# Molecular Design and In-Silico Analysis of Trisubstituted Benzimidazole Derivatives as Ftsz Inhibitor

Authors: Shankar Thapa; Shachindra L. Nargund; Mahalakshmi Suresha Biradar
Journal: Journal of Chemistry
 (2023)

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## Abstract

Tuberculosis (TB) is the fastest spreading infectious disease and one of the top ten diseases that kill millions of people annually. The rapid spread of a multidrug-resistant strain of Mycobacterium tuberculosis leads to multidrug-resistance tuberculosis (MDR-TB), which is very difficult to treat. Filament temperature-sensitive protein ring-Z (Ftsz) protein could be the best target to inhibit bacterial cytokinesis. This research is conducted to predict the antitubercular activity of trisubstituted benzimidazole derivatives targeting FtsZ protein by an in-silico approach (molecular docking, pharmacokinetic parameter, drug likeliness, toxicity prediction, and biological activity prediction). Amine and aldehyde substitutions are used as primary scaffolds to design 20 trisubstituted benzimidazole derivatives for molecular docking. AutoDock vina v.1.2.0 software was used to predict the binding interaction between ligand and receptor (FtsZ, PDB ID : 1RQ7). The drug-likeliness properties and toxicity of ligands were predicted from SwissADMET and ToxiM web servers, respectively. Compound A15 (2,3,5,6-tetrafluoro-N1-{6-fluoro-5-[4-(1H-imidazole-1-yl) phenoxy]-1H-1,3-benzodiazol-2-yl} benzene-1,4-diamine) showed the best binding energy (ΔG = −10.2 kcal/mol/) along with four hydrogen bond interactions (GLY107, PHE180, ASP 184). Similarly, compounds A19 and A20 have the best binding score of −9.8 kcal/mol, with excellent pharmacokinetic parameters. It is found that the binding energy of all ligands (ΔG = −8.0 to −10.2 kcal/mol) is better than the reference compound Moxifloxacin (ΔG = −7.7 kcal/mol). None of the ligands violate Lipinski’s rule, but all ligands’ toxicity is slightly high (>0.8 score). It is reported that the amine-substituted benzimidazole derivatives have better binding energy than the aldehyde substitution. Therefore, it is concluded that compounds A19 and A20 can be the best candidate as Ftsz protein inhibitors but an in-vitro animal study and toxicity study are necessary to validate these data.


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## Body

## 1. Introduction

Tuberculosis (TB) is a rapidly contagious but preventable and curable disease caused by an acid-fast bacillus, Mycobacterium tuberculosis [1]. Tuberculosis has been around for many years and still wreaks havoc on global health systems. Millions of people have died annually (1.6 million death in 2021 [2]), and thousands of new TB reports are being registered daily [3, 4]. It is considered one of the leading causes of death around the world and is causing a global health crisis. This affects the global health economy and the global socio-political and cultural economy as a whole [5]. Although much research has been conducted in the field of infectious disease (tuberculosis), prevention and treatment of this disease remain a major challenge in poor and developing countries [6]. So far, the best treatment for this has proven to be directly observed therapy, short-course (DOTS), a World Health Organization (WHO)-administered combination drug therapy. This regimen requires first-line drug therapy (isoniazid, rifampicin, pyrazinamide, and ethambutol) to be taken for at least 4 to 6 months. Irregularities and dosing errors during the 6-month treatment can lead to an emerging multidrug resistance (MDR) problem [7]. The process of resistance to bacterial strain with any two first-line drugs (especially isoniazid and rifampicin) is considered multidrug resistance [8]. Despite of effective combination, therapy remains a global problem [9]. All the major challenges associated with the treatment of TB demonstrate the need for a new drug design targeting the new receptor/protein for better treatment regimens.To add value to the drug design, filament temperature-sensitive protein ring-Z (FtsZ) could be the best target for a small molecule to treat TB. FtsZ (Figures1 and 2) was discovered in the early 1960s from Escherichia coli (E. coli) but its function to Z ring formation, so the name FtsZ, on bacterial cell division, was discovered in 1991 [12]. FtsZ is a bacterial cell division monomer that becomes a very important binding site for a small molecule [13]. FtsZ is responsible for forming a ring around the cell, called as divisome. FtsZ also has the potential to shrink the divisome and divide the cell. It provides the terminating force during spindle fiber dissociation in a prokaryotic cell. GTPase stabilizing protein controls the life span of the activated state of FtsZ protein. From 1991s onward, due to its role in the karyokinesis of bacteria, its value as a new target for anti-TB drug discovery increases surprisingly. Inhibition of the Ftsz protein during the cell division cycle of a bacterial cell could be the best way to reduce tuberculosis infection [14, 15].Figure 1
Divisome with FtsZ protein [10].Figure 2
Basic trisubstituted benzimidazole [11].Several substituted benzimidazoles have been evaluated for their FtsZ inhibitory activity in preclinical studies. One of the most promising series of compounds is benzo [d] imidazole-2-carboxamide derivative (1), which was developed by Hakeem et al. [16]. Meanwhile, Akinpelu and collagenous conducted the in-silico screening to evaluate the potential inhibitory effect of fusidic acid (2), L-tryptophan (3), citric acid (4), and carbamic acid (5) against FtsZ protein [17]. Some of the derivatized natural products also showed the promising FtsZ inhibition viz 4-bromo di methoxycoumarin (6) and piperine (7) Figure 3 showed the better inhibitory action against FtsZ protein with minimum inhibitory concentration (MIC) of 56.0 ± 4.3 μM and 84.0 ± 2.6 μM, respectively [18]. Similarly, several benzimidazole derivatives are marketed and used for anticancer, antitubercular, antibacterial, antifungal, antiviral, immunosuppression, anticonvulsion, analgesic, anti-inflammatory, and antihypertensive activities [19–21]. These diverse therapeutic activities of benzimidazole moiety make us curious to explore other substitutions for it. In this research, we designed some novel trisubstituted benzimidazole derivatives and performed molecular docking to inhibit the Ftsz protein, which plays a very important role in the replication of Mycobacterium tuberculosis. To support docking results, we predicted the biological activity. We also conducted an in-silico pharmacokinetic parameter and toxicity prediction of trisubstituted benzimidazole derivatives by designing the amine and the aldehyde as a primary substitution. (Figure 3).Figure 3
Approved FtsZ inhibitors for preclinical studies.


## 2. Materials and Methodology

### 2.1. Ligand Selection

The literature survey selected ligands, benzimidazole as core moiety, and significant anti-TB activity/antibacterial [22–25]. The selected ligands were designed for docking with aldehyde (A1–A8) and amine (A9–A20, Table S1, supplementary material) as primary substitutions on it. The 2-dimensional (2D) structure of ligands was drawn on ChemDraw Pro 12.0 software and modified into the 3-dimensional (3D) structure [26]. All 3D structures were minimized by using the Merck Molecular Force Field 94 (MMFF 94) [27] and converted into desired file format by OpenBabel 3.1.1 software for docking [28].


### 2.2. Protein Preparation

The protein is identified by a literature survey, and selection is carried out by conducting the FASTA and RUN BLAST on National Center for Biotechnology Information (NCBI) server (https://www.ncbi.nlm.nih.gov/). From that result, the best Ftsz protein (Protein Data Bank, or PDB ID : 1RQ7 [29], Figure S1 supplementary material) was selected and retrieved from the Protein Data Bank (https://www.rcsb.org/) as a target. It was cocrystal with guanine diphosphate (GDP). The retrieved crystal protein structure was prepared for docking by eliminating the native ligand, water, and hetero atom. Polar hydrogens were added on chain “A” removing chain “B.” BIOVIA Discovery visualizer 2021 software was used for protein preparation [30].


### 2.3. Binding Pocket Identification

The active pocket for Ftsz protein (PDB ID : 1RQ7) was determined by the CastP web server (https://sts.bioe.uic.edu/). Binding pocket amino acids were used for docking, generating the grid box around the protein molecule, and moxifloxacin (A21) was used as a standard drug [31, 32]. The amino acid sequence around the binding pocket is listed in Table 1.Table 1
Binding pocket amino acids.
LEU8, ALA9, VAL10, ILE11, LYS12, GLY34, VAL35, GLU36, ILE38, ALA52, ASP53, VAL54, LYS55, GLU88, LEU89, ARG91, GLY92, ILE198, THR199, ASN22, ASN25, ARG26, ILE28, GLU29, GLN30, GLY31, LEU32, LYS33, LEU48, MET49, SER50, ASP51, GLU102, PHE180, ARG181, ASP184, GLU185, VAL186, LEU188, ASN189, GLN192, GLY193, ASP196, THR200, ILE225, GLY226, SER227, SER260, ALA262, VAL294, ILE295, ASP296, LEU299, GLU302, ARG304, VAL305, THR306


### 2.4. Physiochemical Properties of Protein

The essential properties in the different biological system of protein, Ftsz (PDB ID : 1RQ7), were estimated by using the ExpasyProtParam web server (https://web.expasy.org/protparam), which is a public platform for protein characterization. Significant properties of protein were estimated and utilized for protein structure validation [33].


### 2.5. Prediction of Pharmacokinetic Parameters

Drug-likeliness (Lipinski’s parameters) parameters were determined using the Swiss ADMET server (https://www.swissadme.ch/). The 2D molecular descriptors such as molecular weight (MW), hydrogen bond donor (HBD) count, hydrogen bond acceptor (HBA) count, rotatable bond (RT), and Log po/w(MLPGP) value were calculated [34].


### 2.6. Molecular Docking

The AutoDock Vinav.1.2.0 (https://vina.scripps.edu/), based on scoring function and rapid gradient-optimization conformational search, software was used to determine the molecular interaction of ligands with protein MtbFtsz [35]. The protein in pdb file format was uploaded to the AutoDock software, and the grid dimension (40 × 40 × 40 Å) was set for blind docking [36]. Each coordinate randomly selected as center_x = 119.704, center_y = 92.756, center_z = 10.787 with 1 Å spacing [37]. Ubuntu configuration file was generated using grid coordinate, and all other docking work was performed on Ubuntu 20.04.4 LTS (GNU/Linux 4.4.0-19041-Microsoft x86_64) platform [38]. The binding energy, conventional hydrogen bond, and other interaction within the bond distance of 4 Å were calculated [39] and visualized by BIOVIA Discovery visualizer 2021 software [40]. Autodock vina calculates the docking scoring function by the following formula:(1)∆Gbinding=c1∆Gydw+c2∆GHbond+c3∆Gentropy,where the c1, c2, and c3 are the coefficients obtained from the respective ΔG term [41].


### 2.7. Validation of Molecular Docking

The native ligand from the Ftsz protein was extracted and re-docked with the same protein to calculate the root mean square deviation (RMSD) for validation. PyMOL (https://pymol.org/) software was used to calculate the RMSD value and superimpose all the docked ligands with the redocked native ligand. RMSD value is good when it is in the range of 2 or less than 2 [42].


### 2.8. Biological Activity Prediction

Prediction of Activity Spectra for Substance (PASS,https://way2drug.com/passonline/predict.php) [43] is an online web server for the prediction of the biological activity of small molecules. To support the docking result, we performed a virtual antibacterial activity prediction task.


### 2.9. Toxicity Prediction

ToxiM (https://metagenomics.iiserb.ac.in/ToxiM/), a machine learning (ML) tool for toxicity prediction of small molecules, was used to predict the toxicity of the ligand molecules. Descriptors toxicity prediction model was applied, and CaCO2 permeability (LogPapp) of the compound was also calculated [44]. The toxicity class and lethal dose (LD50) of the compounds were determined by Pro Tox-II server (https://tox-new.charite.de/protox_II/index.php?site=home) [45].


## 2.1. Ligand Selection

The literature survey selected ligands, benzimidazole as core moiety, and significant anti-TB activity/antibacterial [22–25]. The selected ligands were designed for docking with aldehyde (A1–A8) and amine (A9–A20, Table S1, supplementary material) as primary substitutions on it. The 2-dimensional (2D) structure of ligands was drawn on ChemDraw Pro 12.0 software and modified into the 3-dimensional (3D) structure [26]. All 3D structures were minimized by using the Merck Molecular Force Field 94 (MMFF 94) [27] and converted into desired file format by OpenBabel 3.1.1 software for docking [28].


## 2.2. Protein Preparation

The protein is identified by a literature survey, and selection is carried out by conducting the FASTA and RUN BLAST on National Center for Biotechnology Information (NCBI) server (https://www.ncbi.nlm.nih.gov/). From that result, the best Ftsz protein (Protein Data Bank, or PDB ID : 1RQ7 [29], Figure S1 supplementary material) was selected and retrieved from the Protein Data Bank (https://www.rcsb.org/) as a target. It was cocrystal with guanine diphosphate (GDP). The retrieved crystal protein structure was prepared for docking by eliminating the native ligand, water, and hetero atom. Polar hydrogens were added on chain “A” removing chain “B.” BIOVIA Discovery visualizer 2021 software was used for protein preparation [30].


## 2.3. Binding Pocket Identification

The active pocket for Ftsz protein (PDB ID : 1RQ7) was determined by the CastP web server (https://sts.bioe.uic.edu/). Binding pocket amino acids were used for docking, generating the grid box around the protein molecule, and moxifloxacin (A21) was used as a standard drug [31, 32]. The amino acid sequence around the binding pocket is listed in Table 1.Table 1
Binding pocket amino acids.
LEU8, ALA9, VAL10, ILE11, LYS12, GLY34, VAL35, GLU36, ILE38, ALA52, ASP53, VAL54, LYS55, GLU88, LEU89, ARG91, GLY92, ILE198, THR199, ASN22, ASN25, ARG26, ILE28, GLU29, GLN30, GLY31, LEU32, LYS33, LEU48, MET49, SER50, ASP51, GLU102, PHE180, ARG181, ASP184, GLU185, VAL186, LEU188, ASN189, GLN192, GLY193, ASP196, THR200, ILE225, GLY226, SER227, SER260, ALA262, VAL294, ILE295, ASP296, LEU299, GLU302, ARG304, VAL305, THR306


## 2.4. Physiochemical Properties of Protein

The essential properties in the different biological system of protein, Ftsz (PDB ID : 1RQ7), were estimated by using the ExpasyProtParam web server (https://web.expasy.org/protparam), which is a public platform for protein characterization. Significant properties of protein were estimated and utilized for protein structure validation [33].


## 2.5. Prediction of Pharmacokinetic Parameters

Drug-likeliness (Lipinski’s parameters) parameters were determined using the Swiss ADMET server (https://www.swissadme.ch/). The 2D molecular descriptors such as molecular weight (MW), hydrogen bond donor (HBD) count, hydrogen bond acceptor (HBA) count, rotatable bond (RT), and Log po/w(MLPGP) value were calculated [34].


## 2.6. Molecular Docking

The AutoDock Vinav.1.2.0 (https://vina.scripps.edu/), based on scoring function and rapid gradient-optimization conformational search, software was used to determine the molecular interaction of ligands with protein MtbFtsz [35]. The protein in pdb file format was uploaded to the AutoDock software, and the grid dimension (40 × 40 × 40 Å) was set for blind docking [36]. Each coordinate randomly selected as center_x = 119.704, center_y = 92.756, center_z = 10.787 with 1 Å spacing [37]. Ubuntu configuration file was generated using grid coordinate, and all other docking work was performed on Ubuntu 20.04.4 LTS (GNU/Linux 4.4.0-19041-Microsoft x86_64) platform [38]. The binding energy, conventional hydrogen bond, and other interaction within the bond distance of 4 Å were calculated [39] and visualized by BIOVIA Discovery visualizer 2021 software [40]. Autodock vina calculates the docking scoring function by the following formula:(1)∆Gbinding=c1∆Gydw+c2∆GHbond+c3∆Gentropy,where the c1, c2, and c3 are the coefficients obtained from the respective ΔG term [41].


## 2.7. Validation of Molecular Docking

The native ligand from the Ftsz protein was extracted and re-docked with the same protein to calculate the root mean square deviation (RMSD) for validation. PyMOL (https://pymol.org/) software was used to calculate the RMSD value and superimpose all the docked ligands with the redocked native ligand. RMSD value is good when it is in the range of 2 or less than 2 [42].


## 2.8. Biological Activity Prediction

Prediction of Activity Spectra for Substance (PASS,https://way2drug.com/passonline/predict.php) [43] is an online web server for the prediction of the biological activity of small molecules. To support the docking result, we performed a virtual antibacterial activity prediction task.


## 2.9. Toxicity Prediction

ToxiM (https://metagenomics.iiserb.ac.in/ToxiM/), a machine learning (ML) tool for toxicity prediction of small molecules, was used to predict the toxicity of the ligand molecules. Descriptors toxicity prediction model was applied, and CaCO2 permeability (LogPapp) of the compound was also calculated [44]. The toxicity class and lethal dose (LD50) of the compounds were determined by Pro Tox-II server (https://tox-new.charite.de/protox_II/index.php?site=home) [45].


## 3. Result and Discussion

### 3.1. Physiochemical Properties of Protein

The Ftsz protein is a dimer having two chains (chain A and chain B). The binding pocket was completely located in chain A, but both chains’ physiochemical properties were calculated. The instability index of the protein should be less than 40 to be stable. In our case, it was found to be 29.27, and the theoretical isoelectric constant was 4.55. The grand average hydropathy value (GRAVY) of protein was found to be 0.119. Other details are shown in supplementary material TableS2.


### 3.2. Molecular Docking

The binding energy of all the benzimidazole derivatives was determined by AutoDock Vina, and all the binding interactions formed around the 4 Å distance from the ligand were analyzed. The blind docking was performed to identify the best pose, and we chose the best pose (interaction amino acid) out of 10 different poses. We found that all the ligand molecules formed the best pose around the four amino acids of the binding site (GLY105, THR106, PHE180, ASP 184). The role played by various substitutions in making the interaction with the binding pocket amino acid of the protein Ftsz was examined, and biopharmaceutical properties of all those ligands were determined from the SwissADMET server. Along with this, toxicity and drug-likeness properties were also determined. Compound A15 (2,3,5,6-tetrafluoro-N1-{6-fluoro-5-[4-(1H-imidazol-1-yl) phenoxy]-1H-1,3-benzodiazol-2-yl} benzene-1,4-diamine) showed the best binding energy (ΔG = −10.2 kcal/mol) with four hydrogen bond interactions (GLY107, PHE180, ASP 184) with protein, which seems to be very suitable to be drug candidate. This binding score is better than the reference compound Moxifloxacin A21 (ΔG = −7.7 kcal/mol, Figure4). By visualizing the bonding interaction of compound A15 (Figures 5(a) and 5(b)) with protein, the bond distance with three amino acids viz GLY107, PHE180, and ASP 184 was 1.26, 2.57, and 1.93/2.29 Å, respectively. The ASP184 amino acid in the binding pocket appears to form two hydrogen bonds with ligand A15. In both bond formation, the ligand acts as the H-donor, and ASP184 act as the H-acceptor. From the result analysis, it is found that the binding score of all ligands (ΔG = −8.0 to −10.2 kcal/mol, Table 2) is better than the reference compound Moxifloxacin, i.e., A21(ΔG = −7.7 kcal/mol).Figure 4
3D & 2D interaction of reference Moxifloxacin A21 with FtsZ protein.Figure 5
3D and 2D binding interaction of A15 with FtsZ protein.Table 2
Biological activity of top 7 compounds.
CodeActivity (Pa > Pi)PaPiA6Antibacterial/antimitotic——A10Antibacterial/antimitotic0.2180.047A11Antibacterial/antimitotic0.2220.046A15Antibacterial/antimitotic0.1790.069A19Antibacterial/antimitotic0.1950.060A20Antibacterial/antimitotic0.2220.046Pa = probability “to be active”; Pi = probability “to be inactive”.Similarly, compounds A19(N1-{6-fluoro-5-[4-(1H-imidazol-1-yl) phenoxy]-1H-1,3-benzodiazol-2-yl}-2-methylbenzene-1,4-diamine, Figure6) and A20 (N1-{6-fluoro-5-[4-(1H-imidazol-1-yl) phenoxy]-1H-1,3-benzodiazol-2-yl}-3-methylbenzene-1,4-diamine, Figure S2 supplementary material) formed five conventional hydrogen bonds with a binding pocket and maintain their binding score high, i.e., ΔG = −9.8 kcal/mol. Both compounds use the same amino acid (THR106, PHE180, ASP 184) to form a hydrogen bond with protein, but they share different bond distances. Most of the compounds use three binding pocket amino acids (THR106, PHE180, ASP 184) to induce hydrogen bond interaction, but compounds A7, A8, and A17 use four amino acids (THR106, PHE180, ARG 181, ASP 184) from the binding pocket. Analyzing all the 3D interactions in turn, we found that most of the interactions are concentrated in the parent molecule only (Figure 7). It is understood that there is rarely an interaction in the substitution. While saying this, surprisingly, we found that the benzimidazole substituted with amine derivatives (A9–A20) had a better binding score and interaction than the aldehyde derivative (A2–A8), except A4. Among all ligands, the lowest binding energy (ΔG = −8.0 kcal/mol) was recorded by compound A1 (5-[4-(1H-imidazol-1-yl) phenoxy]-1H-1,3-benzodiazol-2-amine, Figure S3 supplementary material) with four H-bond. The RMSD value of all ligands ranges from 1.524 to 2.402 which is acceptable. All other complete bond interactions and the binding score are listed in Table 3.Figure 6
3D and 2D interaction of A19 with FtsZ protein.Figure 7
Probable interactive zone of benzimidazole derivatives with binding pocket.Table 3
Binding energy hydrogen bonding, bond length, other interaction, and RMSD value of ligands.
CodeBinding energy (kcal/mol)Hydrogen bondingBond length (Å)Other interactionsRMSD valueA1−8.0THR106, PHE180, ASP 1842.02, 2.51, 2.09/2.10Π-donor H-bond1.848A2−9.4THR106, PHE180, ASP 1842.13, 2.66, 1.93Π-donor H-bond2.273 [46]A3−9.7THR106, PHE180, ASP 1842.16, 2.54, 1.97Π-donor H-bond2.402 [47]A4−9.4THR106, PHE180, ASP 1842.03, 2.67/2.97, 2.09/2.27Π-donor H-bond, Π-cation1.724A5−9.5THR106, PHE180, ASP 1842.11, 2.61, 1.94Π-donor H-bond1.560A6−9.1THR106, PHE180, ASP 1842.12, 2.61, 1.96Π-donor H-bond, Π-σ bond1.643A7−9.3THR106, PHE180, ARG181, ASP 1842.19, 2.64, 2.85, 1.95Π-donor H-bond2.155 [46]A8−9.3THR106, PHE180, ARG181, ASP 1842.10, 2.61, 3.05, 1.95Π-donor H-bond1.524A9−9.6THR106, PHE180, ASP 1842.07, 2.50, 1.96/2.25/3.37Π-donor H-bond1.639A10−9.7THR106, PHE180, ASP 1841.99, 2.47, 2.03/2.06Π-donor H-bond, C-H-bond1.488A11−9.7THR106, PHE180, ASP 1841.99, 2.59, 2.02/2.06Π-donor H-bond, C-H-bond2.205 [48]A12−9.3GLY107, PHE180, ASP 1842.20, 2.96, 1.88/2.07Π-donor H-bond, Π-σ bond1.514A13−9.2GLY107, PHE180, ASP 1842.24, 2.95, 1.87/1.96Π-donor H-bond, Π-σ bond1.643A14−9.3GLY107, PHE180, ASP 1842.19, 2.95, 1.86/2.05Π-donor H-bond, Π-σ bond1.927A15−10.2GLY107, PHE180, ASP 1842.26, 2.57, 1.93/2.29Π-donor H-bond, Π-σ bond2.016 [49]A16−9.6THR106, PHE180, ASP 1841.97, 2.48/2.08, 3.07/1.99Π-donor H-bond1.702A17−9.7GLY105, THR106, PHE180, ASP 1842.49, 2.02, 2.49 2.02/2.25/3.30Π-donor H-bond2.042 [47]A18−9.7THR106, PHE180, ASP 1842.05, 2.49, 1.99/2.25/3.26Π-donor H-bond1.562A19−9.8THR106, PHE180, ASP 1842.10, 2.53, 1.91/2.19Π-donor H-bond1.797A20−9.8THR106, PHE180, ASP 1841.96, 2.53/3.02, 2.03/2.01Π-donor H-bond1.529A21−7.7ALA70, THR106, ARG140, GLU1362.42, 2.11/2.34, 3.08, 2.39C-H-bond, halogen1.710


### 3.3. Drug Likeliness and Pharmacokinetic Properties

It is common in the field of drug design that if a compound violates Lipinski’s rule of 5, cannot be taken into the next step. It states that “to be a drug candidate, molecular weight (MW) should be less than 500 Dalton, logp less than 5 (or Logpo/w MLPGP less than 4.15), hydrogen bond donor (HBD) less than 5, and hydrogen bond acceptor (HBA) less than 10” [50]. Therefore, studying Lipinski’s rule along with biopharmaceutical parameters revealed whether the docked ligands can further proceed or not. Gastrointestinal (GI) absorption of all the ligands is very good except for A15, A16, and A17 which showed low GI absorption. The entire group of ligands does not penetrate the blood-brain barrier (BBB). Meanwhile, compounds A3, A15, and A17 did not obey Lipinski’s rule of five while other ligands fulfil all the criteria of the rule. Compound A15 which had the highest docking score has a Log po/w (MLPGP) value greater than 4.15 (i.e., 4.22) resulting in Lipinski’s rule violation. Some of the ligands viz A2–A8, A12–A15, and A17 do not inhibit the CYP1A2 enzyme while most of the ligands viz A1, A9–A11, A16, A19, A20 inhibit it. The result of the complete analysis of all these parameters is mentioned in Table 4.Table 4
Drug-likeness and pharmacokinetic properties.
CodeLipinski’s RO5/drug-likenessPharmacokinetics propertiesMWHBAHBDLogpViolationLogsGI abs.CYP1A2 inhibitorBBBLogKpA1309.3422.03Zero−3.79HighYesNo−6.40A2413.4623.20Zero−5.30HighNoNo−5.88A3427.4514.22One (>logp)−5.87HighNoNo−5.25A4413.4522.80Zero−5.27HighNoNo−5.84A5413.4623.20Zero−5.30HighNoNo−5.88A6442.4712.85Zero−5.50HighNoNo−5.92A7443.4722.89Zero−5.37HighNoNo−6.08A8427.4613.41Zero−5.51HighNoNo−5.73A9400.4432.92Zero−5.19HighYesNo−5.86A10400.4432.92Zero−5.19HighYesNo−5.86A11400.4432.92Zero−5.19HighYesNo−5.86A12434.8433.40Zero−5.78LowNoNo−5.62A13434.8433.40Zero−5.78LowNoNo−5.62A14418.4533.29Zero−5.35HighNoNo−5.90A15472.3834.41One (>logp)−5.81LowNoNo−6.01A16445.4632.07Zero−5.59LowYesNo−5.86A17524.3632.65One (>MW)−6.15LowNoNo−6.25A18414.4433.13Zero−5.48HighYesNo−5.69A19414.4433.13Zero−5.48HighYesNo−5.69A20414.4433.13Zero−5.48HighYesNo−5.69A21401.4621.66Zero−2.70HighNoNo−8.32LogKp: skin permeability in cm/s, log S (ESOL): water solubility, MW: molecular weight in gram, HBD: hydrogen bond donor, HBA: hydrogen bond acceptor, BBB: blood-brain barrier.


### 3.4. Biological Activity Analysis

Out of 20 ligands, top seven compounds were selected on the basis of binding energy and screened for antibacterial activity. The antimitotic agents inhibit the cell multiplication in microorganisms (bacteria, virus, etc.) [51]. Compound A6 is not showing any antibacterial activity while the other six compounds are predicted to be active against microorganisms. Compound A20 is active as antimitotic agent having Pa value (0.222) greater than Pi (0.046). Detail activity value is explained in Table 2.


### 3.5. Toxicity Prediction

Out of 20 ligands, the top seven compounds (best binding energy) were selected on the basis of binding energy and screened for toxicity analysis. A compound having a toxicity score greater than or equal to 0.8 is considered toxic. When predicting the toxicity profile of the top 7 ligands, unfortunately, all the ligands’ toxicity score was higher than the normal range (Table5), but the toxicity class for most of the compounds is, relatively, on the safer side. Compounds A16, A18, and A19 belong to toxicity class V with LD50 = 2000 mg/kg. The compound A15 has a toxicity score of 0.909. Compound A15 belongs to class IV with LD50 = 320 mg/kg. The predicted toxicity score, toxicity class, and LD50 value suggest that the ligands molecules could be the good choice for FtsZ inhibition. The CaCO2 permeability (LogPapp) of compound A15 is −5.358.Table 5
Toxicity score and logPapp of top 7 compounds.
CompoundToxicity scoreToxicity classLD50 (mg/kg)LogPappA60.938IV320−4.629A100.942IV320−5.358A110.933IV320−5.358A150.909IV320−5.358A160.938V2000−4.629A180.936V2000−5.358A190.933V2000−5.358Note. “Toxicity classes are defined according to the globally harmonized system of classification of labelling of chemicals (GHS). LD50 values are given in [mg/kg]: class I: fatal if swallowed (LD50 ≤ 5), class II: fatal if swallowed (5 < LD50 ≤ 50), class III: toxic if swallowed (50 < LD50 ≤ 300), class IV: harmful if swallowed (300 < LD50 ≤ 2000), class V: may be harmful if swallowed (2000LD50 ≤ 5000), class VI: nontoxic (LD50 > 5000) [52].”


## 3.1. Physiochemical Properties of Protein

The Ftsz protein is a dimer having two chains (chain A and chain B). The binding pocket was completely located in chain A, but both chains’ physiochemical properties were calculated. The instability index of the protein should be less than 40 to be stable. In our case, it was found to be 29.27, and the theoretical isoelectric constant was 4.55. The grand average hydropathy value (GRAVY) of protein was found to be 0.119. Other details are shown in supplementary material TableS2.


## 3.2. Molecular Docking

The binding energy of all the benzimidazole derivatives was determined by AutoDock Vina, and all the binding interactions formed around the 4 Å distance from the ligand were analyzed. The blind docking was performed to identify the best pose, and we chose the best pose (interaction amino acid) out of 10 different poses. We found that all the ligand molecules formed the best pose around the four amino acids of the binding site (GLY105, THR106, PHE180, ASP 184). The role played by various substitutions in making the interaction with the binding pocket amino acid of the protein Ftsz was examined, and biopharmaceutical properties of all those ligands were determined from the SwissADMET server. Along with this, toxicity and drug-likeness properties were also determined. Compound A15 (2,3,5,6-tetrafluoro-N1-{6-fluoro-5-[4-(1H-imidazol-1-yl) phenoxy]-1H-1,3-benzodiazol-2-yl} benzene-1,4-diamine) showed the best binding energy (ΔG = −10.2 kcal/mol) with four hydrogen bond interactions (GLY107, PHE180, ASP 184) with protein, which seems to be very suitable to be drug candidate. This binding score is better than the reference compound Moxifloxacin A21 (ΔG = −7.7 kcal/mol, Figure4). By visualizing the bonding interaction of compound A15 (Figures 5(a) and 5(b)) with protein, the bond distance with three amino acids viz GLY107, PHE180, and ASP 184 was 1.26, 2.57, and 1.93/2.29 Å, respectively. The ASP184 amino acid in the binding pocket appears to form two hydrogen bonds with ligand A15. In both bond formation, the ligand acts as the H-donor, and ASP184 act as the H-acceptor. From the result analysis, it is found that the binding score of all ligands (ΔG = −8.0 to −10.2 kcal/mol, Table 2) is better than the reference compound Moxifloxacin, i.e., A21(ΔG = −7.7 kcal/mol).Figure 4
3D & 2D interaction of reference Moxifloxacin A21 with FtsZ protein.Figure 5
3D and 2D binding interaction of A15 with FtsZ protein.Table 2
Biological activity of top 7 compounds.
CodeActivity (Pa > Pi)PaPiA6Antibacterial/antimitotic——A10Antibacterial/antimitotic0.2180.047A11Antibacterial/antimitotic0.2220.046A15Antibacterial/antimitotic0.1790.069A19Antibacterial/antimitotic0.1950.060A20Antibacterial/antimitotic0.2220.046Pa = probability “to be active”; Pi = probability “to be inactive”.Similarly, compounds A19(N1-{6-fluoro-5-[4-(1H-imidazol-1-yl) phenoxy]-1H-1,3-benzodiazol-2-yl}-2-methylbenzene-1,4-diamine, Figure6) and A20 (N1-{6-fluoro-5-[4-(1H-imidazol-1-yl) phenoxy]-1H-1,3-benzodiazol-2-yl}-3-methylbenzene-1,4-diamine, Figure S2 supplementary material) formed five conventional hydrogen bonds with a binding pocket and maintain their binding score high, i.e., ΔG = −9.8 kcal/mol. Both compounds use the same amino acid (THR106, PHE180, ASP 184) to form a hydrogen bond with protein, but they share different bond distances. Most of the compounds use three binding pocket amino acids (THR106, PHE180, ASP 184) to induce hydrogen bond interaction, but compounds A7, A8, and A17 use four amino acids (THR106, PHE180, ARG 181, ASP 184) from the binding pocket. Analyzing all the 3D interactions in turn, we found that most of the interactions are concentrated in the parent molecule only (Figure 7). It is understood that there is rarely an interaction in the substitution. While saying this, surprisingly, we found that the benzimidazole substituted with amine derivatives (A9–A20) had a better binding score and interaction than the aldehyde derivative (A2–A8), except A4. Among all ligands, the lowest binding energy (ΔG = −8.0 kcal/mol) was recorded by compound A1 (5-[4-(1H-imidazol-1-yl) phenoxy]-1H-1,3-benzodiazol-2-amine, Figure S3 supplementary material) with four H-bond. The RMSD value of all ligands ranges from 1.524 to 2.402 which is acceptable. All other complete bond interactions and the binding score are listed in Table 3.Figure 6
3D and 2D interaction of A19 with FtsZ protein.Figure 7
Probable interactive zone of benzimidazole derivatives with binding pocket.Table 3
Binding energy hydrogen bonding, bond length, other interaction, and RMSD value of ligands.
CodeBinding energy (kcal/mol)Hydrogen bondingBond length (Å)Other interactionsRMSD valueA1−8.0THR106, PHE180, ASP 1842.02, 2.51, 2.09/2.10Π-donor H-bond1.848A2−9.4THR106, PHE180, ASP 1842.13, 2.66, 1.93Π-donor H-bond2.273 [46]A3−9.7THR106, PHE180, ASP 1842.16, 2.54, 1.97Π-donor H-bond2.402 [47]A4−9.4THR106, PHE180, ASP 1842.03, 2.67/2.97, 2.09/2.27Π-donor H-bond, Π-cation1.724A5−9.5THR106, PHE180, ASP 1842.11, 2.61, 1.94Π-donor H-bond1.560A6−9.1THR106, PHE180, ASP 1842.12, 2.61, 1.96Π-donor H-bond, Π-σ bond1.643A7−9.3THR106, PHE180, ARG181, ASP 1842.19, 2.64, 2.85, 1.95Π-donor H-bond2.155 [46]A8−9.3THR106, PHE180, ARG181, ASP 1842.10, 2.61, 3.05, 1.95Π-donor H-bond1.524A9−9.6THR106, PHE180, ASP 1842.07, 2.50, 1.96/2.25/3.37Π-donor H-bond1.639A10−9.7THR106, PHE180, ASP 1841.99, 2.47, 2.03/2.06Π-donor H-bond, C-H-bond1.488A11−9.7THR106, PHE180, ASP 1841.99, 2.59, 2.02/2.06Π-donor H-bond, C-H-bond2.205 [48]A12−9.3GLY107, PHE180, ASP 1842.20, 2.96, 1.88/2.07Π-donor H-bond, Π-σ bond1.514A13−9.2GLY107, PHE180, ASP 1842.24, 2.95, 1.87/1.96Π-donor H-bond, Π-σ bond1.643A14−9.3GLY107, PHE180, ASP 1842.19, 2.95, 1.86/2.05Π-donor H-bond, Π-σ bond1.927A15−10.2GLY107, PHE180, ASP 1842.26, 2.57, 1.93/2.29Π-donor H-bond, Π-σ bond2.016 [49]A16−9.6THR106, PHE180, ASP 1841.97, 2.48/2.08, 3.07/1.99Π-donor H-bond1.702A17−9.7GLY105, THR106, PHE180, ASP 1842.49, 2.02, 2.49 2.02/2.25/3.30Π-donor H-bond2.042 [47]A18−9.7THR106, PHE180, ASP 1842.05, 2.49, 1.99/2.25/3.26Π-donor H-bond1.562A19−9.8THR106, PHE180, ASP 1842.10, 2.53, 1.91/2.19Π-donor H-bond1.797A20−9.8THR106, PHE180, ASP 1841.96, 2.53/3.02, 2.03/2.01Π-donor H-bond1.529A21−7.7ALA70, THR106, ARG140, GLU1362.42, 2.11/2.34, 3.08, 2.39C-H-bond, halogen1.710


## 3.3. Drug Likeliness and Pharmacokinetic Properties

It is common in the field of drug design that if a compound violates Lipinski’s rule of 5, cannot be taken into the next step. It states that “to be a drug candidate, molecular weight (MW) should be less than 500 Dalton, logp less than 5 (or Logpo/w MLPGP less than 4.15), hydrogen bond donor (HBD) less than 5, and hydrogen bond acceptor (HBA) less than 10” [50]. Therefore, studying Lipinski’s rule along with biopharmaceutical parameters revealed whether the docked ligands can further proceed or not. Gastrointestinal (GI) absorption of all the ligands is very good except for A15, A16, and A17 which showed low GI absorption. The entire group of ligands does not penetrate the blood-brain barrier (BBB). Meanwhile, compounds A3, A15, and A17 did not obey Lipinski’s rule of five while other ligands fulfil all the criteria of the rule. Compound A15 which had the highest docking score has a Log po/w (MLPGP) value greater than 4.15 (i.e., 4.22) resulting in Lipinski’s rule violation. Some of the ligands viz A2–A8, A12–A15, and A17 do not inhibit the CYP1A2 enzyme while most of the ligands viz A1, A9–A11, A16, A19, A20 inhibit it. The result of the complete analysis of all these parameters is mentioned in Table 4.Table 4
Drug-likeness and pharmacokinetic properties.
CodeLipinski’s RO5/drug-likenessPharmacokinetics propertiesMWHBAHBDLogpViolationLogsGI abs.CYP1A2 inhibitorBBBLogKpA1309.3422.03Zero−3.79HighYesNo−6.40A2413.4623.20Zero−5.30HighNoNo−5.88A3427.4514.22One (>logp)−5.87HighNoNo−5.25A4413.4522.80Zero−5.27HighNoNo−5.84A5413.4623.20Zero−5.30HighNoNo−5.88A6442.4712.85Zero−5.50HighNoNo−5.92A7443.4722.89Zero−5.37HighNoNo−6.08A8427.4613.41Zero−5.51HighNoNo−5.73A9400.4432.92Zero−5.19HighYesNo−5.86A10400.4432.92Zero−5.19HighYesNo−5.86A11400.4432.92Zero−5.19HighYesNo−5.86A12434.8433.40Zero−5.78LowNoNo−5.62A13434.8433.40Zero−5.78LowNoNo−5.62A14418.4533.29Zero−5.35HighNoNo−5.90A15472.3834.41One (>logp)−5.81LowNoNo−6.01A16445.4632.07Zero−5.59LowYesNo−5.86A17524.3632.65One (>MW)−6.15LowNoNo−6.25A18414.4433.13Zero−5.48HighYesNo−5.69A19414.4433.13Zero−5.48HighYesNo−5.69A20414.4433.13Zero−5.48HighYesNo−5.69A21401.4621.66Zero−2.70HighNoNo−8.32LogKp: skin permeability in cm/s, log S (ESOL): water solubility, MW: molecular weight in gram, HBD: hydrogen bond donor, HBA: hydrogen bond acceptor, BBB: blood-brain barrier.


## 3.4. Biological Activity Analysis

Out of 20 ligands, top seven compounds were selected on the basis of binding energy and screened for antibacterial activity. The antimitotic agents inhibit the cell multiplication in microorganisms (bacteria, virus, etc.) [51]. Compound A6 is not showing any antibacterial activity while the other six compounds are predicted to be active against microorganisms. Compound A20 is active as antimitotic agent having Pa value (0.222) greater than Pi (0.046). Detail activity value is explained in Table 2.


## 3.5. Toxicity Prediction

Out of 20 ligands, the top seven compounds (best binding energy) were selected on the basis of binding energy and screened for toxicity analysis. A compound having a toxicity score greater than or equal to 0.8 is considered toxic. When predicting the toxicity profile of the top 7 ligands, unfortunately, all the ligands’ toxicity score was higher than the normal range (Table5), but the toxicity class for most of the compounds is, relatively, on the safer side. Compounds A16, A18, and A19 belong to toxicity class V with LD50 = 2000 mg/kg. The compound A15 has a toxicity score of 0.909. Compound A15 belongs to class IV with LD50 = 320 mg/kg. The predicted toxicity score, toxicity class, and LD50 value suggest that the ligands molecules could be the good choice for FtsZ inhibition. The CaCO2 permeability (LogPapp) of compound A15 is −5.358.Table 5
Toxicity score and logPapp of top 7 compounds.
CompoundToxicity scoreToxicity classLD50 (mg/kg)LogPappA60.938IV320−4.629A100.942IV320−5.358A110.933IV320−5.358A150.909IV320−5.358A160.938V2000−4.629A180.936V2000−5.358A190.933V2000−5.358Note. “Toxicity classes are defined according to the globally harmonized system of classification of labelling of chemicals (GHS). LD50 values are given in [mg/kg]: class I: fatal if swallowed (LD50 ≤ 5), class II: fatal if swallowed (5 < LD50 ≤ 50), class III: toxic if swallowed (50 < LD50 ≤ 300), class IV: harmful if swallowed (300 < LD50 ≤ 2000), class V: may be harmful if swallowed (2000LD50 ≤ 5000), class VI: nontoxic (LD50 > 5000) [52].”


## 4. Conclusion

This in-silico research identified the potential candidate to bind with the active site of Ftsz protein by forming three to five hydrogen bond interactions. Docking data are supported by the predicted biological activity. It is found that amine scaffolds have higher potency for making H-bond with protein. Most of the compounds have decent to moderate toxicity scores so further modification of the parent molecule is necessary to minimize it for the drug development stage. Compound A15 has the best binding score but it does not satisfy the pharmacokinetic parameter and Lipinski’s rule of five. With this analysis, we concluded that compounds A19 and A20 can be the best candidate as Ftsz protein inhibitors but in-vitro animal study and acute/chronic toxicity study are necessary to validate these data. This in-silico analysis of trisubstituted benzimidazole derivatives can reach out to the potential drug candidate for TB, but the dissolution parameter and permeability parameter of the candidate should be improved before conducting the drug development process.



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*Source: 9307613-2023-03-01.xml*