Identification of quinoline scaffolds against emerging drug target glycerophosphodiester phosphodiesterase from Staphylococcus aureus: an integrated in vitro, in silico, and STD-NMR approach

Abstract

Glycerophosphodiester phosphodiesterases (GDPDs) are biocatalysts and highly conserved enzymes in both prokaryotic and eukaryotic organisms. GDPD plays an important role in cellular metabolism by hydrolyzing glycerophosphodiester substrates into smaller but useful molecules, such as glycerol-3-phosphate and its corresponding alcohol. GDPD directly affects the pathogenicity of infectious bacteria, including Staphylococcus aureus, and consequently represents a potential therapeutic drug target. In the current study, the GDPD enzyme from vancomycin-resistant S. aureus (VRSA) was heterologously expressed in BL21(DE3)pLysS and purified. Its catalytic activity was assessed on bis(p-nitrophenyl) phosphate (BpNPP), a commonly used non-physiological substrate for phosphodiesterases. 24 compounds based on pharmacologically important aminoquinolines were evaluated for their inhibitory potential against GDPD. Among these, 15 compounds exhibited significant to moderate inhibitory potential with IC₅₀ values ranging from 79.3 ± 4.8 to 943.5 ± 17.3 µM. Notably, compound 6 exhibited the most potent inhibition (IC₅₀ = 79.3 ± 4.8 µM), as compared to the standard ethylenediaminetetraacetic acid EDTA (IC₅₀ = 468.5 ± 1.09 µM). Circular dichroism (CD) spectroscopy indicates that GDPD has a properly folded secondary structure, which is essential for enzyme activity. CD spectra showed a red shift upon inhibitor addition, from 211 to 237 nm, reflecting alterations in the enzyme's secondary structure. The thermal shift assay was performed to observe the thermal stability and structural integrity of the enzyme in the presence of inhibitors. Saturation transfer difference (STD) NMR spectroscopy provided limited but key insights into the binding interactions of inhibitors with the target enzyme. Binding interactions were further explored using extensive molecular docking studies.

Evaluation of quinoline scaffolds as potential inhibitors of glycerophosphodiester phosphodiesterase (GDPD) for S. aureus by using biochemical and biophysical studies.

Introduction

Glycerophospholipids are the predominant components of membrane lipids, structurally consisting of a glycerol molecule attached to hydrophobic fatty acid chains, which form the nonpolar “tails,” and a phosphate group that constitutes the hydrophilic “head”.^1–3 During normal cellular metabolism and the ongoing remodeling of glycerophospholipids, membrane lipids give rise to intermediate metabolites known as glycerophosphodiesters. Structurally, glycerophosphodiesters contain a glycerol backbone attached to a phosphate group, which is further linked to an additional alcohol or amino alcohol. These metabolites are not stable products and cannot accumulate indefinitely inside the cell.^4,5 To avoid disrupted cell homeostasis and promote normal cell function, cells use glycerophosphodiester phosphodiesterases (GDPDs) to hydrolyze glycerophosphodiester substrates into smaller, useful molecules such as glycerol-3-phosphate and their corresponding alcohols (i.e., choline, ethanolamine, or inositol).^6,7 These alcohols are further utilized in a variety of biosynthetic pathways, whereas glycerol-3-phosphate serves as a precursor in pathways that supply both carbon and phosphate groups for phospholipid biosynthesis. Thus, GDPDs are biocatalysts and play a critical role in recycling for energy production, and provide essential building blocks that support the formation and maintenance of membranes, ensuring the recycling and biosynthesis of lipid components, proper cellular homeostasis, cellular signaling, and overall metabolic balance within the cell.^8–10 It is found in both unicellular and multicellular organisms, including plants, yeast, bacteria, and mammalian cells, indicating that it plays fundamental biological roles in cell persistence and overall metabolic processes. GDPDs are implicated in essential physiological and pathological mechanisms, particularly in prokaryotes (bacteria), where they promote persistence within the host, support nutrient acquisition, and facilitate evasion of immune recognition and clearance.^3,8,11–14 Some of the GDPDs served as pathogenic determents in infectious bacteria including Mycoplasma pneumonia, Haemophilus influenza, and Staphylococcus aureus (S. aureus).^10,15–18

S. aureus is a Gram-positive class of bacteria and a broad-spectrum infectious pathogen, responsible for a variety of clinical manifestations, from superficial skin infections to severe systemic diseases, such as osteomyelitis, septicemia, necrotizing fasciitis, pneumonia, bloodstream infections, and infective endocarditis are the most common.^19–25 Its pathogenicity is closely associated with its ability to adapt to adverse host environments and to exploit a variety of virulence determinants, including surface adhesins, secreted toxins, and hydrolytic enzymes. The clinical impact of S. aureus is further aggravated by the emergence of methicillin-resistant (MRSA) and vancomycin-resistant (VRSA) S. aureus strains, which significantly constrain available therapeutic options.^26–32 Collectively, multidrug-resistant variants not only complicate treatment but are also associated with elevated healthcare costs, prolonged hospital stays, adaptability, morbidity, intrinsic virulence, and mortality globally, underscoring the urgent need for novel therapeutic strategies, as their virulence is mediated by toxins, secreted enzymes, surface adhesins, and biofilm formation, which collectively promote colonization, immune evasion and tissue damage.^33–37

GDPDs have recently attracted attention for their dual roles in cell metabolism and as an emerging virulence-associated enzyme family in S. aureus. GDPDs facilitate and support membrane turnover, the attainment of nutrients from host phospholipids, and enhance survival under conditions of limitation or immune stress. Disruption or inhibition of GDPD activity has been shown to disrupt bacterial fitness and attenuate virulence, highlighting these enzymes as potential targets for novel strategies.^13,38 Recently, we reported the expression, purification, functional, and inhibition studies of the GDPD enzyme from S. aureus (VRSA strain). In that study, we explored a range of pharmaceutical drugs under the umbrella of drug repurposing, against this potential drug target, and identified potential inhibitors as a starting point (Fig. 1).³⁹ Careful inspection of the molecular structure suggested that most of the newly identified inhibitors possess a nitrogen heterocycle as the core nucleus. Thus, we decided to explore synthetic nitrogen-heterocyclic compounds based on aminoquinoline against this potential drug target.⁴⁰

Fig. 1. Active pharmaceutical ingredients as potential S. aureus GDPD inhibitors.

Chemically, aminoquinolines are composed of a fused benzene–pyridine heterocyclic moiety, characterized by a quinoline scaffold, bearing amino group at different positions, such as 2-amino, 4-amino, 5-amino, or 8-aminoquinolines. Amino group enhances reactivity, electronic distribution, physicochemical characteristics, and pharmacological properties such as antimicrobial, anticancer, antiparasitic, antimalarial, and enzyme-inhibitory activities. Additional substitutions on the quinoline scaffold further regulate steric orientation, electronic distribution, π–π stacking, and hydrogen-bonding capacity, thereby affecting binding affinity, biological activity, and molecular recognition. Their structural flexibility makes them an attractive scaffold for the development of GDPD inhibitors.^41–48

This study comprised the expression and purification of the GDPD enzyme and the evaluation of aminoquinoline derivatives as potential inhibitors. Further, the assessment of the structural integrity of GDPD was done using CD (Circular Dichroism) analysis. This technique allowed monitoring of conformational stability and alterations in secondary structure associated with ligand binding, as well as the potential inhibitory effects of compounds on the enzyme's activity.^49,50 In addition, a thermal shift assay is also performed to assess the enzyme's thermal stability in the presence of ligands. Variation in thermal shifts (T_m) suggests differential ligand (inhibitor) binding affinities.^51–53 In addition to that, the ligand-binding potential of the aminoquinoline compounds was studied through saturation transfer difference NMR (STD-NMR) experiments in conjunction with other biophysical studies. STD-NMR is a highly sensitive technique for detecting and characterizing ligands with low to moderate affinity for target biomolecules in solution. It requires only a millisecond timescale to elevate the ligand-to-protein ratio under fast exchange conditions. It allows the characterization of transient ligand–protein interactions, revealing variable inhibitory potentials of the compounds.^54–59

Experimental

General procedure for the synthesis of aminoquinoline derivatives (1–24)⁴⁰

Potassium thiocyanate (1 mmol) was added to the anhydrous acetone (10 mL) in a 100 mL round-bottomed flask. Then, the substituted acyl chlorides were added with continuous stirring, followed by 2 h of refluxing. After 2 h, the mixture was cooled to room temperature. After that, the corresponding 3/5/8-aminoquinoline (1 mmol) was added to the reaction mixture, which was then stirred and refluxed for an additional 12 h. Periodic TLC (hexane/ethyl acetate) was monitored to assess reaction progress. When the reaction was completed, the reaction mixture was poured into ice lead precipitation. The resulting precipitates were filtered, washed with excess water, and then dried under vacuum. The crude products were recrystallized from ethanol to get the pure products in good yields. All compounds were characterized by spectroscopic techniques.

Expression and purification of GDPD enzyme

Recombinant pET-25b(+) vector with the gene of interest of GDPD (GeneScript USA Incorporated, USA) has already been transformed into E. coli BL21(DE3)pLysS by CaCl₂ (Merck KGaA, Germany) heat shock method and confirmed by Sanger's gene sequencing method (Macrogen, South Korea) as described in our earlier published work.³⁹ In the current study, expression cells were grown in Luria Broth (LB) media (1 L) at 37 °C until the optical density (OD) reached 0.6, which was measured on a UV/vis spectrophotometer (PerkinElmer, Inc., USA). At 0.6 OD, IPTG (isopropyl-β-d-thiogalactopyranoside, 1 mM) (Affymetrix Inc., USA) was added to the culture for induction, and the culture was further incubated for 18–20 h at 18 °C. Then, cells were harvested at 8000 rpm for 10 minutes by centrifugation and then resuspended in lysis buffer (pH 7.0), composed of 20 mM bis–tris (Bio Basic Canada Inc., Canada), 20 mM NaCl (VWR International BVBA, Belgium), and 1 mM phenylmethylsulfonyl fluoride (PMSF) (SERVA Electrophoresis GmbH, Germany). Suspended cells were lysed using an ultrasonicator with the following settings: pulse amplitude = 27–28, pulse energy = 50 000 joules, pulse on/off = 6/12 s. Unwanted cell debris in the cell lysate was pelleted out by centrifugation at 14 500 rcf for 40 min. The supernatant was filtered using 0.45 and 0.22 µm syringe filters, and the expressed enzyme was purified by anion-exchange chromatography on an AKTA start FPLC system (GE Healthcare, Life Sciences, Sweden) using a Hitrap Q HP column (5 mL; GE Healthcare, UK). In a typical purification procedure, the column was first equilibrated with the equilibration buffer (20 mM bis–tris, 20 mM NaCl, pH 7) at a flow rate of 4 mL min⁻¹. The sample was loaded onto the column at a flow rate of 0.5 mL min⁻¹ and eluted with buffer (20 mM bis–tris, 1 M NaCl, pH 7) in gradient mode. The presence and purity of the enzyme were checked using SDS-PAGE analysis, which was run in MES (2-(N-morpholino)ethanesulfonic acid) (SERVA Electrophoresis GmbH, Germany) buffer (pH 7.3). The enzyme was analyzed in comparison to PageRuler™ prestained protein ladder (Thermo Fisher Scientific, USA). The desired protein was successfully purified and eluted with 35–40% elution buffer. The purified GDPD (molar extinction coefficient = 37 930 M⁻¹ cm⁻¹; mol. wt. = 28 kDa) was quantified using NanoDrop (Thermo Fisher Scientific, USA).

In vitro GDPD inhibitory activity assay

The GDPD enzyme inhibitory activity was measured by the colorimetric method as previously reported.^16,39 Briefly, enzyme (3 µM) and MnCl₂ (0.5 mM) in tris-buffer (50 mM, pH 8.5) were pre-incubated with three different concentrations initially (1, 0.5, and 0.25 mM) of aminoquinoline derivative for 30 min at 55–60 °C in a 96-well plate. Then, the substrate (0.5 mM) bis(p-nitrophenyl) phosphate (BpNPP) (Sigma N-3002, USA) was added to each well (final volume: 200 µL), and the mixture was further incubated for an additional hour. The release of p-nitrophenol (a yellow-colored product) was monitored on a spectrophotometer at λ_max = 410 nm. EDTA was used as a standard inhibitor. All experiments were performed in triplicate. The following formula calculated the percent inhibition:

Thermal shift assay/differential scanning fluorimetry

In a typical screening protocol, the GDPD enzyme (20 µM), compounds at different concentrations, and Sypro^®Orange dye (2 µL, 100×, final dilution: 1 : 1000) were added to bis–tris buffer (pH 7.0) in 0.2 cm³ PCR tube strips. Bis–tris buffer alone served as a blank, and a control sample containing only protein and dye in the buffer, to account for intrinsic thermal stability, was also run. All samples were prepared in triplicate to ensure reproducibility. The thermal shift assay was conducted on a CFX real-time PCR machine (Bio-Rad Laboratories, USA). Fluorescence of the Sypro®Orange dye (Invitrogen Life Technologies, USA) was measured as a function of temperature using the HEX channel (λ_ex 538 nm; λ_em 555 nm), enabling real-time detection of protein unfolding. The samples were gradually heated from 20 to 98 °C at a constant rate of increment 0.3 °C min⁻¹, allowing the dye to bind to the exposed hydrophobic regions as the protein unfolded. CFX ManagerTM software (Bio-Rad, USA) was used to analyze the data. The melting temperature (T_m) was determined from the midpoint of the melt curve, with greater accuracy achieved by identifying the most negative peak in the first derivative of the melt curve, corresponding to the temperature at which the rate of protein unfolding is highest.⁵¹

Circular dichroism spectroscopy

Protein solution (0.1 mg mL⁻¹) was prepared in 50 mM tris-buffer (pH 8.75), while the compound's stock solutions in the range of 150–300 µM were dissolved in different percentages of DMSO. Protein and compound mixtures were prepared at the IC₅₀ concentrations of the compounds. The circular dichroism experiment was performed using a spectropolarimeter (Jasco J810, Japan).⁶⁰ Baseline spectra were recorded in tris buffer using a 1 mm path-length cuvette before acquiring CD spectra of 300 µL samples in the far-UV amide range (190–250 nm). Measurements were conducted at a scanning speed of 100 nm min⁻¹ with a data interval of 1 nm. The BestSel online tool was used to estimate the secondary structure composition.⁶¹

Saturation transfer difference (STD-NMR) experiments

All STD-NMR experiments were recorded on a Bruker Avance Neo 600 MHz spectrometer (Fällanden, Switzerland), equipped with a cryogenically cooled probe. Ligand (1 mM) and enzyme (5 µM) in sodium phosphate buffer (500 µL, pH 6.0), which was composed of NaH₂PO₄ (20 mM), Na₂HPO₄ (20 mM), and NaCl (20 mM), prepared in D₂O and taken into a 5 mm NMR tube. Experiments were conducted at 298 K using the stddiffesgp.3 pulse program, applying water suppression by excitation sculpting with gradients.³⁸ On- and off-resonance irradiations were set at −524.466 Hz and −20,000 Hz, respectively. On resonance irradiation at −524.466 Hz, targets methyl resonances of the GDPD. Data was processed and analyzed by Bruker Topspin 4.0.8 software (Bruker, Biospin GmbH, Germany). STD amplification factors for the ligand proton(s) that showed STD were calculated by the following formula:where I_o and I_sat denote the intensity of the signal in off- and on-resonance NMR spectra, respectively, and I_o – I_sat represents the intensity in the STD difference NMR spectrum.

Protein structure modeling

The 3D structure of the GDPD protein was modeled using AlphaFold, as no experimentally determined structure is available yet. The predicted model was evaluated for structural stability and validity via the Ramachandran plot. The potential binding site of the modeled protein structure was predicted using DoGSite Scorer to identify key residues of the binding pocket.

Molecular docking

The predicted 3D model of the GDPD protein was prepared for molecular docking by removing any non-essential molecules, adding polar hydrogen atoms, and assigning Kollman charges using AutoDock Tools. Grid box parameters were defined to encompass the predicted active site residues. A total of 24 selected compounds, along with EDTA as a reference control, were then subjected to molecular docking using AutoDock 4.2. AutoDock tools were used to assign Gasteiger charges and rotatable bonds, and all ligand structures were optimized and energy-minimized before docking.

Docking simulations were performed using the Lamarckian Genetic Algorithm (LGA) with default parameters. Multiple conformations were generated for each ligand, and the best binding pose was selected based on the lowest binding energy and a favorable interaction profile. The docking results were analyzed in terms of binding affinity (kcal mol⁻¹) and hydrogen bonding and hydrophobic interactions between the ligands and the active-site residues of the GDPD protein. Protein–ligand interaction profiles were analyzed using PLIP, and images were generated via Pymol.

Molecular dynamics simulation

All-atom molecular dynamics (MD) simulations were performed to assess the stability and dynamics of the protein–ligand complexes, using GROMACS (Version 2022.3) with the Amber99SB force field and the TIP3P water model. The simulations were carried out on a NVIDIA GTX 1070 GPU, utilizing the CUDA version of GROMACS. To maintain a minimum distance of 2.0 nm between successive images of the complex, a cubic box was created, ensuring the complex was at least 1.0 nm from the box edges. Periodic boundary conditions were employed for this purpose. To eliminate steric clashes and ensure proper geometry, the structure was relaxed via energy minimization. The system was then equilibrated for 100 ps using isochoric–isothermal (NVT) equilibration at 300 K, followed by 100 ps of isothermal–isobaric (NPT) equilibration at 300 K. The equilibrated ensembles were then subjected to MD simulations for 50 ns. The structural trajectories were calculated using the trjconv tool. Trajectory analyses were carried out using Chimera. As standard stability metrics, root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (R_g) plots were created using rmsd, rmsf, and gyrate tools, respectively, and were visualized using the xmgrace plotting tool.

Results and discussion

Expression and purification of GDPD enzyme

Previously, the plasmid construct pET-25b(+), carrying the gene of interest for GDPD from S. aureus, was commercially acquired in lyophilized form. This recombinant plasmid was transformed into competent E. coli BL21(DE3)pLysS expression cells using the CaCl₂ heat-shock method, as recently reported by us.³⁹ The desired GDPD enzyme was expressed in E. coli BL21(DE3)pLysS cells and purified by anion-exchange chromatography. The enzyme was obtained at a concentration of ∼200 µM.

In vitro inhibitory activity on GDPD enzyme

The GDPD enzyme catalyzes the hydrolysis of various glycerophosphodiester substrates in the presence of bivalent cations (Mn⁺² and Mg⁺²). Enzyme inhibitory activity was measured in the presence of bis(p-nitrophenyl) phosphate (BpNPP), a widely used non-physiological substrate for phosphodiesterases. On hydrolytic reaction, this substrate produced a yellow-colored p-nitrophenol, which is easily detected on a spectrophotometer at λ_max = 410 nm. Inhibitory activity was measured at a concentration of 3 µM of enzyme, in the presence of MnCl₂ (0.5 mM), and BpNPP (0.5 mM) as substrate.

A range of diversely functionalized aminoquinoline derivatives (24) was selected for screening against the GDPD enzyme. These aminoquinoline-based molecules were previously synthesized by reacting different positional isomers of aminoquinoline with acyl isothiocyanates. All compounds were structurally well-characterized by different spectroscopic techniques and reported.⁴⁰ Compounds were screened for their GDPD inhibitory activity, and the results were compared with the standard inhibitor EDTA. Out of 24, 15 derivatives showed significant to moderate inhibitory activity with IC₅₀ values in the range of 79.3 ± 4.8 to 943.5 ± 17.3 µM against GDPD, in comparison to EDTA (IC₅₀ = 468.5 ± 1.09 µM). Specifically, compound 6 was identified as the most potent inhibitor (IC₅₀ = 79.3 ± 4.8 µM), outperformed the standard (Table 1).

Table 1. IC₅₀ values of the in vitro GDPD inhibitory activity of compounds 1–24.

Compounds no.	Structures of compounds	IC50 ± SEMa (µM)
1		NAb
2		179.70 ± 1.7
3		144.27 ± 2.1
4		NAb
5		NAb
6		79.30 ± 4.8
7		NAb
8		943.51 ± 17.3
9		241.46 ± 0.4
10		NAb
11		132.03 ± 2.8
12		265.75 ± 1.0
13		NAb
14		486.02 ± 1.9
15		143.24 ± 0.4
16		857.05 ± 2.6
17		230.56 ± 1.1
18		185.19 ± 1.3
19		163.26 ± 1.6
20		NAb
21		NAb
22		NAb
23		606.97 ± 3.4
24		244.02 ± 2.5
—	EDTAc	468.5 ± 1.09

^aSEM (Standard error mean).

^bNA (Not active).

^cEDTA (Standard inhibitor of GDPD).

Structure–activity relationship (SAR)

Structure–activity relationship (SAR) of synthetic aminoquinoline derivatives demonstrated that the inhibitory potency is strongly dependent on the electronic nature of the substituents present at the aryl moiety, as well as the positional isomerism of amino group on the quinoline scaffold. Furthermore, the presence of the substituted carbamothioyl amide pharmacophore is also found to significantly influence the inhibitory activity.

The comprehensive SAR analysis revealed that among compounds 1–3, 3 (IC₅₀ = 144.27 ± 2.1 µM) with carbamothioyl benzamide moiety at the 8-position of the quinoline, exhibited three-fold more potent activity as compared to the standard EDTA (IC₅₀ = 468.5 ± 1.09 µM), which might be due to the favorable π–π stacking interactions with the enzyme's binding pocket. Its positional isomer 2 (IC₅₀ = 179.70 ± 1.7 µM) showed a bit less inhibitory activity than 3, with the same moiety at the 5-position of the quinoline, reflecting a less favorable orientation within the active site. In contrast, another positional isomer, 1, was found to be inactive, suggesting that the compound failed to achieve optimal binding due to a different 3D conformation, as the carbamothioyl benzamide moiety is at position 3 of the quinoline ring. However, among the compounds bearing mesomerically electron-donating methoxy at the phenyl ring, i.e., 4 and 5, analog 4, having carbamothioyl benzamide moiety at position-3 of quinoline, was also found to be inactive, which confirms that 3-substituted positional isomers failed to attain the conformation to fit well into the binding pocket. Another compound 5 having carbamothioyl benzamide moiety at position-5 of quinoline and p-methoxy at phenyl ring was also found to be inactive, which demonstrates that methoxy substitution is also not playing any role in depicting the inhibitory potential. Compound 5 can be compared with structurally similar molecule 7, which has an ethoxy substituent instead of a methoxy and also showed no optimal activity. Luckily, compound 6 (IC₅₀ = 79.30 ± 4.8 µM), which is related to compound 7, but with an ethoxy group at the ortho position of the phenyl ring, was identified as the most potent of the whole series. The activity results for compounds 4–7 show that methoxy and ethoxy substitutions did not affect inhibitory activity. Still, when the substitution, like ethoxy, moved to the ortho position, it contributed significantly to inhibition. The proposed rationale for its significant contribution is that it may form a stable six-membered ring with the carbonyl oxygen via hydrogen bonding to the enzyme's active-site residue. But its active participation can be confirmed by STD-NMR and an in silico study (Fig. 2).

Fig. 2. Structures and GDPD inhibitory activity relationships of compounds 1–7.

Compounds 8–12 contain an electron-donating methyl group on the phenyl ring and showed an interesting trend in inhibitory activity. Pertinent to mention that compound 11 (IC₅₀ = 132.03 ± 2.8 µM) with carbamothioyl benzamide moiety at position-3 of quinoline and additional m-methyl substitution at phenyl ring was identified as the second most active inhibitor of this series, compared to EDTA. When the whole m-methyl possessing carbamothioyl benzamide moiety shifted to position-8 of the quinoline ring in compound 12, a two-fold decreased inhibition (IC₅₀ = 265.75 ± 1.0 µM) was observed. Similarly, when the m-methyl possessing carbamothioyl benzamide moiety shifted to position-5 in the case of derivative 8 (IC₅₀ = 943.51 ± 17.3 µM), about a sevenfold decline in the inhibitory potential was observed. It suggests that in the case of m-methyl substituted compounds, the favorable position for the carbamothioyl benzamide moiety is position-3 as compared to −8 and −5. The activity of compound 12 can be compared with that of its structurally similar molecule 10, where moving the methyl from meta to para position results in complete inactivity. Compound 10 might not meet the conformational requirement for optimal binding to the enzyme's active site, whereas comparing the activities of compounds 8 and 9 (IC₅₀ = 241.46 ± 0.4 µM) showed that moving the methyl group from meta to para increases the inhibitory potential up to fourfold. Compound 24 (IC₅₀ = 244.02 ± 2.5 µM) is an exceptional derivative, structurally related to compound 8, but possessing a bromo group (halogen class) instead of methyl, and showed fourfold enhanced inhibitory potential (Fig. 3). The greater activity of compound 24 prompted us to investigate halogen-bearing analogs in future studies.

Fig. 3. Structures and GDPD inhibitory activity relationships of compounds 8–12 and 24.

A group of compounds 13–19 possesses a carbamothioyl alkanamide moiety of varying alkane chain lengths and revealed varying degrees of inhibitory potential. Compound 13 with carbamothioyl pentanamide substitution at the 5-position of quinoline was identified as the inactive analog. Just increasing the chain length from pentanamide to octanamide in the case of compound 14 (IC₅₀ = 486.02 ± 1.9 µM) revealed reasonable inhibitory potential. Extending the octanamide chain to the nonanamide chain in compound 16 (IC₅₀ = 857.05 ± 2.6 µM) leads to decreased inhibitory potential. But further extension to decanamide in compound 17 (IC₅₀ = 230.56 ± 1.1 µM) showed much enhanced inhibitory potential than octanamide and nonanamide bearing compounds 14 and 16, respectively. Activity of carbamothioyl octanamide containing compound 14 can be compared with its positional isomer 15 (IC₅₀ = 143.24 ± 0.4 µM), which has the carbamothioyl octanamide moiety at position 8 instead of 5, and showed remarkable activity. Pertinent to mention that compound 15 is the third most active analog of this series, showing much better inhibitory potential than standard EDTA. Comparing the activity of compound 15 with 18 (IC₅₀ = 185.19 ± 1.3 µM), where the chain length extends to decanamide, showed potent inhibitory activity, slightly less than that of the octanamide derivative 15. The positional isomer 19 (IC₅₀ = 163.26 ± 1.6 µM) of 18, having carbamothioyl decanamide moiety at position 3 of quinoline ring, revealed even better activity (Fig. 4). The above activity trend suggests that amongst all alkanamide derivatives, the different positional isomers of octanamide performed well. In addition, the sole derivative bearing carbamothioyl octanamide at position 8 has outperformed all others.

Fig. 4. Structures and GDPD inhibitory activity relationships of compounds 13–19.

Compounds 21 and 22 are the positional isomers containing carbamothioyl pivalamide moiety and found to be completely inactive, which might be due to steric crowding by the pivalyl group, which reduced binding interactions with the enzyme. However, an exceptional derivative 23 (IC₅₀ = 606.97 ± 3.4 µM), which contains carbamothioyl pivalamide moiety at position 4 and a chloro substitution at the position 7 of quinoline ring, showed significant inhibitory potential, which suggests the active participation of chloro substitution in the inhibitory activity (Fig. 4).

Collectively, the limited SAR suggested that the position of carbamothioyl benzamide moiety at quinoline ring exerts a significant impact on the inhibitory potential of the compounds. In case of varying substitutions, –OC₂H₅, Me, and extended alkyl chains have played a crucial role in delivering good inhibition against GDPD. These findings provide valuable insights for rational optimization of aminoquinoline-based GDPD inhibitors.

Circular dichroism (CD) analysis of GDPD enzyme in the presence of compounds 1–24

Circular dichroism (CD) analysis is a powerful analytical technique that reveals key features of protein secondary structure, including α-helices and β-sheets, and detects folding/unfolding dynamics and binding interactions and stable conformation under physiological conditions, consistent with the structural integrity required for enzymatic activity. In the current study, the secondary structure composition of GDPD from S. aureus is determined for the first time by the CD analysis. The CD spectra (Fig. 5A) of the GDPD in the far-UV amide region (190–250 nm) showed two broad, overlapped, high-intensity negative bands at 211 and 222 nm, respectively, indicative of a mixed α/β secondary structure of the native enzyme. The far-UV CD spectrum arises from exciton-coupled π → π* and n → π* transitions of the peptide backbone. Deconvolution analysis revealing 32.2% α-helix and 25.2% β-sheet quantification. This analysis confirms that GDPD is well-folded, has a properly organized secondary structure essential for its catalytic activity, and is conformationally stable in tris buffer.

Fig. 5. CD spectrum of GDPD enzyme, presenting well-defined negative bands at 211 nm and 222 nm, and in the presence of inhibitors, presenting significant alteration in the secondary structure of the native enzyme.

CD spectroscopy was performed on all fifteen inhibitors identified by biochemical inhibitory assay. IC₅₀ concentrations were used to evaluate the impact of aminoquinoline derivatives. The calculated secondary structure composition of GDPD is presented in Table 2. In the presence of inhibitors, the primary negative peak shifted from 211 nm to 223–237 nm. This red shift indicates significant alterations in the enzyme's secondary structure. Structural analysis using BestSel software further confirmed these changes, highlighting the impact of these compounds on the protein conformation in terms of percentage change, as represented in Table 2. The far-UV CD spectrum of GDPD, plotted as mean residue ellipticity [θ] (deg cm⁻² dmol⁻¹), in the presence and absence of inhibitors, is shown in Fig. 5.

Table 2. Assessment of percentages of secondary structural elements of GDPD with the addition of inhibitors.

Sample	Helix (%)	β Sheet		Turn (%)	Others (%)	Wavelength (nm)
		Antiparallel (%)	Parallel (%)
Protein (alone)	32.2	25.2	10.7	9.5	0.0	211 and 222
Protein + C2	1.7	8.5	6.3	34.8	48.7	227
Protein + C3	0.0	25.5	19.0	39.6	—	16.0
Protein + C6	5.7	17.8	18.8	28.8	28.9	226
Protein + C8	15.2	30.7	22.3	31.8	—	231
Protein + C9	0.0	19.9	15.4	30.5	34.1	231
Protein + C11	4.5	19.1	16.6	30.9	28.9	227
Protein + C12	19.2	30.7	21.6	28.6	—	230
Protein + C14	0.0	25.3	19.0	39.5	—	232
Protein + C15	22.1	30.5	20.4	27.0	—	227
Protein + C16	0.0	26.5	19.8	40.0	—	237
Protein + C17	0.1	19.5	7.2	22.9	50.3	229
Protein + C18	0.0	26.0	19.5	39.8	—	229
Protein + C19	22.0	31.0	21.6	25.4	—	228
Protein + C23	20.9	31.0	21.7	26.4	—	237
Protein + C24	0.0	12.0	22.7	29.0	36.3	229

Effect of quinoline-based inhibitors on thermal stability of GDPD

The thermal shift assay (TSA) or differential scanning fluorimetry (DSF) is a fluorescence-based analysis used to investigate protein–ligand interactions and protein conformational stability in the presence of inhibitors or ligands at different temperatures. In the current study, TSA/DSF was performed to study the thermal stability of the GDPD enzyme, evaluating its structural integrity and functionality in the presence of aminoquinoline-based inhibitors. Protein thermal unfolding was monitored by increasing the temperature from 20 to 98 °C in a real-time PCR system, resulting in a fluorescence shift that yielded a sigmoidal unfolding profile. The melting temperature of GDPD in the absence of inhibitors in tris buffer was found to be 56.6 °C, while their melting temperatures (T_m) in the presence of inhibitors are shown in Fig. 6. In general, the T_m changes of the protein in the presence of compounds indicated that they exerted neutral, stabilizing, destabilizing, or degradative effects on the enzyme's thermal stability. Neutral compounds typically cause negligible T_m shifts, indicating no interaction with the enzyme.

Fig. 6. Comparison of the change in T_m values of GDPD on the addition of compounds 2, 3, 5, 6, 8, 11, 12, 14–19, 21, 24 determined by thermal shift assay. The T_m value for GDPD alone is presented by the red bar, while blue bars represent the change in T_m value of the enzyme on the addition of compounds.

In contrast, compounds with a stabilizing influence increase T_m, indicating ligand binding that increases conformational rigidity and stabilizes the protein against thermal denaturation. Conversely, compounds with a destabilizing effect decrease conformational perturbations or may promote partial enzyme unfolding. In addition, some compounds showed irregular or absent melting curves, which is due to degradation; therefore, they lost or reduced their thermal transitions. Fig. 7 showed the variation in T_m in the absence and presence of inhibitors. Of the 15 tested compounds, 9, including 6, 8, 9, 14–18, and 24, showed a decrease in T_m of 0.3–2.4 °C, confirming their negative modulatory effect on the enzyme. Conversely, compounds 2, 3, 11, 12, and 19 showed a stabilizing effect on the enzyme, indicative of ligand binding. However, only compound 23, a moderately active inhibitor, showed no change in T_m.

Fig. 7. Graphical representation of T_m variations (ΔT_m) in GDPD on the addition of aminoquinoline compounds.

Ligand binding analysis using saturation transfer difference (STD) NMR spectroscopy

STD-NMR assessed the binding potential of the inhibitors or ligands with the target. To avoid unwanted signals from the buffer, experiments were performed in sodium phosphate buffer (pH 6.0) prepared in D₂O. Unfortunately, of the 15 inhibitors, 2, 3, 6, 12, 14, 15, 19, and 24 showed poor solubility in the desired buffer; thus, STD interactions were not observed, and the ligand molecules were always required in excess. However, inhibitors 8, 11, 16–18, and 23 are soluble enough to give STD interactions.

Compound 11 was the second most potent inhibitor of the whole series and showed significant interactions in the STD spectrum. Analysis of the STD NMR spectrum of inhibitor 11 (Fig. 8) revealed that the whole aryl ring is interacting with the binding site of the enzyme. Since multiple protons showed interactions with the enzyme, they were subjected to group epitope mapping (GEM) analysis. GEM maps the proximity of ligand protons on the protein surface. The ligand protons that are in the close vicinity of the protein are saturated to the maximum extent possible, resulting in strong STD intensities, and its STD integral value is considered to be 100%. Whereas ligand protons farther from the protein show lower STD intensities. Thus, their STD integrals are normalized against the highest STD integral value; as a result, each proton received relative degrees of saturation. According to the GEM analysis, H-2′ of the 3′-methyl substituted aryl ring showed the highest STD integral value, and was considered to have 100% saturation. It indicates that H-2 strongly interacted with the target enzyme. Whereas, H-6′ got 83% relative degree of saturation, followed by H-4′ and H-5′, which received 73 and 69% saturation, respectively. CH₃-3′ got the least saturation 48% that indicates CH₃-3′ has comparatively less interaction with the enzyme. In addition, labile protons on the urea moiety may receive saturation but cannot be detected. It is well known that labile protons attached to heteroatoms immediately exchange with deuterium in the NMR buffer system and cannot be analyzed by STD, even when they are saturated.

Fig. 8. Analysis of STD-NMR of inhibitor 11: (A) ¹H-NMR spectrum of inhibitor 11 (1 mM), recorded on AV NEO 600 MHz instrument equipped with a cryogenically cooled probe, with water suppression; acquisition parameters: Pulsprog = zgesgp; DS = 4; NS = 128; P1 = 10.0 µs. (B) STD difference spectrum recorded in the presence of GDPD (5 µM); acquisition parameters: Pulsprog = stddiffesgp.3; DS = 8; D1 = 10; D20 = 3 s; NS = 1024; P1 = 10.0 µs.

Compound 23 was a moderately active inhibitor but showed important interactions with the target (Fig. 9). GEM analysis showed that the whole 7-chloroquinoline ring interacted with the binding site. Among all interacting protons, H-5 showed 100% saturation and is very close to the hydrazine-1-carbonothioyl moiety. It may be that the hydrazine-1-carbonothioyl moiety also interacted with the target but could not be detected because it possesses labile protons. However, H-2, H-3, H-6, and H-8 showed relative saturations of 63–46%, thus interacting comparatively weaker than H-5.

Fig. 9. Analysis of STD-NMR of inhibitor 23: (A) ¹H-NMR spectrum of inhibitor 23 (1 mM), recorded on AV NEO 600 MHz instrument equipped with a cryogenically cooled probe, with water suppression; acquisition parameters: Pulsprog = zgesgp; DS = 4; NS = 128; P1 = 10.0 µs. (B) STD difference spectrum recorded in the presence of GDPD (5 µM); acquisition parameters: Pulsprog = stddiffesgp.3; DS = 8; D1 = 10; D20 = 3 s; NS = 1024; P1 = 10.0 µs.

Compounds 8, 16–18 also showed different extents of STD interactions. Compound 8 was identified as a moderately active inhibitor, and its 3′-CH₃ aryl ring showed STD interactions with the target. Just like the case of inhibitor 11, H-2′ received maximum saturation (100%) and interacted strongly, while other protons, H-4′, 5′, and -6′ received relatively lower saturation. In addition, in cases 16–18, the entire alkyl chain received saturation and showed STD interactions with the target (Fig. S1–S4).

There are several limitations associated with STD-NMR, including limited solubility of compounds, exchange of labile protons, and the inability to detect interacting heteroatoms. Looking at the structures of all 24 compounds, several heteroatoms may interact strongly with the target but could not be detected. Thus, we enriched our study by performing an in silico study to predict potential and key interactions between inhibitors and the target enzyme.

Molecular docking studies

Structural modelling

The 3D structure of the GDPD protein was predicted using AlphaFold, as shown in Fig. 10A. The modelled structure demonstrated overall good stereochemical quality. Structural validation of the modelled protein was performed using a Ramachandran plot (Fig. 10C), which revealed that 93% of residues were located in the most favored regions, indicating a high level of structural reliability and a stable backbone conformation. A minimal number of residues were also observed in the additionally allowed region, suggesting that the predicted model possesses acceptable geometric parameters for further analyses. The overall folding pattern of the protein was also consistent with expected structural features, further supporting its suitability for molecular docking studies. The predicted binding site of the GDPD protein, via DoGSiteScorer, was well-defined and accessible, with a surface area of 407.55 Ų and a drug score of 0.84, providing a reliable site for analyzing protein–ligand interaction profiles (Fig. 10B).

Fig. 10. (A) The 3D structure of the GDPD protein as modelled by AlphaFold. (B) The predicted binding site of the GDPD protein is shown as a mesh as predicted by DogSite Scorer. (C) Ramachandran plot of the modelled GDPD protein.

Protein–ligand interaction profiles

The docking analysis of 24 compounds against the GDPD protein revealed varying binding affinities, interaction profiles, and inhibitory potentials when compared with the control (EDTA) (Table 3). Among all the compounds, compound 6 exhibited the most potent inhibitory activity, with an IC₅₀ value of 79.30 ± 4.8 µM and a predicted binding affinity of −5.71 kcal mol⁻¹. This compound formed stable hydrogen bonds with Thr3 and Asp232, along with hydrophobic interactions involving Trp33 and Asp232, indicating strong stabilization within the binding pocket (Fig. 11A).

Table 3. Docking profiles of all compounds against GDPD.

Comp. No.	Docking scores	Hydrogen bonds	Distance (Å)	IC50 ± SEMa (µM)	Hydrophobic bonds
1	−5.3911562	Val143	2.15	NA	Trp33, Glu142, Tyr163
2	−5.753932	Tyr5, Trp33	3.16, 2.26	179.70 ± 1.7	Ile189
3	−5.41707468	Trp33, Glu142	2.64, 1.91	144.27 ± 2.1	Trp33, Leu144, Asn164, Ile189
4	−5.63058615	Tyr5, Glu142, Val143, Asn164	2.94, 2.08, 3.02, 2.72	NA	Thr3, Trp33, Glu142, Tyr163
5	−5.6217227	Tyr5, Asn32, Trp33	3.16, 3.34, 2.19	NA	Ile189
6	−5.71194267	Thr3, Asp232	2.63, 3.42	79.30 ± 4.8	Trp33, Asp232
7	−5.47959661	Thr3, Trp33, Asn213	2.16, 2.02, 3.01	NA	Asp232
8	−5.69944096	Val143, Tyr163	2.23, 2.62	943.51 ± 17.3	Trp33, Glu142, Gln162
9	−5.73586321	Tyr5, Trp33	3.21, 2.29	241.46 ± 0.4	Ile289
10	−5.90252304	Val143, Asn164	3.38, 2.39	NA	Trp33, Val143, Tyr163
11	−5.64184523	Glu211	2.53, 2.09	132.03 ± 2.8	Asn164, Lys188, Ile189, Asp232
12	−5.19408131	Asn164	2.27	265.75 ± 1.0	Trp33, Leu144, Lys188
13	−5.6749711	Trp33, Asn213	2.26, 2.59	NA	Trp33, Ile189, Asp232
14	−6.37473249	Val143, Tyr163	2.09, 2.98	486.02 ± 1.9	Trp33, Glu142, Gln162, Tyr163, Asn164
15	−5.74364042	Val143, Asn164	2.37, 2.64	143.24 ± 0.4	Trp33, Ile189
16	−5.95529413	Gln162, Asn164, Arg165, Asn186	2.11, 2.44, 2.71, 3.20	857.05 ± 2.6	Trp33, Glu142, Asn164, Ile189
17	−6.18617439	Val143, Asn164	2.40, 3.07	230.56 ± 1.1	Trp33, Glu142, Tyr163, Ile189
18	−6.06843281	Asn164, Glu211	2.22, 1.92	185.19 ± 1.3	Lys188
19	−6.54248667	Tyr5, Val143	3.33, 2.00	163.26 ± 1.6	Ile189
20	−5.63380337	Tyr5, Trp33, Asn164	3.32, 2.28, 3.15	NA	Ile189
21	−5.58746338	Asn32, Trp33, Glu211	2.13, 2.27, 2.56	NA	Trp33, Asn164, Ile189
22	−4.87721348	Asn164	2.35	NA	Thr3, Asp232
23	−5.40042639	Val143	2.60	606.97 ± 3.4	Trp33, Asn164
24	−5.70683908	Tyr5	3.20	244.02 ± 2.5	Ile189
Ctrl	−5.13186264	Thr3, Tyr5, Asn32, Trp33, Asn106, Asn164, Asn213	2.53, 2.95, 2.62, 2.12, 3.08, 2.08, 2.06	468.5 ± 1.09	—

^aSEM is reffered as standard error mean.

Fig. 11. 3D representation of ligand–protein interactions of compounds 6 (A), 11 (B), 3 (C), 15 (D), 19 (E), and control EDTA (F).

Compound 11, with an IC₅₀ value of 132.03 ± 2.8 µM, and compound 3, with an IC₅₀ value of 144.27 ± 2.1 µM, also demonstrated notable inhibitory activity. Compound 11 interacted via hydrogen bonding with Glu211 and exhibited multiple hydrophobic contacts with Asn164, Lys188, Ile189, and Asp232 (Fig. 11B). Similarly, compound 3 formed hydrogen bonds with Trp33 and Glu142, supported by hydrophobic interactions involving key residues such as Leu144, Asn164, and Ile189 (Fig. 11C). Compound 15, with an IC₅₀ of 143.24 ± 0.4 µM, showed comparable activity, forming hydrogen bonds with Val143 and Asn164 and hydrophobic interactions with Trp33 and Ile189 (Fig. 11D). Compound 19 with an IC₅₀ of 163.26 ± 1.6 µM also displayed favorable binding, with hydrogen bonds involving Tyr5 and Val143 and hydrophobic interaction with Ile189 (Fig. 11E).

In comparison, the control EDTA showed a binding affinity of −5.13 kcal mol⁻¹ and an IC₅₀ value of 468.5 ± 1.09 µM, forming multiple hydrogen bonds with residues including Thr3, Tyr5, Trp33, and Asn164 (Fig. 11F). Residues such as Trp33, Val143, Asn164, Glu142, and Ile189 were consistently involved in ligand binding, highlighting their importance in stabilizing ligand–protein interactions within the active site.

Molecular dynamics simulation

Root mean square deviation (RMSD)

RMSD plots represent the overall structural deviation of the protein from its initial conformation during the simulation, indicating system stability. The RMSD profiles showed an initial equilibration phase during the first 5–10 ns, after which all systems reached a relative stability. The control complex exhibited the lowest RMSD values, ranging from 0.17 to 0.20 nm, indicating high structural stability. Among the ligand-bound systems, compound 3 displayed the highest RMSD values (0.20–0.25 nm), suggesting slightly greater conformational flexibility upon ligand binding. Compounds 11 and 15 showed intermediate RMSD values that remained close to the control throughout the simulation. None of the complexes showed significantly large structural deviations, indicating that ligand binding did not significantly destabilize the protein structure, as shown in Fig. 12A.

Fig. 12. (A) RMSD, (B) RSMF, and (C) R_g plots of protein–ligand complexes, including control (black), compound 3 (violet), compound 11 (turquoise), and compound 15 (blue) throughout the 50 ns molecular dynamics simulations.

Root mean square fluctuation (RMSF)

The RMSF is a measure of the average fluctuation of individual residues around their mean positions. RMSF analysis (Fig. 12B) revealed similar fluctuation patterns across all systems, suggesting that ligand binding did not significantly alter the overall dynamic behavior of the protein. Most residues exhibited fluctuations below 0.10 nm, reflecting a relatively rigid protein structure. Peaks were observed around residues 110–120 and at the C-terminal region (residues 245–250), indicating intrinsically flexible loop and terminal regions. Compounds 3, 11, and 15 showed slightly elevated fluctuations at these regions compared to the control, with compound 3 producing the largest fluctuations at the C-terminus. However, the similarity of the overall RMSF profiles suggests that the ligands did not induce major local structural perturbations.

Radius of gyration (R_g)

The radius of gyration measures the compactness of the protein structure and remained relatively constant across all systems throughout the simulation (Fig. 12C), ranging approximately between 1.80 and 1.85 nm. The control complex exhibited the lowest fluctuations, while the ligand-bound complexes showed slightly higher but comparable variations. Compound 3 displayed a marginally higher average R_g during portions of the simulation, whereas compounds 11 and 15 remained very close to the control. The absence of significant changes in R_g indicates that the overall compactness of the protein was preserved in all systems and that ligand binding did not promote unfolding or major conformational expansion.

These RMSD, RMSF, and R_g analyses demonstrate that the protein maintained structural stability and compactness in the presence of compounds 3, 11, and 15 throughout the 50 ns simulation. Although compound 3 induced slightly higher global and local fluctuations compared to the control and the other ligands, these variations remained within an acceptable range and did not compromise protein stability. Compound 15 exhibited dynamics most similar to the control, while compound 11 showed intermediate behavior. These findings suggest that all three compounds form stable protein–ligand complexes and are well accommodated within the protein structure during the simulation period.

Conclusion

A diverse library of aminoquinoline-based compounds was evaluated for the first time against the heterologously expressed and purified GDPD enzyme, an emerging drug target from S. aureus. Of 24 analogs, 15 were active, with IC₅₀ values ranging from 79.3 to 943.5 µM, compared with the standard EDTA (IC₅₀ = 468.5 µM). Compound 6 outperformed all others and was identified as the most potent analog, with an IC₅₀ of 79.3 µM. Several biophysical techniques, including CD spectroscopy, differential scanning fluorimetry (DSF), and saturation transfer difference (STD) NMR spectroscopy, were used to obtain information on the different physical and chemical behaviors of compounds towards the target enzyme. In addition, molecular docking was performed to predict the key binding interactions between inhibitors (ligands) and the binding pocket of the GDPD enzyme. In conclusion, this study has identified several new inhibitors that can serve as lead molecules for further study of this enzyme.

Consent for publication

All authors have read and approved the final version of the manuscript.

Conflicts of interest

The authors declare no conflict of interest, financial or otherwise. No writing assistance was utilized in the production of this manuscript.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Supplementary information (SI): amino acid sequence of GDPD enzyme from S. aureus and STD-NMR spectra. See DOI: https://doi.org/10.1039/d6ra03432b.