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Der Chemica Sinica, 2017, 8(4):421-435

ISSN : 0976-8505CODEN (USA): CSHIA5

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3D-QSAR Pharmacophore-based Ligand Alignment, Virtual Screening and Molecular Docking of Arylidene (Benzimidazol-1-yl)acetohydrazones as

Biomimetics of Bacterial InhibitorsEl Sayed H El Ashry1*, Mohamed E I Badawy2, Yeldez El-kilany3, Nariman M Nahas3 and

Mariam A Al-Ghamdi3

1Department of Chemistry, Faculty of Science, Alexandria University, Alexandria, Egypt2Department of Pesticide Chemistry and Technology, Faculty of Agriculture, Alexandria University, Alexandria, Egypt

3Department of Chemistry, Faculty of Applied Science, Umm Al-Qura University, Mekkah, Kingdom of Saudi Arabia

ABSTRACT

The arylidene (1H-benzimidazol-1-yl) acetohydrazone derivatives were previously designed, synthesized, structurally determined and their antibacterial activity was evaluated against Rhizobium radiobacter. The minimum inhibitory concentration (MIC) result demonstrated that salicylaldehyde(1H-benzimidazol-1-yl)acetohydrazone (4) was the most active compound (MIC=15 mg/L). The mapping of pharmacophores based on 3D structure, docking on protein, protein-ligand interaction and energy of binding were accomplished to identify the interaction between compounds and active sites of the supposed targets. A pharmacophore generated by using the HypoGen 3D-QSAR pharmacophore-based ligand alignment in Discovery Studio 2.5 software produced an accurate predictive model of antibacterial activity of the acetohydrazone derivatives. The validity of the pharmacophore model extends to structurally different class of compounds that could open new frontiers for study. The best pharmacophore in terms of statistics and predictive value consisted of four characteristics included two hydrogen bond acceptors (HA), a hydrophobic characteristic (HY) and an aromatic characteristic (AR). The selected pharmacophore hypothesis yielded an RMSD of 0.44 and a correlation coefficient of 0.91 with a cost difference (null cost minus total cost) of about 45. Molecular docking using MOE software was performed on the best five target proteins include cystathionine beta-synthase, ribonucleoside-diphosphatereductase 2-subunit alpha, aspartate aminotransferase, glutamate decarboxylase beta, and 3-hydroxy-3-methylglutaryl-coenzyme Areductase. The results revealed a significant correlation between the binding score and biological activity for these compounds to describe the molecular basis for the structure-activity relationship results.

Keywords: Benzimidazolyl-acetohydrazones, Antibacterial activity, 3D-QSAR, Pharmacophore modelling, Docking

INTRODUCTION

Many classes of antibacterial agents are currently available, but resistance phenomena in most pathogenic bacteria to such products appear continuously [1]. In order to avoid this serious problem, the synthesis of new types of antibacterial agents or the development of bioactivity of the previous drugs is a very important task [2-4]. In addition, the in-silico studies of the molecular modeling provide hypothetical evidence on the documentation of explicit structural topographies that require small molecular inhibitors that play a vital role in inhibiting biological activity [5-7]. There has been considerable interest in the chemistry of heterocyclic compounds containing nitrogen, oxygen, sulfur and selenium which have different pharmacophoric properties. For example, quinolinylhydrazones, thiadiazoles, thiose micarbazones, pyrazolines, dioxazoles, and selenadiazoles have shown various important biological activities such as antimicrobial, antimalarial, antitubercular, antitumor, antiamoebic activity, anticancer, CNS depressant, anticonvulsant, molluscicidal, analgesic, anti-inflammatory and anti HIV [8-13].

The incorporation of the benzimidazole nucleus an important synthetic strategyin studies of antimicrobial drug discovery [14-17]. The high therapeutic properties of related drugs have encouraged medicinal chemists to synthesize the large number of novel chemotherapeutic agents, including antiparasitics, fungicides, antimethylemics and anti-inflammatories [2,15,18,19]. According to several studies, the antibacterial mechanism of benzimidazoles is due to

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their structural similarity to purine, which plays an important role in the biosynthesis of nucleic acids and proteins in the bacterial cell wall. As competitive inhibitors, benzimidazoles can replace purine, thus blocking the biosynthesis of key components, killing or inhibiting the growth of bacteria [4]. Recently, great consideration has been paid to the development of potent antibacterial agents containing benzimidazole, which has led to the appearance of many new molecules that share desirable properties [4,17].

Considering the fact that nearly all classes of heterocyclic compounds are biologically active and as a part of our continuous efforts towards the development of more potent antimicrobial agents, we herein report the HypoGen 3D-QSAR pharmacophore-based ligand alignment of eleven arylidene (1H-benzimidazol-1-yl)acetohydrazone derivatives that previously synthesized and characterized with antibacterial activity against Rhizobium radiobacter. In addition, docking simulation was also performed to position the compounds tested in the active sites of certain target proteins in order to explore the probable binding modes of these compounds on the active sites.

MATERIALS AND METHODS

Materials and dataset for analysis

Eleven compounds of arylidene (1H-benzimidazol-1-yl)acetohydrazones were synthesized and the chemical structure are shown in Table 1. The synthesis information and characterization data of the target compounds were previously published [17]. The biological activity of these derivatives against bacterial strain of Rhizobium radiobacter (Family: Rhizobiaceae) was tested using 2,3,5-triphenyltetrazolium chloride (TTC, Sigma-Aldrich Co. USA) as a chromogenic marker [20,21]. Nutrient Agar (NA, Oxoid Ltd. Basingstoke, Hampshire, UK) medium was used to re-activate and propagate the tested bacterium. The biological data against Rhizobium radiobacter as MIC values (minimum inhibitory concentration) were converted into pMIC (-log MIC) for use in 3D-QSAR pharmacophore-based ligand alignment analysis.

Alignment rule

The alignment rule was defined by a chemical function-mapping method and therefore based on a geometric fit of the chemical functions of the molecules to the chemical features of the pharmacophore. All generated conformations were developed in Discovery Studio (DS) 2.5 software (Accelrys software Inc.) using its accurate pattern-matching 3D alignment algorithm. The compounds were converted to 3D structure and minimized its MMFF94 energy with 200 iteration limit and energy threshold value of 15 kcal/mol above the global energy minimum until a local energy minimum was reached [22,23]. This algorithm allows all internal coordinates to vary and energetically analyses all rings and chains. The conformation with highest fit value (i.e., best fitting the pharmacophore) was assumed as the bioactive conformation for each compound.

Pharmacophore model generation

The pharmacophore models were created for the aligned molecules using common feature pharmacophore generation in DS software. HipHop module of Catalyst which was popularly known for common feature pharmacophore generation is available in this program as Common Feature Pharmacophore Generation protocol [24]. The default value of 2.97 Å for the Minimum Inter-feature Distance was changed to 2 during pharmacophoregeneration. Pharmacophore sites of a ligand are represented in the 3D space by a set of points. Other parameters, like the maximum number of 255 conformers and an energy threshold value of 20 kcal/mol above the global energy minimum, were chosen during conformation generation. During the pharmacophore hypothesis generation, pharmacophoric features such as hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), hydrophobic feature (Hyd), and aromatic region (Ar) were used to create reliable pharmacophore sites for the energy calculated ligands for our experimental results. The different pharmacophore hypothesis produced were further examined by using a scoring function so that it produced the best alignment of the ligands which are active yet also incorporating the features from the inactive to make the model more versatile. Hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), hydrophobic feature (Hyd), and aromatic region (Ar) chemical features were chosen on the basis of the chemical characteristics of the compounds using Feature Mapping protocol available in DS. The standard hydrogen bond acceptor/donor functions can be effectively used in place of the metal bonding chemical function, as there are no special structures available for metal bonding in the DS feature dictionary [25,26]. Ten pharmacophore models were produced and a best hypothesis has a high fit score and low RMSD value of the site points. Finally, the best pharmacophore model was used for mapping all the molecules.

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Table 1: Chemical structure of arylidene (benzimidazol-1-yl)acetohydrazone derivatives.

Code Structure Molecular Weight

1 N

N

CH2CONHNH2

190.20

2 N

N

CH2CONHNH2

CH3204.23

3N

N

CH2CONHN CH 278.31

4

N

N

CH2CONHN CH

OH

294.31

5

N

N

CH2CONHN CH

OH

294.31

6

N

N

CH2CONHN CH

OCH3

308.33

7N

N

CH2CONHN CH

CH3

292.34

8

N

N

CH2CONHN CH

CH3

OH

308.33

9

N

N

CH2CONHN CH

CH3

OH

308.33

10N

N

CH2CONHN CH

O

268.27

11N

N

CH2CONHN CH

O

CH3

282.3

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3D-QSAR pharmacophore-based ligand alignment

3D-QSAR Pharmacophore Generation protocol (HypoGen algorithm protocol of CATALYST) was performed with the Discovery Studio 2.5 software (Accelrys software Inc.) [27-29]. This program generated top scoring predictive 3D-QSAR pharmacophore models via aligning dirrerent conformations with a maximum tree depth of five with an inter site distance of 2.96 Å. It builds the simplest hypotheses which give the best correlation among calculated activities and experimental activities. Selecting the chemical feature is one of the most important steps in generating 3D-QSAR pharmacophore. Feature mapping module from DS was used to select the chemical features for hypothesis generation. While generating hypotheses HBA, HBD, Hyd, and Ar features were selected with the minimum of zero to maximum of five features were selected. The ‘Uncertainty’ values for the 11 compounds were set to 3, and the default values for other parameters were kept constant. Consequently, the pharmacophore models were calculated using the 3D-QSAR Pharmacophore module and the top 10 scoring hypotheses obtained. The qualities of the hypotheses depended on the fixed cost, the null cost and the total cost values [30,31]. A compound is considered active, if its activity value satisfies the following condition (Equation 1):

(MA × UncMA)-(A/UncA)>0.0 (1)

Where MA is the activity of the most active compound, “Unc” is the uncertainty of the compounds, and A is the activity of the training compounds. A compound is defined as being inactive if it satisfies Equation 2:

Log(Act)-log(MAct)>BS (2)

Where, ‘BS’ is the bioactivity spread (equals 3.5 by default) [29,32,33].

Mapping of the tested compounds on the pharmacophore model

In order to validate the pharmacophore model, the tested compounds were mapped or aligned on to the best Hypo model by Ligand Pharmacophore Mapping protocol in DS 2.5 software. The alignment of these compounds with the model was analyzed to validate the model.

Protein target search

To predict the ligand-receptor interaction, the pharmacophore model that used for mapping the molecules was submitted by pharm mapper web search with maximum generated conformations was 300 and the number of reserved matched targets was 300. The top five targets ranked by fit score and z`-score in descending order were selected and used for docking studies.

Molecular docking

A Molecular Operating Environment (MOE 2014.13, Chemical Computing Group Inc, Montreal, Quebec, Canada, software was used for molecular docking of the synthesized Compounds on the target proteins [34]. The compounds were converted to 3D structure and minimized its MMFF94 energy with 200 iteration limit and energy threshold value of 15 kcal/mol above the global energy minimum until a local energy minimum was reached [22,23]. This algorithm allows all internal coordinates to vary and energetically analyzes all rings and chains. A crystal structure of Cystathionine beta-synthase (PDB:1JBQ), Ribonucleoside-diphosphatereductase 2 subunit alpha (PDB:1PEO), Aspartate aminotransferase (PDB:1AHY), Glutamate decarboxylase beta (PDB:1PMM), and 3-Hydroxy-3-methylglutaryl-coenzyme A reductase (PDB:2Q6B) which were obtained at 3.58 Å and downloaded from the protein data bank (PDB). This structure was protonated in MOE software [34]. The active site was defined with a radius of 4 Å around the bound inhibitor in the crystal structure of each protein. The triangle-matching algorithm of MOE software was selected to dock selected identified compounds to the active site of the protein. The scoring function shall conform to the following parameters: (1) Specify the ASE score to rank the poses exited by the placement step; (2) Specifying the Forcefield refinement to release the poses; (3) Specifying affinity ΔG scoring to classify the poses using the refinement step [23,35,36]. The free energy of the binding was calculated from the contributions of the hydrophobic, ionic, hydrogenated and van der Waals interactions between protein and ligand, intramolecular hydrogen bonds and ligand strains. We have observed that the docking poses were classified by the calculation of the free binding energy in the S field.

RESULTS AND DISCUSSION

3D alignment and pharmacophore elucidation of the tested compounds

The pharmacophore elucidation for eleven compounds of arylidene (1H-benzimidazol-1-yl)acetohydrazones

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(Table 1) were performed. In this study, the final aligned molecules as shown in Figure 1 were used to generate the pharmacophore model of this class of compounds. The 3D Pharmacophoric design methods take into account both three-dimensional structures and modes of binding of target receptors and inhibitors in order to identify regions that are likely to the specific receptor-inhibitor interaction. The automatic construction and visualization of the 3D pharmacophore models from the structural data of arylidene (1H-benzimidazol-1-yl)acetohydrazone derivatives was created by DS software and the results of the ten top-scored 3D hypothetical pharmacophores generation are shown in Table 2 with their statistical parameters. The investigated pharmacophoric features included HBA, HBD, Hyd, and Ar. Although the hypotheses generated have a similar number of pharmacophoric characteristics, they vary in composition, orientation and vectorial directions. The basic structural requirements identified by DS software dependably consist of HBA or HBD or both for metal binding. In addition, all compounds have a hydrophobic cap region to fit and bind with its hydrophobic residues of target protein.

Figure 1: Molecular alignments of the training set molecules used in the present study, obtained from the pharmacophore model alignment.

Table 2: Results of the ten top-scored 3D hypothetical pharmacophores generation with nformation of statistical significance and predictive power.

Hypothesis Features Rank Direct hit Partial hit Max. fit Total coast (bits) RMSD Correlation

Hypo 1 Hyd, HBA, HBA, Ar 96.026 11111111111 00000000000 6.07 54.12 0.44 0.91

Hypo 2 Hyd, HBA, HBA, Ar 95.934 11111111111 00000000000 5.84 54.18 0.44 0.89

Hypo 3 Hyd, HBA, HBA, Ar 94.966 11111111111 00000000000 6.00 54.18 0.44 0.89

Hypo 4 Hyd, HBA, HBA, Ar 93.876 11111111111 00000000000 5.32 54.21 0.45 0.89

Hypo 5 Hyd, HBA, HBA, Ar 93.871 11111111111 00000000000 5.77 54.26 0.46 0.93

Hypo 6 Hyd, HBA, HBA, Ar 93.757 11111111111 00000000000 5.35 54.42 0.49 0.87

Hypo 7 Hyd, HBD, HBA, Ar 93.424 11111111111 00000000000 5.79 54.54 0.47 0.90

Hypo 8 Hyd, HBA, HBA, Ar 92.605 11111111111 00000000000 5.76 54.55 0.51 0.87

Hypo 9 Hyd, HBA, HBA, Ar 92.509 11111111111 00000000000 5.82 54.58 0.52 0.86

Hypo 10 Hyd, HBA, HBA, Ar 92.038 11111111111 00000000000 5.81 54.63 0.53 0.85

RMSD: Root Mean Square Deviation; Ar: Aromatic Region; Hyd: Hydrophobic region; HBD: Hydrogen Bond Donor; HBA: Hydrogen Bond Acceptor. The null cost, fixed cost and the configuration cost are 98.16, 53.08 and 14.95, respectively.

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The highest ranked pharmacophore (Hypo 1) was chosen as a best model and used for mapping modes of the compounds. Figure 2 shows the best pharmacophore model (Hypo 1) include the chemical features (Figure 2A) with spatial relationship and geometric parameters (Figure 2B). Significantly, the best pharmacophore (Hypo 1) categorized by two functions of hydrogen bond acceptor (lipid) and two aromatic hydrophobic functions is statistically the most important pharmacophore.

Figure 2: The best MOE pharmacophore model (Hypo 1). (A) Chemical features present in model 1 (B) 3D spatial relationship and geometric parameters of Hypo 1 (C) Mapping of the most active compound (4) on the best pharmacophore model (D) Mapping of the most active compound 4 on the best pharmacophore model with excluded volume. Pharmacophore features are color-coded: green, aromatic ring (Ar); cyan, hydrogen bond acceptor (HBA) and metal ligator ML); magenta, hydrogen bond donor (HBD), and gray, excluded volume (EV).

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Mapping of the test set compounds on the pharmacophore model

The tested compounds were mapped onto Hypo 1 using DS 2.5 software, with scoring the orientation of a mapped compound within the hypothesis features, using a ‘‘fit value’’ score (Table 3).

The compounds map the two hydrogen-bond acceptor sites and one of the hydrophobic sites. Surprisingly, compounds 4 which showed the highest antibacterial activity maps (has the required spatial distribution of the four essential features) completely (fit value=5.31) with the pharmacophore Hypo 1 (Figures 2C and 2D) followed by compounds 5 and 8 with fit values of 5.08 and 5.11, respectively and the other compounds map in varying degrees. Compounds 1 and 2, which indicated the lowest antibacterial activity, show the lowest fit values (2.13 and 2.33, respectively).

Table 3: Output for Pharmacophore model (Hypo 1) mapping of the compounds.

Compound MIC (mg/L)

pMIC (mole/L)Fitness score Error Activity

rankActual Predicted from DS program

Predicted from Equation 1

1 400 2.54 2.39 2.59 3.5 1.4 Inactive

2 550 2.71 2.43 2.61 3.52 1.9 Inactive

3 75 3.57 3.77 3.75 4.72 -1.6 Active

4 15 4.29 4.29 4.22 5.22 -1 Active

5 20 4.17 3.94 3.88 4.86 5.7 Active

6 75 3.61 3.78 3.72 4.69 -1.5 Active

7 200 3.16 3.47 3.42 4.38 -1.7 Inactive

8 50 3.79 3.81 3.74 4.71 -1 Active

9 125 3.39 3.17 3.13 4.07 1.7 Active

10 45 3.78 3.74 3.73 4.7 1.1 Active

11 150 3.27 3.52 3.5 4.46 -1.8 InactiveMIC: Minimum Inhibitory Concentration; pMIC: -log MIC; Eq. 1:pMIC =0.9479 Fit value-0.727 (n=11, R2= 0.8874). Error was calculated by DS program and it is the difference between the predicted and actual pMIC values. Fitness score indicates how well the features in the pharmacophore overlap the chemical features in the molecule. Fitness score=weight × [max(0.1-SSE)] where SSE is the sum of squared errors of prediction. Activity rank is based on the Equations 1 and 2.

As a quick and primary validation of Hypo 1, the mapping of the compounds makes it possible to demonstrate a good agreement between the fit value and the biological activity (Table 4). The initial study of the results presented in this table shows a good fitness score (3.50-5.22) between the precision score and the biological activity of the test compounds with the highest score of 5.22 for compound 4. The most active compounds (4 and 5) displayed fit scores of 5.22 and 4.86, respectively while the lowest active compounds (1 and 2) showed values of 3.50 and 3.52, respectively. The other compounds showed moderate activity with a score range of 4.07-4.72.

Table 4: Actual pMIC of arylidene (benzimidazol-1-yl)acetohydrazonederivatives and their predective activity obtained from 3D-QSAR pharmacophore Hypo 1 for Rhizobium radiobacter.

Compound MIC (mg/L)

pMIC (mole/L)Fitness score Error Activity rank

Actual Predicted from DS program

Predicted from Equation 1

1 400 2.54 2.39 2.59 3.5 1.4 Inactive

2 550 2.71 2.43 2.61 3.52 1.9 Inactive

3 75 3.57 3.77 3.75 4.72 -1.6 Active

4 15 4.29 4.29 4.22 5.22 -1 Active

5 20 4.17 3.94 3.88 4.86 5.7 Active

6 75 3.61 3.78 3.72 4.69 -1.5 Active

7 200 3.16 3.47 3.42 4.38 -1.7 Inactive

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8 50 3.79 3.81 3.74 4.71 -1 Active

9 125 3.39 3.17 3.13 4.07 1.7 Active

10 45 3.78 3.74 3.73 4.7 1.1 Active

11 150 3.27 3.52 3.5 4.46 -1.8 InactiveMIC: Minimum Inhibitory Concentration; pMIC: -log MIC; Eq. 1:pMIC =0.9479 Fit value-0.727 (n=11, R2= 0.8874). Error was calculated by DS program and it is the difference between the predicted and actual pMIC values. Fitness score indicates how well the features in the pharmacophore overlap the chemical features in the molecule. Fitness score=weight × [max(0.1-SSE)] where SSE is the sum of squared errors of prediction. Activity rank is based on the Equations 1 and 2.

Figure 2 shows the mapping of the most active compound 4 with the Hypo 1. It was observed that this compound aligned well with the pharmacophoric characteristics of the predicted model. Therefore, this model describes phamacophoric characteristics that support better reactivity and can predict the activities of external compounds.

Antibacterial actiovity and 3D-QSAR pharmacophore-based ligand alignment

The antibacterial activity presented in Table 4 shows that salicylaldehyde(1H-benzimidazol-1-yl)acetohydrazone (compound 4) having ortho-OH group on the phenyl ring was the most active compound with MIC of 15 mg/L against Rhizobium radiobacter. Followed in the descending order by 4-hydroxybenzaldehyde(1H-benzimidazol-1-yl)acetohydrazone (5), furfural(1H-benzimidazol-1-yl)acetohydrazone (10), and salicylaldehyde(2-methyl-1H-benzimidazol-1-yl)acetohydrazone (8)(MIC=20, 45, and 50 mg/L, respectively). This result confirms that substitution with a hydroxyl group at the ortho or para positions on the phenyl ring enhanced the antibacterial activity over other compounds. Benzimidazol-1-yl) acetohydrazide (1) and 2- (2-methylbenzimidazol-1-yl) acetohydrazide (2) were the lowest (MIC=400 and 550 mg/L, respectively).

3D-QSAR pharmacophoregeneration module in DS software was used to construct pharmacophore model using HBA, HBD, Hyd, and Archemical features. Ten top-scored hypotheses were produced based on the activity values of the tested molecules (Table 2). The best ten hypotheses contain features of HBD, HBA, Ar, and Hyd. The statistical significance of the pharmacophores (hypotheses) obtained is assessed based on their cost relative to the null hypothesis and their correlation coefficients [37]. The "fixed cost", which is the simplest model that fits perfectly with all data and the second, is known as "null cost", which represents the highest cost of a pharmacophore with no topographies that the activity is the average of the activity data of the training set molecules. A significant pharmacophore hypothesis may result when the difference between these two values is significant; A 40 to 60 bit value for a pharmacophor hypothesis may designate that it has a likelihood of 75% to 90% correlating the data. Superlatively, the difference between the fixed cost and the null cost should be greater than or equal to 60 bits. The total costs of pharmacophores varied between 54.12 and 54.63 bits and the difference between the fixed cost and the null cost is 45.08. In addition, the total cost of any pharmacophore hypothesis is close to the fixed cost, which provide that any model obtained can be significantly useful. The fixed and total cost values of Hypo1 are 53.08 and 54.12, respectively shows very less difference (Table 2), demonstrating good prediction ability of this Hypo. The cost of configuration and cost of error are two other parameters also define the value of any pharmacophor hypothesis with conceivable predictive values. The cost of the configuration, which is also known as entropy cost, depends on the complexity of the pharmacophore hypothesis space and the cost of error, which depends on RMSD differences between the assessed and he actual activities of the training molecules. The RMS variances signify the quality of the relationship between the estimated and the actual activity data. The correlation coefficients were found to be between 0.85 to 0.91 for the ten models, and the RMSD values ranged between 0.44 and 0.53.

Figure 3 represents the correlation between negative logarithms of the minimal inhibitor concentration in mol/L (pMIC actual and predicted) as dependent values versus the fitness score values obtained from the DS 2.5 program as an independent value against Rhizobium radiobacter. In addition, the correlation between fit score values against actual pMIC produced linear model (Equation 3) that showed very good statistics and was used successfully to calculate the activity of the compounds as shown in Table 4.

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y = 0.9479x - 0.727R² = 0.8874

2.0

2.5

3.0

3.5

4.0

4.5

3.00 3.50 4.00 4.50 5.00 5.50

pMIC

(mol

e/L)

Fitness score value

pMIC (Actual)pMIC (predicted)Linear (pMIC (Actual))

Figure 3: A chart representing the correlation between negative logarithm of the minimal inhibitor concentration in mol/L (pMIC actual and predicted) as dependent values versus the fitness score

obtained from DS 2.5 program as an independent value against R.radiobacter.pMIC=0.9479 Fit value-0.727 (n=11, R2=0.8874) (3)

Where n is the number of compounds and R2 is the squared correlation coefficient. The antibacterial activity of any compound can be estimated from a Hypo 1 through this equation [38,39].

Ghorab and others predicted the biological activity of a series of quinazolines, triazoloquinazolines, and triazinoquinazoline derivatives using pharmacophore model with LigandScout program against Staphylococcus aureus [40]. They reported that the degree of binding of the tested compounds to the hypothetical model generated revealed a qualitative measure of the more or less microbial inhibition of Staphylococcus aureus.

Molecular docking

With the in vitro antibacterial results in hand, it is believed that it is worth doing in silico studies to support the in vitro activity. In the present study, the automated docking was used to determine the orientation of arylidene (1H-benzimidazol-1-yl)acetohydrazoneinhibitors bound in the active site of the target enzymes include cystathionine beta-synthase (1JBQ), ribonucleoside-diphosphatereductase 2 subunit alpha (1PEO), aspartate aminotransferase (1AHY), glutamate decarboxylase beta (1PMM), and 3-hydroxy-3-methylglutaryl-coenzyme A reductase (2Q6B) (Table 5). These top five targets ranked by fit score (4.513-3.852) and z`-score (3.412-1.890) in descending order were obtained from pharm mapper web search using Hypo 1. The structures of the compounds as well as the enzymes were kept flexible to obtain different binding conformations and the best-docked complex obtained from it was analyzed in detail. The molecular docking protocol was validated by extracting the native specific ligand of each enzyme from the binding site and then docking the conformations of the compounds to the binding site with less than 4ºA which validating the reliability and reproducibility of the docking procedure.

Table 5: The best five target proteins for arylidene (benzimidazol-1-yl)acetohydrazones obtained from pharm mapper web search.

Rank Target PDB ID Target name Number of Feature Fit score z'-score

1 1JBQ Cystathionine beta-synthase 7 4.513 3.412

2 1PEO Ribonucleoside-diphosphatereductase 2 subunit alpha 7 4.331 3.15

3 1AHY Aspartate aminotransferase 7 4.5 2.2274 1PMM Glutamate decarboxylase beta 6 4.438 1.9365 2Q6B 3-hydroxy-3-methylglutaryl-coenzyme A reductase 8 3.852 1.89

z`-score is a score generated from the molecule’s fit score and a library score matrix calculated beforehand.

The results of the molecular docking have been analyzed based on the docking score (ΔG, kcal/mol), hydrogen bonds and close van der Waals contacts (Table 6). In view of these parameters, the binding affinity of the compounds with respect to the five enzymes was discussed. The data show the intermolecular interaction energy values obtained from the docking calculation. Analysis of the docking poses showed that the eleven products had good binding affinity to the sites of all the target enzymes with docking energy ranging from -8.69 to -15.62 kcal/mol.

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Table 6: Molecular docking, binding scores and binding reactions of compounds 1-11 within the active sites of target enzymes. Residues/water molecules participating in hydrogen bonds and close van der Waals contacts (<4 Å) with the inhibitors are shown.

Comp.

Cystathionine beta-synthase

(PDB: 1JBQ)

Ribonucleoside-diphosphatereductase 2 subunit alpha (PDB:

1PEO)

Aspartate aminotransferase

(PDB: 1AHY)

Glutamate decarboxylase beta (PDB: 1PMM)

3-Hydroxy-3-methylglutaryl-

coenzyme A reductase (PDB: 2Q6B)

Docking score, ΔG (kcal/mol)

H-bondsDocking

score, ΔG (kcal/mol)

H-bondsDocking

score, ΔG (kcal/mol)

H-bondsDocking

score, ΔG (kcal/mol)

H-bondsDocking

score, ΔG (kcal/mol)

H-bonds

1 -10.51Gly256, Ser254, Ser349

-13.59Gly230, Pro233, Ser229

-12.63

Ser255, Ser257, Lys258, Tyr70, Trp140

-12.09Gln347, Gly216, Gly349, Ser214,

Trp215-10.75

Ala564, Arg568, Asn567

2 -11.41 Gly A349 -10.57Ser228, Ser229

-12.78

Ser255, Ser257, Trp140, Lys258

-12.03 Asn98 -11.34

Asp690, Glu559, Lys691, Lys864

3 -9.14Ser50, Thr313

-10.21Asn186, met187, MG715

-11.68Ala253, Asp222, Asn194

-10.42 Ala348, Asn98 -10.09Leu862, Lys864

4 -9.92

Arg B422, Asp A86, Ile A85,

Cys B64, Glu A88

-11.8Asn186, Ser228

-13.58

Arg266, Arg292, Ser296, Ser297, Lys258, Trp140, Tyr70

-11.95Arg97, Tyr60A,

Trp215-12.73

Asn725, Asp690, Cys561, Lys691, Val561

5 -11.11

Asn149, Gly305, ser254, Ser349

-11.75Asn186, MG715

-15.62

Arg292, Gly36, Ser296, Trp140

-12.9 Glu97A, Gly349 -12.42Asn658, His752, Lys864

6 -9.97

Arg B422, Gln B163, Ile B164, Thr B212

-11.33Asn186, Mg715

-14.22

Arg292, Asp222, Cys192, Ser109, Ser296, Trp140

-11.5 Ala348, Tyr60A -11.63Lys691, Ser565

7 -8.69Trp B96, Leu B99, Pro B60C

-9.66Arg215, Ser228, Ser229

-12.68

Arg292, Gly108, Ser296, Ser297

-10.57 Ala348, Arg175 -11.01

Asn658, His752, Leu862, Lys864, Ser661

8 -11.5Gly A349, Trp B96, Pro B60C

-12.52Mg715, Ser229

-14.42

Arg265, Arg292, Gly108, Ser296, Ser297, Trp140

-11.86 Arg97, Trp96 -12.65

Asn658, His861, Leu862, Lys864

9 -10.15Asn149, Ser349, Thyr257

-12.16Asp185, Ser228, Ser229

-14.66

Arg265, Arg292,Trp140, Ser109

-13.34 Ala348, Ala350 -12.88Asp690, Lys864

10 -10.42Arg266, Cys52, Val314

-11.08Asp185, MG715, Ser229

-12.21Arg386, Asn194, Trp140

-10.5 Ala348, Arg175 -10.37

Asn658, Lys864, Leu862, Ser661

11 -9.49Arg266, Trp54

-11.73MG715, Ser228

-11.17

Arg292, Trp140, Ser296, Lys258

-10.17 Glu97A -10.75

Asp690, Asn658, Lys691, Lys864, Leu862

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The docking of ligand molecules with enzymes reveals that all the derivatives are exhibiting the bonding with two or the other amino acids in the active pockets which is showed in Figures 4-8. A residue interaction analysis between the protein and the most active compound (4) was tested to study the detailed explanations of the molecular mechanisms involved in antibacterial activity. From the resulting docking structures, it is clear that all products are conveniently attached to the active site of the target enzymes (Figures 4-8), subsequent in various close contacts with the amino acid residues that border the active site. Interaction analysis of the residues could also provide an elucidation of the difference observed in the binding affinity for these molecules. Compound 4 is stabilized within the active site of cystathionine beta-synthase (1JBQ) through extensive van der Waals contacts with Arg B422, Asp A86, Ile A85, Cys B64, and Glu A88 residues (Figure 4). Ribonucleoside-diphosphatereductase 2 subunit alpha (PDB:1PEO)-compound 4 interactions through van der Waals contacts were with Asn186 and Ser228 residues (Figure 5). However, the interaction of compound 4 with the Aspartate aminotransferase (1AHY) through van der Waals as shown in Figure 6 indicates the binding with amino acids of Arg266, Arg292, Ser296, Ser297, Lys258, Trp140, and Tyr70 protein residues. The compound binded with Glutamate decarboxylase beta (PDB:1PMM) through Arg97, Tyr60A, and Trp215 protein residues. (Figure 7). However, it was binded with 3-Hydroxy-3-methylglutaryl-coenzyme A reductase (PDB:2Q6B) through linking with Asn725, Asp690, Cys561, Lys691, and Val561 protein residues (Figure 8).

Figure 4: Docking view of compound 4 in the binding sites of Cystathionine beta-synthase (PDB: 1JBQ).Left: Interaction diagram of compound 4-1JBQ complexstructure(2D) and right the complex structure in stereo view (3D).Hydrogen-bonding interactions between the receptor and the ligand are drawn with an arrowhead to denote the direction of the hydrogen bond. When the hydrogen bond is formed with the residue side chain, the arrow is drawn in green. Residues are annotated with their 3-letter amino acid code.

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Figure 5: Docking view of compound 4 in the binding sites of Ribonucleoside-diphosphatereductase 2 subunit alpha (PDB: 1PEO).Left: Interaction diagram of compound 4-1JBQ complexstructure(2D) and right the complex structure in stereo view (3D).Hydrogen-bonding interactions between the receptor and the ligand are drawn with an arrowhead to denote the direction of the hydrogen bond. When the hydrogen bond is formed with the residue side chain, the arrow is drawn in green. Residues are annotated with their 3-letter amino acid code.

Figure 6: Docking view of compound 4 in the binding sites of Aspartate aminotransferase (PDB: 1AHY).Left: Interaction diagram of compound 4-1JBQ complexstructure(2D) and right the complex structure in stereo view (3D).Hydrogen-bonding interactions between the receptor and the ligand are drawn with an arrowhead to denote the direction of the hydrogen bond. When the hydrogen bond is formed with the residue side chain, the arrow is drawn in green. Residues are annotated with their 3-letter amino acid code.

Figure 7: Docking view of compound 4 in the binding sites of Glutamate decarboxylase beta (PDB: 1PMM).Left: Interaction diagram of compound 4-1JBQ complexstructure(2D) and right the complex structure in stereo view (3D).Hydrogen-bonding interactions between the receptor and the ligand are drawn with an arrowhead to denote the direction of the hydrogen bond. When the hydrogen bond is formed with the residue side chain, the arrow is drawn in green. Residues are annotated with their 3-letter

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amino acid code.

Figure 8: Docking view of compound 4 in the binding sites of 3-Hydroxy-3-methylglutaryl-coenzyme A reductase (PDB: 2Q6B).Left: Interaction diagram of compound 4-1JBQ complexstructure(2D) and right the complex structure in stereo view (3D).Hydrogen-bonding interactions between the receptor and the ligand are drawn with an arrowhead to denote the direction of the hydrogen bond. When the hydrogen bond is formed with the residue side chain, the arrow is drawn in green. Residues are annotated with their 3-letter amino acid code.

CONCLUSION

Eleven compounds of arylidene (1H-benzimidazol-1-yl)acetohydrazone were studied as antibacterial agents based on pharmacophore mapping, molecular docking, and protein-ligand interaction. A pharmacophore modelling study of the antibacterial effect of the synthesized compounds against the Rhizobium radiobacter strain revealed that compounds 4, 5, 8 and 10 showed high activity and were able to efficiently satisfy the proposed common feature sites using 3D-QSAR pharmacophore-based ligand alignment. Molecular docking studies revealed that the compounds exhibited good binding affinity towards the sites of all target enzymes with docking energy ranging from -8.69 to -15.62 kcal/mol, indicating that these products can be considered as good inhibitors of the selected enzymes. Therefore, this study has expanded the possibility of developing these acetohydrazone derivatives as promising antimicrobial or other bioactive agents.

CONFLICT OF INTEREST

The authors confirm that this article content has no conflict of interest.

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