Closing the Gap: An Atomistic Structural and Functional Perspective of S. mansoni Universal Stress G4LZI3 Protein in Complex with Phenolic Compounds

Abstract

Background

For decades, Praziquantel has been the undisputed drug of choice for all schistosome infections, but rising concerns due to the unelucidated mechanism of action of the drug and unavoidable reports of emerging drug resistant strains has necessitated the need for alternative treatment drug. Moreover, current apprehension has been reinforced by total dependence on the drug for treatment hence, the search for novel and effective anti-schistosomal drugs.

Methods

This study made use of bioinformatic tools to determine the structural binding of the Universal G4LZI3 Stress Protein (USP) in complex with ten polyphenol compounds, thereby highlighting the effectiveness of these recently identified ‘lead’ molecules in the design of novel therapeutics targeted against schistosomiasis. Upregulation of the G4LZI3 USP throughout the schistosome multifaceted developmental cycle sparks interest in its potential role as a druggable target. The integration of in silico tools provides an atomistic perspective into the binding of potential inhibitors to target proteins. This study therefore, implemented the use of Molecular Dynamic (MD) simulations to provide functional and structural insight into key conformational changes upon binding of G4ZLI3 to these key phenolic compounds.

Results

Post-MD analyses revealed unique structural and conformational changes in the G4LZI3 protein in complex with curcumin and catechin respectively. These systems exhibited the highest binding energies, while the major interacting residues conserved in all the complexes provides a route map for structure-based drug design of novel compounds with enhanced inhibitory potency against the G4LZI3 protein.

Conclusion

This study suggests an alternative approach for the development of anti-schistosomal drugs using natural compounds.

1. INTRODUCTION

A significant number of the world’s most impoverished, prejudiced, marginalized and vulnerable populations, especially among the African, Asian and Latin American tropical and subtropical regions of the world, still experience the devastating effects of infectious neglected tropical diseases [1, 2]. Schistosomiasis is no stranger to this group of illnesses and stands as one of the most chronic and debilitating parasitic infections that is commonly associated with limited access to health care services and clean drinking water, inadequate nutrition, poor sanitation and sub-standard social behaviours, which altogether bear negative effects on child development, pregnancy outcomes and agricultural productivity, thereby posing major hindrances to socio-economic growth [3-5]. Over 207 million people in more than 76 countries of the world carry the brunt of infection, while a staggering 779 million are at possible risk of infection with over 90% of the cases emanating from sub-Saharan Africa [6, 7]. Typically associated with schistosome infections is granulomatous inflammation that results in scarring and fibrosis triggered by immune responses to the parasite eggs that unintentionally become wedged in host tissues such as the liver intestine and genital areas [8, 9]. As a result, a myriad of momentous and life-threatening chronic complications that include liver fibrosis, cirrhosis and bladder calcification, muscle weakness, visual impairment, severe kidney damage, microscopic bleeding, portal and pulmonary hypertension, cognitive and physical under-development in children, as well as closely associated ties to bladder cancer and HIV/AIDS, are then obvious [10-12].

Over the years, various drugs such as oxamniquine, hycanthone and metrifonate were used for the treatment of schistosomiasis but their use has, however, been discontinued due to species selectivity, severe side effects and drug resistance [13]. Instead, the pyrazino-isoquinoline derivative, Praziquantel (PZQ), has, for many decades, still remained the sole line of chemotherapeutic treatment against schistosomiasis and has taken precedence in countless disease-control programs, particularly in developing countries as it is readily available, relatively affordable and possesses high levels of tolerability and efficacy. Minimal and short-term side effects are displayed by the drug and high egg reduction and cure rates of up to 95% are expected after treatment [7, 14, 15]. In addition to these minor setbacks are more serious concerns about PZQ; the most alarming being reports on the reduction of efficacy of the drug and schistosome resistance due to drug-selective pressure [15]. Amidst this are other disadvantages such as the drug inactiveness against early schistosome stages, failure of the drug to reverse the chronic complications that accompany the disease, and the fact that little is known about the disease even after decades of research exact mechanism of action of the drug [16]. Hence, an urgent need for the discovery of alternative treatment is required and keen interest has been sparked in studying the effects of natural compounds for schistosome treatment.

Secondary metabolites from plants are gradually gathering attention as possible drugs used for schistosomiasis treatment and control [17]. A class of such compounds is the polyphenols, which are ubiquitous plant organic compounds that exhibit beneficial effects in the treatment, prevention and control of cardiovascular and neurodegenerative disorders, obesity and cancer [7, 14, 15, 18, 19]. Additionally, polyphenols also possess the ability to scavenge free radicals and reduce inflammation [20]. Schistosome infections, and as seen in all other chronic infections caused by helminth parasites, trigger the activation of host immune cells (immunomodulation) that in turn generate reactive oxygen species and oxidative damage via the production of pro-inflammatory secretions such as leukocytes and eosinophils [21-23]. This in turn may lead to protein, nucleic acid, lipid and membrane damage once an imbalance between the normal production of ROS and the antioxidant system has been reached, causing harm to the host. In most diseased states, the overproduction of ROS not only results in oxidative stress but also the release of inflammatory signals, which in turn play a pathogenic role in the manifestation of inflammatory diseases such as schistosomiasis [24].

A study by Aanandhi and co-workers demonstrated molecular simulations of the hypothetical in-silico inhibition of the Universal Stress Protein (USP) Rv1636 from Mycobacterium tuberculosis by a select group of natural polyphenols [25]. This current study adopted a similar approach in determining possible inhibitors of the G4LZI3 USP from Schistosoma mansoni using a range of polyphenols. In several organisms, including schistosomes, USPs are induced in response to a myriad of environmental insults such as drought, high temperatures, salinity, toxic chemicals and antibiotics, heavy metals, un-couplers of the electron transport chain and DNA damaging agents such as oxidants to provide protective and regulatory effects that aid their survival [3, 26-28].

A study by Mbah and colleagues highlighted and prioritized the G4LZI3 and Q86DW2 USPs from S. mansoni and S. japonicum respectively as attractive ‘lead’ compounds for future schistosomiasis interventions [29]. However, the exact molecular and biochemical mechanism of this class of proteins is yet to be elucidated despite widespread insight into the regulation and physiology of USPs through various studies. Therefore, this current study integrated the use of in silico tools to gain an atomistic perspective into the binding of a selective range of polyphenols to the G4LZI3 USP based on their potential inhibitory activity against universal stress proteins. Traditionally, High-Throughput Screening (HTS) has been the norm for the identification of biologically active compounds. Still, the introduction of in silico techniques has steadily increased efficacy, cost efficiency and speed in the drug discovery process. Identification of disease proteins remains essential in the drug discovery process as it provides vital information regarding the mechanism of action and effectiveness of a drug molecule [30]. In the absence of any USP experimental structures derived from schistosomes, the structure of the G4LZI3 protein was computationally predicted and used for molecular docking. The best five complexes were selected for post-MD analyses and subsequent binding free energy calculations. This study suggests an alternative approach for the development of anti-schistosomal drugs that makes use of natural compounds with high efficacy.

2. MATERIALS AND METHODS

2.1. Homology Modeling of G4LZI3

The amino acid sequence for the Universal Stress G4LZI3 protein from Schistosoma mansoni was accessed from NCBI (Accession number AB023721.2) [31]. Numerous in silico studies have highlighted the minimum sequence similarity of 25% that is required to model a protein structure whose structure is unknown. Therefore, the unknown 3D structure of G4LZI3 was modeled against PDB structures 2DUM and 3HJA with a sequence similarity of 25% and 37.5% respectively. These PDB structures exhibited the highest sequence similarity in relation to G4LZI3. Alignment of the G4LZI3 target sequence to the template sequences was performed using ClustalW. The predictive 3D structure of G4LZI3 was generated using the built-in version of MODELLER in the Chimera software [32]. A Ramachandran plot for the analyses of bond angles and torsional strain was generated using RAMPAGE [33]. Results showed that 98% of all residues were in the favoured regions, while 2% of all residues were in the allowed regions. This left two outliers, none of which formed part of the active site of the protein. The fold reliability was assessed using the ProSA-web server, which predicts the energy profile of a structure based on its Z-score. The Z-score of the G4LZI3 predicted model was -5.4 [34].

2.2. Ligand Acquisition and Preparation

The 2D structures of the polyphenol compounds listed in Table 1 were obtained from PubChem [35]. These compounds were optimized using Avogadro molecular editor [36]. The G4LZI3 model was prepared for docking using Chimera software [37].

Table 1.

3D structures and molecular docking scores of G4LZI3 protein in complex with polyphenol compounds.

2.3. Molecular Docking

The molecular docking software utilized in this study included Raccoon [38], Autodock Graphical user interface supplied by MGL tools [39] and AutoDockVina [40] with default docking parameters. Prior to docking, Gasteiger charges were added to the compounds and the non-polar hydrogen atoms were merged to carbon atoms.

2.4. Molecular Dynamic (MD) Simulations

Molecular Dynamic (MD) simulations provide a robust tool to explore the physical movements of atoms and molecules, thus providing insight into the dynamic evolution of biological systems. The MD simulations were performed using the GPU version of the PMEMD engine provided within the AMBER 18 package [41] ANTECHAMBER was used to generate atomic partial charges for the compounds by utilizing General Amber Force Field (GAFF) procedures. The Leap module of AMBER 18 suspended each system implicitly within an orthorhombic box of TIP3P water molecules ensuring all atoms were within 10Å of any box edge. Leap also allowed for the addition of Na⁺ or Cl^- counter ions for the neutralization of all six systems. An initial minimization of 20000 steps was carried out with an applied restraint potential of 500kcal/mol. This was followed by a full minimization of 10000 steps carried out using the conjugate gradient algorithm in the absence of restraints. A gradual heating MD simulation from 0K to 300K was run for 50ps, to ensure that all systems maintained a fixed number of atoms and volume. The solutes within the system were exposed to a potential harmonic restraint of 10kcal/mol and a collision frequency of 1ps. Following heating, an equilibration step estimating 500ps for each of the systems was conducted with the operating temperature being kept constant at 300K. Additional features such as the number of atoms and pressure were also kept constant, mimicking an isobaric-isothermal ensemble. The systems pressure was maintained at 1 bar using the Berendsen barostat. The total time for the MD simulation conducted was 100ns. In each simulation, the SHAKE algorithm was employed to constrict the bonds of hydrogen atoms. The step size of each simulation was 2fs and an SPFP precision model was used. The simulations coincided with isobaric-isothermal ensemble, with randomized seeding, a constant pressure of 1bar, a pressure-coupling constant of 2ps using the Monte Carlo barostat, a temperature of 300K and Langevin thermostat with collision frequency of 1ps.

2.5. Post-Dynamic Analysis

The coordinates of the free enzyme and bound complexes were then saved after every 1ps and the trajectories were analyzed using the CPPTRAJ module employed in the AMBER 18 suit. The Root Mean Square Deviation (RMSD) and thermodynamic energies of each system were thereafter investigated.

2.6. Binding Free Energy Calculations

The Molecular Mechanics/Generalized Born Surface Area method (MM/GBSA) [42] was employed to estimate the binding free energy of each system. This was averaged over 20000 snapshots extracted from the 20ns trajectory. The free binding energy (ΔG) computed by this method for each molecular species (complex, ligand and receptor) can be represented as:

ΔG_bind = G_complex – G_receptor – G_ligand (1)

ΔG_bind = E_gas + G_sol – TS (2)

E_gas – E_inl + E_vdw + E_ele (3)

G_sol = G_GB + G_SA (4)

G_SA = γSASA (5)

The term E_gas denotes the gas-phase energy, which consists of the internal energy (E_int), Coulomb energy (E_ele) and the van der Waals energies (E_vdw). The E_gas was directly estimated from the FF14SB force field terms. Solvation free energy (G_sol) was estimated from the energy contribution from the polar states (G_GB) and non-polar states (G). The non-polar solvation energy (G_SA) was determined from the solvent accessible surface area (SASA) using a water probe radius of 1.4 Å, whereas the polar solvation (G_GB) contribution was estimated by solving the GB equation. The terms S and T denote the total entropy of the solute and temperature respectively.

3. RESULTS AND DISCUSSION

3.1. Homology Modeling and Molecular Docking

In the absence of experimental structures, homology modeling presents itself as an innovative and alternative tool that is not only trustworthy and reliable to a considerable extent but is also time efficient and cost-effective for the generation of three dimensional protein models [30]. Homology modeling forms the basis of optimal and effective drug discovery and design process by providing insight into the structural mechanisms of a protein, which involves the interaction between the potential drug and protein, as well as its associated molecular functions. A systematic search of the PDB database was made with the amino acid sequence of the G4LZI3 protein obtained from NCBI. Templates were selected based on those with high similarities, taking into account that the accuracy of predicted models is directly proportional to the increase in similarity as a result of fewer alignment errors [43]. The G4LZI3 model was generated using MODELLER and the structure accuracy determined by a Ramachandran plot, which is a 2-dimensional graphic representation of the model that bases its analysis on the presence of torsion angles at specific regions within the map [44]. Results indicated that 98% of all residues were present in the favoured regions, while 2% were in the allowed region, leaving two outliers that did not form part of the active site (Fig. S1). This result showed the predicted model was of good quality structure and this was further corroborated by a Z-score of -5.4 (Fig. S2). This parameter not only displays the fold reliability of the protein but in turn predicts the energy profile of the model.

In recent years, the field of drug discovery has been aided greatly by the docking computational technique, especially in areas that incorporate lead optimization, drug after-effect predictions, and virtual screening. The technique is regarded as a potent complementary tool that screens prospective drug targets and evaluates biomolecular interactions formed by protein-protein or protein-ligand complexes [45]. Docking characterizes the binding affinity of a ligand to a target molecule and the energy released from the complex system [46]. The polyphenols as listed in Table 1 were docked onto the G4LZI3 model using Raccoon [38]. Autodock Vina [40] and Autodock Graphical User [39] after the preparation of the protein using Chimera software [37] (Fig. 1). The docked avenanthramide A-, catechin-, curcumin-, quercetin- and dalbergin-G4LZI3 complexes were considered the best as they possessed the least energy functions of -7.4, -9.4, -7.9, -8.4 and -8.8, respectively (Fig. 1). These complexes were then selected and subjected to MD simulations. The lower the Z-score or binding energy, the closer it can be assumed that the docked complex resembles its native state.

Fig. (1).

Diagrammatic representation of the sequence and homology model of the G4LZI3 protein. The docked complexes of the top five systems were subjected to molecular dynamic simulations. (A higher resolution/colour version of this figure is available in the electronic copy of the article).

3.2. Assessment of System Stability

Root mean square deviation (RMSD) analysis reveals information that can measure the stability or stereochemical variability across a set of structural models; this parameter is complementary to the crystallographic B factor [47-49]. RMSD analysis was performed to validate the stability of all the systems (Fig. 2) and to ensure the accuracy of successive post-dynamic analyses. The RMSD value is in addition an indicator of atom mobility during MD simulation [50]. Therefore, the higher the value the higher the mobility and equally vice versa. As observed in Fig. (2), all systems reached a state of stability and the initial fluctuation observed in the complexes may be attributed to the adjustment of the ligands as they position themselves within the active site during simulation. The average RMSD of the G4LZI3 protein in complex with avenanthramide A, catechin, curcumin, dalbergin and quercetin were 2.61 Å, 2.74 Å, 4.55 Å, 3.44 Å and 5.23 Å, respectively (Fig. 2). The G4LZI3-curcumin and G4LZI3-quercetin complexes exhibited the highest fluctuation, followed closely by the 3.55 Å apo system. System stability of the other docked polyphenols suggests increased stability in ligand binding within the hydrophobic pocket of the G4LZI3 protein [51]. Further analyses were performed to decipher the unique structural versatility observed in these systems.

Fig. (2).

Graph showing root mean square fluctuation of the Cα backbone of the G4LZI3 apo protein (red) as well G4ZLI3 in complex with avenanthramide A (black); catechin (green); dalbergin (dark blue); quercetin (cyan) and curcumin (magenta). (A higher resolution/colour version of this figure is available in the electronic copy of the article).

3.3. Protein Structural Flexibility

To effectively quantify the flexibility observed in the Cα backbone of the G4LZI3 protein, Root Mean Square Fluctuation (RMSF) calculations were performed. Proteins undergo conformational changes even in their free and unbound state. These minor conformations can resemble functional states such as the structure that a protein adopts when it binds to another biological molecule [52, 53].

The ‘perturbations’ that affect the populations of the protein ensemble are mediated by modifications, mutations and interactions with other biomolecules and may have a direct impact on related biological mechanisms [54]. Intuitively, the molecular interaction that occurs during the binding of a protein to other molecules may actively restrict the intrinsic flexibility of the binding region in a protein and in its binding target, which results in a significant loss in flexibility as can be observed in Fig. (3). The G4LZI3 protein in complex with catechin, dalbergin and quercetin systems exhibited the lowest average residue fluctuations (Fig. 3 and Table 2), which may be due to conformational changes, or the tight binding of the ligand, thus effectively reducing the fluctuation of the Cα backbone of the protein. Decreased fluctuation in residues ~10-20, ~55-65 and 110-130 highlights the interaction in the binding of each ligand to G4LZI3. The energy contribution of each of these amino acid residues of the G4LZI3 protein was further expounded.

Fig. (3).

Root mean square fluctuation of G4LZI3 apo protein (red) as well G4ZLI3 in complex with avenanthramide A (black); catechin (green); dalbergin (dark blue); quercetin (cyan) and curcumin (magenta). (A higher resolution/colour version of this figure is available in the electronic copy of the article).

Table 2.

Residue fluctuation of all systems during the 100ns MD simulation measured in Angstroms.

-	Average	Maximum	Minimum	Δa
Avenanthramide	12.36	23.68	0.85	22.83
Catechin	11.67	27.04	0.93	26.11
Dalbergin	11.35	21.63	0.08	21.55
Quercetin	10.86	23.51	0.81	22.70
Curcumin	13.09	31.43	0.09	23.42
Apo	12.41	27.88	0.90	26.98

3.4. Binding of Polyphenols Induce Structural Compactness

The Radius of Gyration (RoG) indicates the compactness of the tertiary structure of a protein and this is denoted by the root mean square distance between the centre of gravity and the edges of an object [51, 55]. All bound systems exhibited a RoG ranging between ~15.58-15.89Å, whilst the G4LZI3 apo protein exhibited a RoG of 16.16 Å (Fig. 4). Observation of the figure indicates an increase in the RoG score of the bound systems during the first ~50ns of the simulation, followed by a maintained decrease in globularity of the bound systems for the remainder of the simulations. The RoG results indicated the bound systems were highly compact in comparison to the apo system, which was the least compact system. The decrease in the RoG of the bound systems is suggestive of ligand binding, resulting from the formation of hydrogen bonds and electrostatic interactions playing a critical role in the binding of each ligand within the catalytic domain, and simultaneously reducing the flexibility of the protein. Interestingly, RMSF and RoG profiles for the G4LZI3 apo protein are consistent with the resultant RMSD profile. Curcumin, however, exhibited a compact system during the first 50ns followed by a sharp decrease in globularity at ~60ns indicating an increase in the protein compactness resulting in structural stability induced by the binding of curcumin to the G4LZI3 protein, thus corresponding to its RMSD value.

Fig. (4).

Radius of gyration of G4LZI3 in complex with a range of selected polyphenol inhibitors. (A higher resolution/colour version of this figure is available in the electronic copy of the article).

3.5. Hydrogen Bond Profile

Fig. (5) represents the hydrogen bonds present in each system, with those bound displaying a greater number of hydrogen bonds as compared to the apo system. With the formation of hydrogen bonds comes protein stability [53]. At the start of the simulation, there is an increase in flexibility of the protein as a larger catalytic space becomes available. Once the ligand binds, the bound systems reach a state of stability facilitated by the formation of hydrogen bond interactions.

Fig. (5).

Stabilizing hydrogen bonds of the G4LZI3 apo protein (red) as well the apo protein in complex with avenanthramide A (black); catechin (green); dalbergin (dark blue); quercetin (cyan) and curcumin (magenta). (A higher resolution/colour version of this figure is available in the electronic copy of the article).

3.6. Binding Free Energy Calculations

The G4LZI3-polyphenol complexes were ranked based on their binding affinities to the G4LZI3 USP using MM-GBSA calculations, a valuable binding free energy determining tool [56]. MM/GBSA calculations were performed to calculate binding free energies for the five different systems during 100ns of each MD simulation trajectory, which revealed specific binding to G4LZI3. The high relative binding free energies observed in all systems are linked to the increase in non-covalent interactions observed between each ligand within the active site, resulting in restricted mobility of the interacting amino acid residues. Although all complexes display relatively high binding free energies, the G4LZI3-catechin complex exhibited the highest binding energy of -55.86 kcal/mol, followed closely by the G4LZI3-curcumin complex with a binding energy of -40.70 kcal/mol.

Fig. (6) shows the change in conformation of the G4LZI3 protein upon binding of curcumin and catechin during 100ns of simulation. The high binding entropy observed in these systems may be a direct result of the conformational entropy brought about by the unique flexibility change upon each ligand's binding. Prominent intermolecular interactions observed in all complexes originated from van der Waals and electrostatic forces (Table 3). The favourable electrostatic interactions are neutralized by the polar solvation effect resulting in a much lower electrostatic contribution. As such, large van der Waals interactions contribute to the overall total binding of G4LZI3 in both complexes. Furthermore, the conformational changes associated with the G4LZI3-catechin complex may be as a result of a high number of interacting residues due to its bulky structure. The G4LZI3-curcumin complex on one hand observably displays a significant conformational change 50ns after the bound complex was formed, maintaining a closed structure in its bound state there afterward. This is in perfect corroboration with the RoG profile of the complex, which portrayed a highly compact and structurally stable complex ~60ns into the simulation analysis. This may be a result of the high number of hydrogen bonds and electrostatic interactions that occurred, which did not allow the protein assume any other conformational shape. These results suggest improved drug design should focus on increasing van der Waals interactions, while simultaneously decreasing the net electrostatic repulsion upon binding. However, optimization of the net electrostatic interaction remains a difficult task because the greater the direct electrostatic interactions are between an inhibitor and a protein, the more likely an increase in polar solvation penalty.

Fig. (6).

Conformational changes of G4LZI3 in association with bound curcumin and catechin over 100ns simulation. (A higher resolution/colour version of this figure is available in the electronic copy of the article).

Table 3.

MM/GBSA-based binding free energy profile analysis.

Systems	Energy Components (kcal/mol)
	ΔEvdw	ΔEele	ΔGgas	ΔGsol	ΔGbiod
Avenanthramide	-35.48±0.16	-24.35±0.43	-59.84±0.41	32.59±0.33	-27.24±0.16
Catechin	-55.11±0.11	-85.86±0.35	-140.97±0.33	85.11±0.22	-55.86±0.15
Curcumin	-49.15±0.08	-21.11±0.18	-70.26±0.20	29.56±0.15	-40.70±0.09
Dalbergin	-37.84±0.07	-21.53±0.14	-59.37±0.17	24.93±0.08	-34.44±0.10
Quercetin	-33.06±0.09	-37.85±0.36	-70.90±0.31	35.75±0.19	-35.15±0.14

3.7. Per-Residue Energy Decomposition

It was interesting to note that although binding of the compounds occurred at varying spatial landscapes, there was a conservation of interacting residues at the active site among all complexes. Fig. (7) depicts the binding energies of various amino acids within the best two and least-favourable complex. Overall, the major interacting residues conserved in all complexes were Asp11; Ile39; Gly120; Arg122 and Gly123. The lesser the value in kJ/mol, the more involved that particular amino acid in the binding of the ligand to the complex, showing that Asp11 was the highest energy contributing residue in all complexes that formed a hydrogen bond interaction with each ligand [57]. A combination of hydrogen bond interactions (Fig. 5) along with hydrophobic interactions (Fig. 6) plays an essential role in stabilizing the binding of each ligand within the active site. Identifying these crucial residues and their respective interactions provides basis for further structure-based drug design of new compounds, which could possess inhibitory potency against the G4LZI3 protein. This could also allow for the construction of a pharmacophore model for screening new hits from large databases.

Fig. (7).

Per-residue energy decomposition of G4LZI3 in complex with (a) avenathramide A (black); (b) catechin (green); and (c) curcumin (magenta). (A higher resolution/colour version of this figure is available in the electronic copy of the article).

CONCLUSION

Schistosomiasis is an ancient and major helminthic disease that has seen several abortive attempts for its long-term elimination and control. This study predicted the structure of the G4LZI3 USP and the conformational changes associated with the interaction of the protein to a range of polyphenolic compounds. Relatively high binding free energies were displayed among all the systems, which displayed a high level of compactness and stability, although the G4LZI3-catechin and G4LZI3-curcumin exhibited more distinct features. Key conserved interacting residues within the complexes were highlighted and proposed for the design of nutraceuticals or as adjuvants with anti-schistosomal potential. A number of studies in vitro have been done on curcumin and have elucidated its potential as such. Therefore, more studies should be directed towards its mechanism of action and that of other polyphenols, but should also focus on substantiating the results revealed in this study.

Added to the above, we propose the investigation of polyphenols, particularly catechin and curcumin in combination with Praziquantel treatment. Apart from the immune response switches the body has to battle with due to schistosomiasis, the production of reactive oxygen species (ROS) generated by both the human host and the parasitic worm deepens the burden of infection. Fortunately, polyphenols are known antioxidative scavengers of ROS. Therefore, combinatorial therapy developed with these polyphenols and PZQ would not only broaden the efficacy of the drug potency against schistosomiasis but could also provide a revolutionary solution for this disease of poverty.

AUTHORS’ CONTRIBUTIONS

All authors contributed equally to this manuscript.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Not applicable.

HUMAN AND ANIMAL RIGHTS

No Animals/Humans were used for studies that are the basics of this research.

CONSENT FOR PUBLICATION

Not applicable.

AVAILABILITY OF DATA AND MATERIALS

The authors confirm that the data supporting the findings of this research are available within the article.

FUNDING

None.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

SUPPLEMENTARY MATERIAL

Supplementary material is available on the publisher’s website along with the published article.