA (comparative molecular field Analysis) approach which has two fields steric and electrostatic potential field’s effects were calculated using the TRIPOS force field.

 

Five different kinds of partial charges are considered:

(1) Gasteiger charges

(2) Gasteiger Huckel charges

(3) Huckel charges

(4) Pullman charges and

(5) MMFF94 charges

 

RESULT AND DISCUSSION:

Pharmacophore Modelling:

The generated models were evaluated by a test database which composed 9 experimentally known FabH inhibitor.

 

Ten pharmacophore ligands Model of training set 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 generated by 37 known FabH antagonists. Table 6 showed the predictable results for each model.

 

Results are given in Fig.1 for each GALAHAD generated pharmacophore model for training set and test set compounds.

 

Figure 1: Molecular alignment of the Pharmaco phore model 8(Training set)

 

The energy term of computer generate pharmacophore model 8 was 104.45kcal mol which designates the total energy (using the TRIPOS force field) of all molecules. Meanwhile the values of sterics, H-bond MOL_QRY and hits were computed as 953.00, 173.0, 62.64 and 37 respectively in Model 8. In the GALAHAD algorithm sterics is defined as the overall steric similarity among ligand conformers H-bond as the overall pharmacophoric similarity among ligand conformers and MOL_QRY represents the agreement between the query tuplet and the pharmacophoric tuplets of the target ligands as a group. In general a good pharmacophore model should have a higher steric rank and minimized energy. Shown in Table 6. The pharmacophore Model 8 have the lowest value for energy in comparison to the other nine models. Model 8 was selected as the final pharmacophore model for benzoylaminobenzoic acid derivatives. As illustrated in Fig 8 the GALAHAD generated pharmacophore Model 8 contained four H-bond acceptor features (AA_2 AA_5 AA_8 and AA_10) four hydrophobic centres (HY_2, HY_3, HY_6 and HY_9) one negative nitrogen centres (NC_7) and one H-bond donor (DA_ 1).

 

The GALAHAD calculated term specificity is a logarithmic indicator of the expected discrimination for each query based on its number of pharmacophore features their allotment across any partial match constraints and the degree to which the features are separated in space.

 

Five pharmacophore ligands Model of test set 1, 2, 3, 4 and 5 generated by 9 known FabH antagonists. Table 7 shows the predictable results for each model. The Model 3 with the lowest value of energy (4327) was considered to be the best model.


Table 6 The Parameter Values for Each Model (Training set)

Mn

Spec

Ht

Ft

Pt

Energy

Steric

HB

Mol Qry

1

4.661

37

8

0

4618397.00

1103.50

209.00

67.27

2

4.659

37

8

0

5287975.00

1113.40

208.20

53.16

3

5.667

32

9

0

2243323.00

1066.10

188.20

82.34

4

4.628

37

8

0

226866.83

1049.40

204.30

39.6

5

4.656

37

8

0

466489.56

1077.80

203.30

32.94

6

4.623

37

8

0

2207772.00

1054.30

188.80

54.95

7

4.718

37

8

0

111.15

1045.40

191.30

49.54

8

4.415

37

10

0

104.45

953.00

173.00

62.64

9

3.535

37

8

0

24633.44

1029.00

198.00

50.34

10

4.661

35

8

1

4618397.00

1103.50

209.00

67.27

 

Table 7 The Parameter Values for Each Model (Test set)

Mn

Spec

Ht

Ft

Pt

Energy

Steric

HB

Mol Qry

1

5.631

8

9

0

248109.00

509.60

155.50

31.36

2

5.631

8

9

0

248109.00

509.60

155.50

31.36

3

4.677

9

7

0

4327.09

420.40

115.40

32.13

4

5.008

5

6

0

4477.00

441.5

139.50

28.03

5

5.631

8

9

2

248109.09

509.60

155.60

31.36

 


Test Validation:

Test set validation is the accurate validation of the model where the where the activity prediction for those compounds are made which are not included in the training set. The external predictive ability of generated pharmacophore model of for benzoylaminobenzoic acid derivatives. Were evaluated for the test set of 9 molecules which are compound 13a, 23b, 24i, 28a, 28b, 28c, 28d, 28e and 31.

 

For all the 9 compounds in the test set GALAHAD was run for 80 generations with a population size of 100. Five pharmacophore models were generated from 9 compounds in test set. Generated pharmacophore model contained one H-bond acceptor features (green) four hydrophobic features (cyan) one negative nitrogen centres (blue) and one H-bond donor (magenta) summarised in the figure descibed in fig 2.

 

Figure 2: Molecular alignment of the pharmacophore model 03(Test set).

 

CoMFA model, predictivity:

A CoMFA model was generated from a training set of 40 molecules and test sets of 6 molecules with pIC50 values ranging from 4.606 to 5.5145 automatically by TRIPOS modules. The predictable power of resulting molecule was evaluating using a test set of 6 molecules. The predicted pIC50 values and experimental pIC50 values of the training set and test set are summarises in the table 8, 9. The statistical parameters related to CoMFA models are described in Table 10. The CoMFA model by both steric and electrostatic fields gives a correlation coefficient (r2) 0.681, standard error estimates (SEE) 0.6578, Q2 value 4.46, steric contribution 19.26, Electrostatic contribution of positive charge desirable 25.41 and negative charge desirable 14.93

 

Table 8 The Experimental pIC50 Values and Predicted pIc50 Values of the Compounds (Training Set)

S. No

Compound Name

Experimental, pIc50

Predicted pIc50

Gasteiger

1

13b

5.796

5.3682

2

13c

3.796

4.6914

3

13e

5.076

4.7048

4

13f

5.658

5.061

5

13g

5.215

4.8606

6

13h

5.678

5.1273

7

15

5.569

5.5368

8

23b

5.959

5.6063

9

23c

3.733

5.1864

10

23e

4.420

5.3065

11

23f

5.495

4.9199

12

23g

5.921

5.2723

13

23h

6.538

5.4466

14

23i

6.569

5.5269

15

24a

4.658

4.9623

16

24c

7.252

6.6338

17

24d

7.018

6.1018

18

24e

6.796

6.6704

19

24f

5.678

5.6778

20

24g

6.387

6.7326

21

24h

6.658

6.741

22

24j

6.102

5.5551

23

24k

6.328

6.389

24

24l

6.620

6.6872

25

24m

6.244

6.5219

26

24n

6.481

6.6761

27

24o

6.602

6.6021

28

24p

6.796

6.434

29

28a

4.996

5.1486

30

28b

4.367

4.7582

31

28c

3.533

4.7832

32

28d

3.000

4.5609

33

28e

5.272

4.992

34

28f

6.387

5.3787

35

28g

5.000

4.8923

36

28h

5.432

4.9593

37

28i

5.356

5.1045

38

28j

5.301

5.2608

39

31

7.208

7.6302

40

33

8.348

8.5145

 

Table 9 The Experimental pIC50 Values and Predicted pIC50 Values of the Compounds Test Set

S. No

Compound Name

Experimental

PIC50

Predicted PIC50

Gasteiger

1

13

5.796

4.7299

2

13d

4.943

4.6086

3

23a

5.420

5.5291

4

23d

4.602

5.3672

5

24b

6.569

6.2782

6

24i

6.959

6.4603

 

Test Validation:

Test validation is the precise validation of model where the activity prediction of those compounds is made which are not include in training set. external predictive ability of generated CoMFA model of benzoylaminobenzoic acid derivative was evaluate for the test set of 6 molecule which are include 13, 13d ,23a,23d,24b and 24i where they obtained predictive r2 value (r2 pred) 0.681 supported the high predictive ability of the generated model.

 

The coefficient from the CoMFA were used to generated 3D counter maps, which determine the critical physiochemical properties responsible for charge in activity and also explore the critical importance of varies substituent’s in their 3D orientation. The physiochemical properties of benzoylaminobenzoic acid molecules, responcible for FabH inhibitory activity. The total contribution of steric field are 19.26 and electrostatic field of positive charge and negative charge are 25.41, 14.93 respectively. Obtained from CoMFA study of benzoylaminobenzoic acid based series. It showed an r2 value 0.681 with a standard error 0.657.

 

CoMFA CONTOUR MAPS:

The results obtained from CoMFA indicate that steric and electrostatic properties play a major role in inhibition activity described in table 10.

 


 

Table 10: Statistical Parameters Related to CoMFA Models

M.n

Q2

R2

Std. Error

Steric Contribution %

Electrostatic Contribution %

Steric Bulk Desirabe

Steric Bulk Undesireabe

PC

NC

K2

0.446

0.681

0.657981

19.26

20.39

25.41

14.93

 


*K2- Gasteiger charge

a. Steric contribution

 

b. Electrostatic contributions

Figure 4: CoMFA contour maps for Gasteiger charge

As shown in the CoMFA contour maps of compound 33 which is the most active compound, the green contour was favored around the benzene ring attached to amino indicates that bulky substituents at the R2 increases activity like OH and green contour around the benzene ring attached to phenoxy ring indicates that bulky substituents at the R1 increases activity like phenyl etc. sterically unfavorable contour in yellow color were found near in benzin ring attached to phenoxy ring and around the of amino group. Thus bulky substituents decrease activity. Blue contour near amino on benzene ring attached to amino group and phenoxy ring attached to benzene ring indicates that substituents with electropositive group enhance activity. While red colour on substituent R2 i.e. 2-hydroxybenzoic acid indicates that electronegative group at this position enhances activity.

 

CONCLUSION:

β-ketoacyl-acyl carrier protein synthase III (FabH) is an emerging target for the development of novel antibacterial agent. Ligand-based design of a series of benzoylaminobenzoic acid derivatives led to the discovery of potent inhibitors of β-ketoacyl-acyl carrier protein synthase III (FabH).

 

These FabH inhibitors demonstrated potent antibacterial activity (MIC) and as such have the potential to be novel and potent antibacterial agents.

Given the unforeseen structural differences within the active site of some pathogenic FabH enzymes the key to discovering inhibitors with broad-spectrum antibacterial activity. The discovery of FabH inhibitors is now of special interest in the treatment of bacterial infection. In the present study a benzoylaminobenzoic acid derivative were designed based on the Pharmacophore modling to identify the pharmacophoric feature required for inhibitory activity CoMFA analysis to identify the essential structural requirements in 3D chemical space for the modulation of FabH inhibitory activity of benzoylaminobenzoic acid derivatives.

 

We have successfully generated QSAR models for a set of benzoylaminobenzoic acid derivatives. The best CoMFA model of benzoylaminobenzoic acid derivatives displayed q2 = 0.446 r2 = 0.681 contribution of steric filed 19.26 and electrostatic contribution of positive charge and negative charge are 25.41, 14.93 respectively.

 

The CoMFA contour maps of benzoylaminobenzoic acid derivatives showed that the green contour around the benzene ring attached to phenoxy benzene ring indicates that bulky substituent at the R1 increases activity like phenyl etc and green contour around the amino benzoic acid part of the scaffold resulted enhance the antibacterial activity.

 

Pharmacophore modling to identify the pharmacophoric feature required for inhibitory activity. The result obtained from Pharmacophore modling of of benzoylaminobenzoic acid derivatives indicates the necessity of hydrogen bon acceptor at the first position of benzoic acid ring and hydrophobic group at 3-position of the benzene ring. Pharmacophore modelling and CoMFA analysis to suggest these inhibitors bind to FabH there by acting as inhibitor of fatty acid biosyntheses in bacteria.

 

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Received on 14.09.2019            Modified on 30.09.2019

Accepted on 16.10.2019            © A&V Publications All right reserved

Asian J. Res. Pharm. Sci. 2019; 9(4):253-259.

DOI: 10.5958/2231-5659.2019.00039.0