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

 

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.

 

 

 

 

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 cycles^4,5 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 q^{2 =} 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.

 

REFERENCES:

1.      Cronan, J. E., Jr; Rock, C. O. In Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology; Neidhardt, F. C., Ingraham, I. L., Low, K. B., et al., Eds.; American   Society of Microbiology: Washington, DC, 1996; pp 612–636.

2.      Khandekar, S. S.; Daines, R. A.; Lonsdale, J. T. Curr. Protein Pept. Sci. 2003, 4, 21.

3.      Heath, R. J.; Rock, C. O. Nat. Prod. Rep. 2002, 19, 581.

4.      Tsay, J. T.; Oh, W.; Larson, T. J.; Jackowski, S.; Rock, C. O. Isolation and Characterization of the Beta-ketoacyl-acyl Carrier Protein Synthase III Gene (fabH) from Escherichia Coli K-12, J. Biol. Chem. 1992, 267 (10), 6807-6814.

5.      Clough, R. C.; Matthis, A. L.; Barnum, S. R.; Jaworski, J. G. J. Biol. Chem. 1992, 267,20992.

6.      Heath, R. J.; Rock, C. O. J. Biol. Chem. 1996, 271, 1833.

7.      Lee, J.-Y.; Jeong, K.-W.; Lee, J.-U.; Kang, D.-I.; Kim, Y. Bio org. Med.Chem.2009, 17, 5408.

8.      C.Y. Lai, J.E. Cronan, Beta-ketoacyl-acyl carrier protein synthase III (FabH) is essential for bacterial fatty acid synthesis, J. Biol. Chem. 19 (2003) 51494-51503.

9.      Jee-Young Lee, Ki-Woong Jeong, Soyoung Shin, Ju-Un Lee, Yangmee Kim* Discovery of novel selective inhibitors of Staphylococcus aureus b-ketoacyl acyl carrier protein synthase III, European Journal of Medicinal Chemistry 47 (2012) 261-269

10.   C. Davies, R.J. Heath, S.W. White, C.O. Rock, the 1.8 A crystal structure and active-site architecture of beta-ketoacyl-acyl carrier protein synthase III (FabH) from Escherichia coli, Structure 8 (2000) 185-195.

11.   Ray CG et al., (2004). Sherries Medical Microbiology 4th ed. ISBN 0-8385-8529-9.

12.   Chennai Declaration holds key for antibiotic resistance January 23 2013 and around Sholapur volume 2003 35 17-23.

13.   Peng-Cheng Lv, Kai-Rui Wang et al., Design, synthesis and biological evaluation of novel thiazole derivatives as potent FabH inhibitors, Bioorganic & Medicinal Chemistry Letters 19 (2009) 6750–6754.

14.   Nie Zhe, Perretta Carin, Lu Jia et al., Structure-Based Design, Synthesis, and Study of Potent Inhibitors of β -Ketoacyl-acyl Carrier Protein Synthase III as Potential Antimicrobial Agents, J. Med. Chem. 2005 48, 1596-1609.

 

 

 

 

 

 

Accepted on 16.10.2019            © A&V Publications All right reserved