Targeting Inhibitor of Enterococcus faecalis: Insights from Comparative Molecular Dynamics and Binding Free Energy Analyses

Abstract

For the past 50 years, antibiotics that target DNA gyrase have proven to be clinically successful. As a result, the search for novel gyrase inhibitors has intensified due to the rise in bacterial resistance. Since it is absent in eukaryotes yet essential in all bacteria, anti-bacterials target it aggressively. Although quinolones are a clinically approved medication, both Gram-positive and Gram-negative bacteria are developing resistance to them, which compromises their therapeutic efficacy. Thus, it is vital to identify novel compounds that can efficiently inhibit DNA gyrase. A recent experimental study shows that the R-enantiomer of compound 1 was likely to be a more favourable stereoisomer than the R-enantiomer in inhibiting the function of DNA gyrase. However, the molecular mechanisms of its selectivity and inhibition remain elusive. To gain insight into the observed inhibitory effect, molecular dynamics simulations have been employed to investigate the inhibitory mechanism as well as selectivity effect. MD simulation revealed that R-enantiomer selectively targeted the ATP-binding pocket residues, with the 2,4 di chloro carbazole ring’s group interacting into the small hydrophobic pocket provided by Asp 25, arg 26, Ile 182, Val 233, Arg 284, and Ala 286 in DNA gyrase. Finding the residues in the catalytic-binding site may pave the way for the development of a new structure-based inhibitor of highly selective DNA gyrase for the treatment of Enterococcus faecalis infection.

1. Introduction

The misuse and overuse of chemotherapeutic drugs and antibiotics have led to the global ineffectiveness of the most significant anti-pathogen weapons whilst also encouraging the progressive growth of bacteria resistant to these drugs [1]. The World Health Organization (WHO) estimates that in 2018, antibiotic-resistant bacteria killed approximately 35,000 people in the US and 33,000 people in Europe [2].

Enterococcus faecalis, or E. faecalis, is becoming more common in hospitals. It is now known to be a major cause of treatment-related issues, including urinary tract, stomach, and pelvic infections. Septicemia and internal carditis could potentially arise from it [3]. Because of its thick cell wall, E. faecalis is easily able to develop intrinsic resistance to a number of pharmaceutical antibiotics, including daptomycin, linezolid, and vancomycin. It can also alter the way that cells divide and multiply [4]. In order to meet the expanding medical requirement, it is essential to constantly search for innovative anti-bacterial drugs with distinct targets that are also curatively effective due to the toxicity, resistance, and proliferation of E. faecalis.

The ATP-dependent enzyme DNA gyrase, also known as topoisomerase II, is necessary for transcription, replication, and chromosome segregation. Because DNA gyrase is absent from mammals and plays a crucial role in the bacterial DNA replication cycle, it has been the focus of much research regarding its potential as a target for antimicrobial drugs [5]. A list of priority diseases [2, 6] that call for the development of novel antibiotics was established by the World Health Organization. Amongst these are E. coli and S. aureus, for which a critical and high priority was assigned. The two subunits of the catalytically active heterotetrameric enzyme are called Gyrase A and Gyrase B (also known as A2B2). The A subunit splits and reassembles the double DNA strand, whereas the B subunit, also known as DNA gyrase B, possesses ATPase activity and provides sufficient energy for DNA supercoiling [7, 8].

Zhang et al. have reported that 3,6-dichlorocarbazolyl triazole 1S- and 1R-enantiomer can successfully suppress E. faecalis growth at a low inhibitory level of 2 μg/mL Figure 1. In comparison to norfloxacin, the active molecule 1S- and 1R R-enantiomer showed a lower potential to induce bacterial resistance and swiftly exerted bactericidal effect. Compounds 1S and 1R exhibited no cytotoxicity against normal human RAW264.7 cells. Further analysis of the mechanism of action showed that the conjugate 1S- and 1R R-enantiomer was membrane active against E. faecalis and could intercalate into the DNA of resistant E. faecalis to form a 1S- and 1R-enantiomer-DNA complex, which could explain its antimicrobial activity.

Figure 1.

2D structure of 1R-, and 1S-enantiomer.

It was demonstrated that the R-enantiomer of 1 was probably a more advantageous stereoisomer for blocking DNA gyrase activity. Figure 2: They expected that the 1R-enantiomer would bind to these enzymes more selectively than the 1S-enantiomer [9], based on molecular modelling and docking techniques. More knowledge regarding the inhibitory effects of these compounds is still required, especially at the ATP binding sites of Protein DNA gyrase, in order to comprehend the strategic design of novel target anti-bacterial drugs. In order to achieve this, we use molecular dynamics (MD) simulation to investigate the processes and dynamics of the interaction between DNA gyrase and the 1S- and 1R- enantiomers with regard to selectivity, inhibition, and high binding affinity. These discoveries, in our opinion, will improve the structure-based creation of extremely specific and unique medicinal molecules that may successfully and selectively target the bacterial protein DNA gyrase enzyme.

Figure 2.

Molecular visualization of 1S-enantiomer [A], and 1R-enantiomer [B] at the ATP binding sites of DNA gyrase. The ATP binding site residues in DNA gyrase exhibit intermolecular interactions between the 1S- and 1R-enantiomers, as depicted in a′ and b′, respectively. Hydrophobic residues are indicated by black arrows in both proteins.

2. Computational methods

2.1 System preparation and MD simulations

The X-ray crystal structures of E. coli DNA gyrase A (PDB code: 1ZI0) were obtained from the RSCB Protein Data Bank [10]. Next, these structures were configured for molecular dynamics (MD) simulation using UCSF Chimera [11] and the Molecular Molegro Viewer (MMV) [12]. Monomer structure was used in this experiment to save computational costs. The missing residues were subsequently simulated using MODELER 9.19, which was coupled with the Chimera program [13]. The 1S- and 1R-enantiomers were drawn using Chem Draw software [14]. Then, hydrogen atoms were transferred from the receptor to the ligand. Afterwards, as described in the Molecular dynamic simulation, all three of the generated systems were put through 200 ns MD simulations.

2.2 Molecular dynamic simulations

The GPU Amber 14 software package [15] was used for all molecular dynamic simulations. The integrated LEAP module was used for protein optimization and explicit solvation, whilst the AMBER FF14SB force field was used to determine protein properties. After applying a constraint of 100 kcal/mol Å for 2500 stages, the systems were fully reduced for 1000 steps. After that, the systems were heated gradually over 50 ps from 0 to 300 K, maintaining a fixed volume and number of atoms (NVT) and a collision frequency of 1.0 ps⁻¹ with a potential harmonic restraint of 10 kcal/mol Å. The Berendsen barostat [16] was then used to equilibrate the systems without any constraints at a temperature of 300 K and a constant pressure of 1 bar. This was followed by MD production for 200 ns for each system which the SHACK algorithm was used to constrict the bonds of hydrogen atoms.

2.3 Post-dynamic analysis

Every 1 ps, the trajectories produced by the MD simulations were stored, and they were then analysed using the AMBER 14 suit’s CPPTRAJ [17] module. Using Chimera [11] and the Origin data analysis tool [18], all plots and visualizations were finished. These follow the previously published MD simulation procedure that we previously developed [19].

2.3.1 Binding calculations of free energy

Binding free energy estimates are a crucial end-point method that could shed light on the mechanism of binding between a ligand and protein, including both enthalpic and enthropic components [20, 21]. Using the Molecular Mechanics/GB Surface Area technique (MM/GBSA) [22], the free binding energy was computed in order to assess the binding affinity of the docked systems. The average of 1000 snapshots taken from the 100 ns trajectory was used to calculate the binding free energy. The free binding energy computed by this method for each molecular species (complex, ligand, and receptor) can be represented as:

$$\Delta G_{bind}=G_{complex}-G_{receptor}-G_{ligand}$$E1

$$\Delta G_{bind}=E_{gas}+G_{sol}-TS$$E2

$$E_{gas}=E_{int}+E_{vdw}+E_{ele}$$E3

$$G_{sol}=G_{GB}+G_{SA}$$E4

$$G_{SA}=\gamma SASA$$E5

The gas-phase energy, internal energy, coulomb energy, and van der Waals energy are represented by the terms E_gas, E_int, E_ele, and E_vdw, respectively. The FF14SB force field words were used to directly assess the E_gas. The energy involvement from the polar states (GGB) and non-polar states (G) was used to calculate the solution-free energy (Gsol). Using a water probe radius of 1.4 Å, the non-polar solvation energy (GSA) was calculated from the solvent-accessible surface area (SASA), whereas the polar solvation (GGB) contribution was evaluated by solving the GB equation. S and T stand for the solute’s total entropy and temperature, respectively.

2.3.2 Per-residue free energy decomposition analysis

The individual binding free energy contribution of ATP binding site residues to the stability and affinity of our compounds was then estimated using per-residue breakdown. As significant residues may be indicated by considerable residual energy contributions, this will shed further light on the basis of the inhibition displayed by our compounds.

2.3.2.1 DCCM analysis

We examined the fluctuations and motions in the α carbon atoms’ backbone using dynamic cross-correlation analysis [23]. Using the following equations [24, 25, 26, 27], one can compute the cross-correlation elements Cij between Cα atoms of residues i and j of proteins based on structural information retrieved from MD trajectories:

$${\text{C}}_{ij}=\frac{⟨\Delta ri.\Delta rj⟩}{{\left(⟨\Delta {\text{r}}_{\text{i}}^2⟩⟨\Delta {\text{r}}_{\text{j}}^2⟩\right)}^{\frac{1}{2}}}$$E6

where Δ ri is the Cα atom’s displacement from its mean position. The ith Cα atom’s displacement from its averaged position is known as the Δri. Cij = 1 represents significantly correlated movements in the trajectory, whereas Cij = −1 represents highly anti-correlated motions. The movements of i and j are anti-correlated, as seen by the divergence of motion from 1 and −1.

The DCCM matrix was carried out using the CPPTRAJ package in Amber 14, and the matrices were plotted and evaluated using Origin software (www.originlab.com) [18].

2.3.2.2 Principal component analysis

Principal Component Analysis (PCA) is a multivariate statistical technique that filters movements from the biggest to the smallest spatial scales in order to systematically limit the number of dimensions required to characterize protein dynamics [28, 29, 30, 31, 32, 33]. A multivariate statistical technique is applied to protein motions in order to systematically reduce the number of dimensions needed to understand the dynamics of proteins [33]. It is typical practice to screen visible motions from the largest to smallest spatial scale using the decomposition approach. Therefore, by differentiating between the complex’s many conformational modes during dynamic simulations, PCA can be utilized to explain atomic displacement and conformational changes of protein complexes. Eigenvectors and eigenvalues, which show the biological system’s motion’s direction and magnitude, can also be defined using PCA [33].

In this work, 200 ns of MD trajectories were cleaned of ions and solvent molecules using Amber14’s CPPTRAJ module [17]. This stage is scheduled in advance and occurs prior to the MD trajectory processing for PCA. A main component analysis was performed on Cα atoms using 1000 pictures acquired at 100 ps intervals. An internal script was used to determine the first two principal components (PC1 and PC2), and the Cartesian coordinates of the Cα atoms were used to create two covariance matrices. PC1 and PC2 stand for the first two eigenvectors of a covariant matrix. The PC plots were made using Origin software [17].

3. Results and discussion

3.1 Molecular dynamic and system stability

To prevent disrupted motions and simulation artifacts, it is crucial to guarantee that the system remains stable during the 200 ns MD production run, specifically for the unbound and bounded complex. In order to gauge the systems’ stability, the root mean square deviations (RMSD) were tracked during the simulation. The recorded average RMSD values for the entire frames of the systems were 1.88 ± 0.27 Å, 1.65 ± 0.35 Å, and 1.50 ± 0.20 Å, for apo, S-enantiomer-system, and R-enantiomer-system Figure 3A.

Figure 3.

(A) RMSD of the protein backbone atoms’ Cα atoms. (B) Protein residues’ RMSF of each backbone atom’s Cα atom; (C) protein residues’ ROG of Cα atoms; (D) solvent-accessible surface area (SASA) of the backbone atoms’ Cα relative (black) to the start minimized over 200 ns for the DNA gyrase receptor’s ATP binding site with ligands’ 1S (red) and 1R (blue) enantiomers.

Furthermore, in contrast to their unbound forms, which largely displayed structural stability, the binding of 1S- and 1R-enantiomers to these proteins produced a consistent pattern of structural modification, which included an increase in deviation amongst the backbone atoms. The computed average atomic fluctuations for apo, S-enantiomer-system, and R-enantiomer-systems were 8.10 ± 3.39 Å, 1.16 ± 0.71 Å, and 1.15 ± 0.60 Å, respectively Figure 3B.

During the MD simulation, the mass-weighted root mean square distance of a group of atoms from the complex centre of mass was measured to calculate the radius of gyration (ROG), which allowed researchers to evaluate the overall compactness of the protein upon ligand binding [34, 35]. The average ROG values were 19.53 ± 0.15 Å, 19.26 ± 0.11 Å, and 19.14 ± 0.09 Å for apo, S-enantiomer-system, and R-enantiomer-systems, respectively. According to the observed behaviour, R-enantiomer-compound has a highly stiff structure against DNA gyrase receptor Figure 3C.

The solvent-accessible surface area (SASA) of the protein during ligand binding was calculated in order to learn more about how the protein surface interacts with solvent molecules and to acquire insight into the relationship of compactance of the protein hydrophobic core. In order to achieve bimolecular stability, the surface area of the protein that is visible to the solvent was measured [36]. Figure 3D displays the computed average SASA values for the apo, S-enantiomer-system, and R-enantiomer-systems, which were 13598.19 Å, 13567.04 Å, and 13541.04, correspondingly. DNA gyrase shows higher structural stability when attached to the R-enantiomer, which is further confirmed by the SASA results along with observations from RSMD, RMSF, and ROG calculations. Disruption of protein DNA gyrase’s backbone atoms presumably highlights the R-enantiomer’s mechanistic inhibitory effect, as a protein’s induced loss of structural integrity is correlated with its functional loss.

3.2 Binding free energy calculation-based mechanism of binding interactions

To learn more about the binding energetics of the S and R enantiomers as DNA gyrase inhibitors, the total binding free energy was computed. By taking snapshots from the compound trajectories, the binding free energies were determined using AMBER14’s MM-GBSA software.

As shown in Table 1, there was a difference in binding energy of S-enantiomer to DNA Gyrase (−27.92 kcal/mol) compared to that of R-enantiomer (−28.43 kcal/mol). This indicated a more favourable binding of R-enantiomer towards the DNA gyrase compared to S-enantiomer.

Energy components (kcal/mol)
Complex	Δ EvdW	ΔEelec	ΔGgas	ΔGsolv	ΔGbind
S-enantiomer	−36.41 ± 0.17	−32.46 ± 0.37	−68.88 ± 0.32	40.96 ± 0.26	−27.92 ± 0.19
R-enantiomer	−35.82 ± 0.13	−16.56 ± 0.24	−52.39 ± 0.28	29.96 ± 0.16	−28.43 ± 0.18

Table 1.

Shows the calculated energy binding for the S-enantiomer and R-enantiomer compounds against the catalytic-binding site of E. coli DNA GyrB receptor.

More information about the intricate binding process has been made available by breaking down the total free binding energy utilizing the MM-GBSA approach into individual contribution energy. The Van der Waals interaction energies are demonstrated to be in charge of the favourable binding free energies in all systems, whilst the polar solvation energy terms have an unfavourable effect on the inhibitors’ ability to bind.

3.3 Identification of the key residues responsible for inhibitor binding

To gain more insight about the key residues involved in the inhibition process, the total binding energy of R-enantiomer, and S-enantiomer towards the DNA gyrase was further decomposed into the involvement of each site residues. As we can see from Figure 4, the major favourable contribution to S-enantiomer to DNA gyrase binding is predominantly observed from residues Val 7 (−1.28 kcal/mol), Thr 8 (−1.3 kcal/mol), Leu 9 (−0.504 kcal/mol), Ser 10 (−0.48 kcal/mol), Phe 23 (−0.61 kcal/mol), Ile 24 (−0.41 kcal/mol), Asp 25 (−0.521 kcal/mol), Leu27 (−0.488 kcal/mol), Leu 28 (−0.148 kcal/mol), Val 131 (−1.193 kcal/mol), Asp 181 (−1.14 kcal/mol), Gln 283 (−0.636 kcal/mol), Arg 284 (−1.726 kcal/mol), Val 285 (−0.749 kcal/mol), and Ala 286 (−0.731 kcal/mol).

Figure 4.

The energy contributions to the binding and stability of the R- and S-enantiomers at the ATP binding site are displayed in per-residue breakdown plots [A]. The S-enantiomers [A′] and [B′] exhibit corresponding intermolecular interactions.

On the other hand, the major favourable contribution of R-enantiomer compound to the ATP binding site receptor of E. coli DNA Gyrase is predominantly observed from residues Ile 24 (−0.396 kcal/mol), Asp 25 (−0.365 kcal/mol), Arg 26 (−2.677 kcal/mol), Leu 27 (−1.02 kcal/mol), Leu 28 (−0.218 kcal/mol), Thr 78 (−0.127 kcal/mol), Gly 130 (−0.241 kcal/mol), Val 131 (−0.269 kcal/mol), Ser 80 (−0.537 kcal/mol), Leu 181 (−0.395 kcal/mol), Ile 182 (−0.381 kcal/mol), Val 183 (−0.168 kcal/mol), Gly 231 (−0.318 kcal/mol), Ala 232 (−0.366 kcal/mol), Val 233 (−1.42 kcal/mol), Leu 282 (−0.314 kcal/mol), Gln 283 (−1.399 kcal/mol), Arg 284 (−1.757 kcal/mol), and Val 285 (−0.454 kcal/mol).

3.4 Principal component analysis (PCA)

The function of a protein is largely determined by its conformation, [37] and one of the most important methods for assessing an atom’s flexibility during a simulation is PCA. The PCA plot of the three systems employed in this investigation is shown in Figure 5. In this study, we use the principal component (PC) clustering method for significant representation. This approach groups molecular structures into a subset according to their structural similarities, which allows it to explain various conformational states collected during a simulation [38].

Figure 5.

PCA projection of the motion of Cα atoms constructed by plotting the first two principal components (PC1 and PC2) in the conformational space with apo (black), S-enantiomer-complex (red) and R-enantiomer-complex (BLUE) colours, respectively, with DNA gyrase receptor.

The overall mean square motion or the percentage variability of the atom’s positional fluctuation collected in each dimension can be adequately determined using the eigenvalues. Major conformational changes in the apo, S-enantiomer, and R-enantiomer systems were examined using this PCA technique during the duration of 200 ns simulations. Systems with unique molecular behaviour were projected along the direction of their first two principal components (PC1 vs. PC2), or eigenvectors, in order to properly assess their motions.

An apo, S-enantiomer, and R-enantiomer complex scattered plot is shown in Figure 5. The features of the structures shown along the direction of the two primary components highlight the significant differences between the three systems in this plot.

The three systems’ motions are noticeably separated from one another. On the other hand, R-enantiomer-system and its two principal components, PC1 and PC2, showed a more correlated motion than the S-enantiomer and apo systems, which show comparatively less correlated motions.

Compared to the R-enantiomer-complex and S-enantiomer-systems, the apo system seems to be more flexible, which could indicate that when R-enantiomer binds to the protein of DNA gyrase, it causes conformational dynamics that is reflected in PCs as a wave of motions. In contrast, the binding of S-enantiomer protein complex system has a less dynamic effect on the protein compared to R-enantiomer binding complex. A decrease in fluctuation observed in the R-enantiomer results from the impact of S-enantiomer binding on the DNA gyrase protein Figure 5.

3.5 Dynamics cross-correlation matrices (DCCM) analysis

To assess conformational changes of DNA gyrase following ligand interaction, DCCM analysis was carried out on the position of Cα during the simulations to explore the dynamics and existence of associated motions, as illustrated in Figure 6. Yellow-red (colour) regions indicate strongly positive-correlated motions of certain residues, whilst blue-black (colour) regions indicate highly negative-correlated motions of specific residues.

Figure 6.

Dynamic cross-correlation matrix analyses for Apo (A), S-enantiomer-DNA gyrase (B), and R-enantiomer-DNA gyrase (C). Numbers closer to 1 indicate a high correlation, whilst those closer to −1 indicate anti-correlation between pairs of residues.

The three systems that were evaluated for this study showed residue’s overall correlated motions in comparison to anti-correlated motions. According to an analysis of DCCM, the binding of R-enantiomer to the DNA gyrase protein gives the protein structural dynamics, which results in conformational changes that are represented by variations in the associated motions.

As evident in Figure 6, There are correlated sections in the R-enantiomer-complex binding to DNA gyrase protein (Figure 6C). The significantly correlated region is located in residues 50–250 in the DNA gyrase protein. These areas of the receptor are the most dynamic and accept the majority of hydrophobic active site residues. In related development, the S-enantiomer-system relative to R-enantiomer-system exhibits a decrease in correlation between residues 50–250 in the DNA gyrase receptor, respectively. This phenomenon suggests that the conformational state of the protein is not significantly altered by the binding of the R-enantiomer to the DNA gyrase proteins. Consequently, compared to S-enantiomer, which has shown signs of being a very strong agent on DNA gyrase protein, the effectiveness of R-enantiomer as a DNA gyrase inhibitor may be a pipe dream.

3.6 Ligand-residue interaction network profiles

Making structural modifications to medicinal compounds in order to boost bioavailability, lower toxicity, and enhance pharmacokinetics is one goal of drug design [39].

The bacterial DNA enzyme gyrase, a member of the topoisomerase II family, is necessary to stop DNA supercoils from forming when viruses replicate and transcribe their DNA. Therefore, blocking gyrase’s ATPase activity keeps the chromosome in a positively supercoiled state and stops the insertion of negative supercoils in DNA, which could potentially have an impact on cell physiology and division in the future [40].

Figure 7 shows how the R-enantiomer fits into DNA Gyrase’s catalytic active site. It has been shown that the R-enantiomer forms a stable hydrogen bond with Leu 27, Arg 284, and Glu 287. Furthermore, the pi-alkyl and pi-cation interactions of the 2,4 di chloro carbazole ring with Arg26 have been shown. Ultimately, a Pi-alkyl contact has developed between Ala 286 and the triazole ring. Conversely, Arg 284 and the docked S-enantiomer have formed a strong H-bonding contact. It is important to remember that the 2,4-di chloro carbazole ring and Ile 182 and Val 233 have created a pi-alkyl connection. Pi-alky interactions have been created by the pharmacophoric hot spot Arg 26 with the R- and S-enantiomers, respectively.

Figure 7.

The interaction residue of S-enantiomer [A], and R-enantiomer [B] into the Catalytic site of DNA gyrase.

4. Conclusion

The selectivity mechanism of the S- and R-enantiomers against DNA gyrase was examined in the current work using binding free energy analysis and comparative MD simulation. The MM/GBSA method was utilized to evaluate the differential binding of S- and R-enantiomers to these DNA gyrase protein targets. The results indicated favourable interactions, with ΔG values of −27.92 kcal/mol for S-enantiomer and −28.43 kcal/mole for R-enantiomer. Similar binding free energy suggested comparable binding affinities and modes, as was previously shown. The van der Waals energy component in the R-enantiomer appears to be the main energy component causing this synergistic impact, according to the binding free energy component analysis.

The amino acid residues Ile 24, Asp 25, Arg 26, Leu 27, Leu 28, Thr 78, Gly 130, Val 131, Ser 80, Leu 181, Ile 182, Val 183, Gly 231, Ala 232, Val 233, Leu 282, Gln 283, Arg 284, and Val 285 are important in DNA gyrase, according to the breakdown of the total energies into contributions from the active site residues. The findings of our investigation are crucial for comprehending the molecular activity against DNA gyrase and for developing a more powerful selective inhibitor.

Conflict of interest

The authors declare no conflict of interest.