Study the Activity of Natural Ligands of Lettuce as Agonists in the Management of Uremic Pruritus: An In-Silico Simulation Survey

doi:10.21203/rs.3.rs-2112561/v1

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Study the Activity of Natural Ligands of Lettuce as Agonists in the Management of Uremic Pruritus: An In-Silico Simulation Survey

https://doi.org/10.21203/rs.3.rs-2112561/v1

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Uremic pruritus frequently occurs in patients on dialysis for more than three months. It is one of the most prominent, debilitating, and distressing symptoms in these patients, and despite various studies, no definite treatment has been found. The purpose of this research is to investigate the outstanding potential of natural components of lettuce (Lactuca Sativa L.) in the management of pruritus. ADME analysis and PASS predictions were employed to determine the drug likeliness potential of these natural ligands. A molecular docking study was performed on 19 bioactive ligands of lettuce against four protein receptors. The results showed that most of the selected compounds strongly react with kappa-opioid receptors 4DJH, 6VI4, 6B73, and G protein-coupled receptor 5ZTY and have better binding energies compared to the antipruritic drug gabapentin. The obtained docking scores (DS) and inhibition constants (Ki) revealed that the most potent antipruritic activities are exhibited by campesterol, δ-tocopherol, α-tocopherol, Δ-tocopherol, and α-lactucerol ligands. Our results from the MD simulation of the campesterol-5ZTY complex provide a theoretical validation that the ligand is stabilized in the protein active site. The binding free energy calculations quantitatively show that the association of the ligand to the protein increases the change in the total binding energy. This in-silico approach proposes that the natural ligands of lettuce could exert antipruritic effects by activating kappa-opioid receptors and may become recommended over synthetic drugs.

Kappa opioid receptor

Lettuce

MD simulation

Molecular docking

Natural ligands

Uremic pruritus

Kidney failure is universal public health trouble. This disease has got increasing rate and prevalence and high costs. Chronic kidney disease (CKD) is the earlier stage of kidney failure with unfavorable outcomes, such as cardiovascular disease (CVD), loss of kidney function, and premature death [1]. CKD is defined by persistent urine abnormalities, structural abnormalities, or impaired excretory renal function, suggesting a loss of functional nephrons [2]. Major CKD complications include anemia, metabolic acidosis, secondary hyperparathyroidism, Bone disease and high phosphorus (hyperphosphatemia), heart disease, high potassium (hyperkalemia), Fluid buildup, and Uremic pruritus (UP) [2–4].

Uremic pruritus is associated with poor quality of life, depression, anxiety, sleep disturbance, and increased mortality [3]. It mainly affects the face, the chest, and the limbs and is generalized in 50% of patients [5]. A combination of factors may play an essential role in the etiology of pruritus [6]. Although some hypotheses exist, the physiopathology of chronic itching is complex and not fully understood [7]. Several approaches are currently used for treating patients with CKD-UP, such as gabapentin [8], systemic kappa-opioid receptor agonists and mu-opioid receptor antagonists [9], drugs with anti-inflammatory action, and ultraviolet phototherapy [10]. According to Mettang et al., most therapeutic trials only provided limited success in treating patients [11].

Opioid receptors have been recognized as a target for pruritis therapy. Mu- and kappa-opioid receptors, expressed in the central and peripheral nervous systems, have opposite mechanisms of action. Stimulation of the mu-opioid receptor (MOR) by the antagonists can inhibit itching, while activation of the kappa-opioid receptor (KOR) occurs by an agonist and inhibits itching [12, 13].

Treatments of any pruritus, in general, are divided into three categories: first are the Topical therapies (Cream and lotion form in different dosages containing some chemical compounds like Doxepin, Salicylic acid, and Lidocaine, Scheme 1). Second are Systemic (Oral) therapies such as Antihistamines, Antidepressants (Mirtazapine), Anticonvulsants (Gabapentin) [3], and phototherapy (UVB and IR) [14]. The first treatment methods are beneficial for neuropathic and chronic pruritus. The second treatment methods are helpful for urticaria, nocturnal, paraneoplastic, cholestatic, intractable, and neuropathic pruritus. None of the mentioned itching treatments directly affect UP and are not specific to it.

Recently, researchers evaluated herbal treatments for this disease as alternative medicine. These types of treatments include 1) Aromatherapy with substances having anti-inflammatory effects like Menthol based on sunflower oil [15], 2) Bath therapy using Safflower, Light yellow sophora, Chinese angelica, Rhizoma smilacis glabrae, and other [16], and 3) Oral consumption of compounds such as turmeric, δ-linoleic acid, and Capsaicin, Scheme 2 [17]. According to the research carried out by Pakfetrat et al., consumption of turmeric reduced the severity of UP. They believed that the anti-inflammatory effect of turmeric plays a significant role in UP reduction. [18]. Weisshaar and Makhlough reported that Capsaicin (red chili pepper extract) caused a reduction in UP, and it can have an antipruritic effect [19, 20]. δ-linoleic acid, an essential fatty acid, produces anti-inflammatory precursors and decreases UP [21]. These reports show that medicinal plants and their extracts can significantly reduce itching agents in this disease.

The purpose of this study is to investigate the effect of bioactive components of lettuce, having anti-inflammatory, antipruritic, antihistaminic, antiallergic, or analgesic properties, on the treatment of pruritus. In-silico screening methods were utilized to evaluate the agonist activation potential of these herbal ligands on the receptors involved in the mechanism of pruritus.

2.1. Ligand selection and preparation

Details about diverse components of lettuce (Lactuca Sativa L.), including minerals, vitamins, polyphenols, etc., were collected from the literature. All the natural compounds present in the library were filtered out based on their drug-likeliness and physicochemical properties satisfying Lipinski's rule of five [22]. Nineteen bioactive ligands from lettuce were passed the screening step and selected for the docking process. Gabapentin, an effective antipruritic drug [8], was chosen as the reference ligand.

The 3-D structures of the ligands were obtained from PubChem in .sdf format (PubChem Url: - https://pubchem.ncbi.nlm.nih.gov/). Each ligand was optimized with MM⁺ and AM1 minimization methods using HyperChem 8. Ligand preparation is performed through two steps; first, calculating the partial atom charges using the Gasteiger-Marsili procedure [23], and second, merging nonpolar hydrogens and assigning rotatable bonds. Eventually, the optimized structures were saved in .mol2 format.

2.2. Receptor preparation

The 3-D structures of four target proteins were downloaded from the Protein Data Bank (Url: -www.rcsb.org) as .pdb files with high resolutions. Human kappa opioid receptor (PDB ID: 4DJH), kappa opioid receptors (PDB ID: 6VI4 and 6B73), and G protein-coupled receptor (PDB ID: 5ZTY) were the protein candidates for antipruritic study. AutoDock Tools were used to prepare individual receptors as follows. The co-crystallized ligands and water molecules were eliminated, missing hydrogens were added, the Kollman united atom charges were determined, nonpolar hydrogens were merged, and desolvation parameters were assigned to each protein atom [24]. Minimized protein structures were saved eventually as .pdb files.

2.3. Binding site prediction

Detection of the binding sites of the receptors was achieved utilizing the AutoGrid program of AutoDock 4.2, and the grid box was made with a spacing of 0.375 Å. In some proteins, the grid pocket was built based on the position of bound co-crystal ligands; in others, it was obtained by using the literature reports.

2.4. Molecular docking

All docking simulations were conducted using the DOCKFACE program (an in-house batch script). Sakhteman et al. programmed this script to operate AutoDock 4.2 step by step, including ligand preparation, receptor preparation, grid map generation, dpf file preparation, and completion of the docking runs [25, 26]. After adding partial atomic charges and atom types for each ligand using MGLTOOLS 1.5.6. the protein and ligand .pdb files transformed to .pdbqt files. In order to begin each docking process, the predicted binding pocket was set. The ligands were considered flexible, and the receptors were kept rigid. Among various standard algorithms, the Lamarckian genetic algorithm (LGA) was chosen with these setups: 100 No. of GA runs, 2,500,000 maximum No. of energy evaluations, 27000 maximum generations, 150 population size, a gene mutation rate of 0.02, and a crossover rate of 0.8. All simulations were done on a core i7 personal computer (CPU at 8 MB).

The best docking results between the created binding poses were further investigated regarding the least binding energies and high interactions. Different software was applied to analyze the interactions, including the PyMOL molecular graphics program, the AutoDock Tools program (ADT, Version 1.5.6), and an online program called PLIP (fully automated protein-ligand interaction profiler) [27, 28]. The docking results were considered reliable based on RMSD values below 2 Å. The docking accuracy was checked by redocking the co-crystalized ligands to the corresponding protein receptors [29].

2.5. Molecular dynamics simulation

The best docked complex, regarding the lowest docking score and more binding interactions, was selected to perform molecular dynamic (MD) simulation. The complex was used as input for the CHARMM-GUI server to prepare input files with the CHARMM36 force field. Molecular dynamics simulation was performed using Gromacs 2021.5 software. First, the correct status of the ionizable amino acids, such as histidine, glutamate, aspartate, arginine, and lysine, was calculated using the PropKa script. Then, chlorine and sodium ions were added with a concentration of 150 mM. Also, 18 chlorine ions were added to the system to neutralize the net charge. TIP3P water model was used as the solvent. The protein-ligand complex was inserted into the POPC membrane with 242 POPC molecules.

The minimization and balancing steps were performed according to the protocol available on the CHARMM-GUI server. According to this protocol, energy reduction was made on the structures with 5000 steps using the steepest descent method to eliminate inappropriate interactions. Next, two stages of temperature equilibration were accomplished under NVT conditions at a time step of 1 fs in 125,000 steps at a temperature of 310 K. The pressure of the system equilibrated in three stages (each stage 250000 steps), in the time step of 2 fs under NPT conditions. The only difference in each step is the force required to maintain the heavy atoms of the protein molecules, the ligand, and POPC in their initial positions. Finally, the production stage was carried out in 100 ns under NPT conditions with the time step of 2 fs. Also, Berendsen's algorithms were used in the equilibration stages to balance the temperature and pressure of the system. In the production stage, the temperature of the system was kept constant with the Nose-Hoover algorithm, and the pressure of the system was kept constant with the Parrinello-Rahman algorithm. Also, the pressure of the system was brought to equilibrium under semi-isotropic conditions. PME method was used to calculate long-range electrostatic interactions. The cut-off method was also used to calculate van der Waals interactions. Also, the cut-off value for van der Waals and electrostatic interactions was considered 1.2 nm.

2.6. Binding energy calculations

After performing the molecular dynamics simulation, one of the most critical analyses is calculating the relative free energy of binding between the protein and the ligand. This calculation was made by the MMPBSA method and using the gmx-MMPBSA software. In this method, the Poisson-Boltzmann (PB) solvation model is applied, which extracts frames with equal distances from the simulation output for simplification, and calculates the free energy based on them.

The binding free energy is calculated using the difference between the receptor free energy, the ligand free energy, and the complex free energy:

ΔG_binding = G_complex – (G_receptor + G_ligand) (1)

2.6. Physicochemical properties

Predicting the pharmacological activity of a proposed ligand requires calculating its ADME parameters (absorption, distribution, metabolism, and excretion). The physicochemical properties of the natural compounds were assessed using the webserver SwissADME (Url:- https://www.swissadme.ch) [30]. Lipinski's rule of five was considered to evaluate the positive drug-likeliness properties of the ligands [31].

2.7. PASS predictions

The PASS prediction screening program is a useful online tool that evaluates the ligands' biological activities (http://www.way2drug.com/passonline/) [32, 33]. The probability of a ligand being or not being active against a receptor is expressed by the ratio of Pa (probability to be active) and Pi (probability to be inactive). The probability of biological activity increases when Pa > Pi.

3.1. Molecular docking analysis for antipruritic study

Searching to find plant-derived agonists or antagonists is a practical way to obtain complementary treatments and limit the side effects of synthetic drugs [34]. Researchers have widely used molecular docking studies to predict the activation or inhibition of a target protein by compounds, hoping to find low-risk and helpful medications to treat diseases.

In this research, 19 bioactive compounds of lettuce were chosen to study the antipruritic activity of this natural food. These ligands were screened based on their PASS analysis results to have one or more properties, such as anti-inflammatory, antipruritic, antiallergic, antihistaminic, and analgesic. Molecular docking simulation was carried out for the selected components of lettuce against three kappa-opioid receptors and one G protein-coupled receptor to get deeper insights regarding the possible binding of the ligands within the protein active site following their predicted physicochemical and drug-likeness properties. Docking scores (DS) and inhibition constants (K_i) were considered in the docked complexes alongside different interactions such as hydrogen bonds, H-Pi interactions, Pi-Pi stacking, Pi-cation bonds, hydrophobic interactions, and salt bridges. PyMOL and PLIP programs were used to visualize the interactions and investigate the antipruritic potential of the proposed ligands. In order to validate the docking process, existing co-crystallized ligands were redocked to the corresponding receptors giving acceptable RMSD values of ≤ 2 Å.

3.1.1. Docking with human kappa opioid receptor, 4DHJ

The human kappa opioid receptor (PDB ID: 4DJH), selected through the literature review, docked to the ligands via a series of interactions, resulting in docking scores ranging from − 2.33 to -11.72 kcal/mol [35]. Docking scores, inhibition constants, and the number of hydrogen bonds for the interactions between the receptor and selected compounds are depicted in the first column of Table 1. δ-tocopherol, Δ-tocopherol, and campesterol were shown with significant docking scores of -11.72, -11.29, and − 11.24, respectively. δ-tocopherol formed one hydrogen bond with Lys227 and hydrophobic interactions with Val108, Asp138, Val230, Ile290, His291, Ile294, Ile316, and Tyr320. Two hydrogen bonds were observed for Δ-tocopherol with Lys227 and Phe231. Besides, it shared hydrophobic interactions with Tyr139, lys227, Val230, Trp287, Ile290, Ile294, Tyr312, and Ile316. Campesterol formed one hydrogen bond with Lys227 and hydrophobic interactions with Gln115, Asp138, Tyr139, Trp287, Ile290, Ile294, Ile316, and Tyr320. α-tocopherol (DS= -10.84) also interacted with Lys227 via hydrogen bonding and several residues such as Val118, Gln115, Trp124, Asp138, Tyr139, Lys227, Val230, Ile290, His291, Ile294, Ile316 were engaged in hydrophobic interactions. α-lactucerol, riboflavin, and thiamine have good binding affinities toward the human kappa opioid receptor, -9.66, -7.93, and − 7.38 kcal/mol, respectively. These abovementioned bioactive ligands revealed significantly better docking scores than the standard antipruritic drug. Gabapentin (DS= -5.68 kcal/mol) docked to the active site of the receptor via three hydrogen bonds with Asp138 (2.54, 2.70, and 3.01 Å) and hydrophobic interactions with Val230, Ile290, and Ile294. Analysis of the best-docked pose for the interaction of Δ-tocopherol with 4DJH is represented in Fig. 1a.

3.1.2. Docking with kappa opioid receptor, 6VI4

The kappa-opioid receptor (PDB ID: 6VI4) revealed significant results with bioactive lettuce components, summarized in the second column of Table 1. As can be seen, the best binding abilities toward 6VI4 are in the sequence campesterol > α-lactucerol > α-tocopherol > δ-tocopherol > Δ-tocopherol with lots of convergence points. Campesterol has shown the highest potential with DS = -12.49 kcal/mol. One hydrogen bond was formed with Cys315 (2.97 Å) residue and hydrophobic interactions with Thr111, Phe114, Gln115, Val118, Trp124 Val134, Asp138, Trp287, Ile290, Ile316, and Tyr320. α-lactucerol, α-tocopherol, and δ-tocopherol have revealed almost close docking scores of -11.84, -11.22, and − 11.04 kcal/mol, respectively. α-lactucerol has docked to the receptor via a hydrogen bond with Glu209 (3.18 Å) and several hydrophobic interactions with Thr111, Gln115, Val118, Trp124, Val134, Asp138, Glu209, and Tyr312. Visualizing the docked complexes showed that α-tocopherol and δ-tocopherol both shared one hydrogen bond with Lys227. Similar hydrophobic interactions have also been observed (Val108, Tyr139, Val230, Trp287, Ile290, Ile294, and Tyr320). Δ-tocopherol (DS= -10.86 kcal/mol) has formed two hydrogen bonds with Cys210 (2.56 and 3.07 Å), and lots of residues have been involved in the hydrophobic interactions as follows: Val108, Thr111, Phe114, Gln115, Val118, Trp124, Val134, Asp138, Trp287, Ile290, Ile316, Tyr320. Riboflavin (DS= -9.05 kcal/mol) shared five hydrogen bonds with the kappa opioid receptor residues, Val134 (2.82 Å), Asp138 (2.57 and 2.80 Å), Tyr312 (2.65 and 2.90 Å). A π-stacking with Tyr320 and hydrophobic interactions with Val108 and Trp287 were also observed. Thiamin formed three hydrogen bonds with Asp138 (2.95 and 3.10 Å) and Asn141 (2.85 Å), a π-stacking with Tyr320 and H-π interaction from its 5-ring to Thr111. Val108 and Ile290 were also involved in hydrophobic interactions, leading to a docking score of -7.45 kcal/mol. Gabapentin exhibited a weak effect with DS= -5.66 kcal/mol. It shared two hydrogen bonds with Thr111 (3.05 Å) and Asp138 (2.44 Å), besides hydrophobic interactions with Ile316 and Tyr320. The interactions observed for Δ-tocopherol with 6VI4 are represented in Fig. 1b.

3.1.3. Docking with kappa opioid receptor, 6B73

Another kappa-opioid receptor, which can be engaged in the pathogenesis of pruritus, is PDB ID 6B73. For this receptor, the interaction order is listed as follows: campesterol > α-tocopherol > δ-tocopherol > α-lactucerol > Δ-tocopherol. Docking scores, number of hydrogen bonds, and inhibition constants for the interaction of the 6B73 receptor and the nominated bioactive ligands of lettuce are collected in the third column of Table 1. Again, the best result is obtained for campesterol with DS= -12.38 kcal/mol. Campesterol docked to the target receptor via two hydrogen bonds with Ser323 (3.36 Å) and Gly319 (3.80 Å) and hydrophobic interactions with Val108, Tyr139, Met142, Lys227, Phe231, Trp287, Ile294, Leu295, and Tyr320. Several amino acid residues were observed binding to α-tocopherol (DS= -11.29 kcal/mol), a hydrogen bond with Ile316 (2.93 Å), and hydrophobic interactions with Phe114 and Gln115, Val118, Trp124, Val134, Leu135, Tyr139, Met142, Ile290, Ile294, Tyr320 and a π-stacking with Trp287. δ-tocopherol (DS= -10.81 kcal/mol) formed a hydrogen bond with Ser323 (2.77 Å) and two π-stacking with Trp287 and Tyr320. Hydrophobic interactions with Val108, Tyr139, Met142, Lys227, Val230, Phe231, Ile290, His291, Ile294, Leu295, and Tyr320 stabilized this ligand in the enzyme pocket. α-lactucerol showed a docking score of -10.26 kcal/mol, sharing a hydrogen bond (3.04 Å) and hydrophobic interaction with Tyr313. Other hydrophobic interactions were Leu135, Tyr139, Glu209, Val230, Trp287, IleIle294, Tyr312, and Ile316. Two amino acid residues, His291 and Leu295, are bound via hydrogen binding with Δ-tocopherol (DS=-10.17 kcal/mol). Moreover, Gln115, Trp124, Val134, Leu135, Asp138, Tyr139, Lys227, Val230, His291, Ile294, and Ile316 are involved in hydrophobic interactions. Riboflavin and thiamine exhibited close docking results to gabapentin, -7.57 and − 7.38 kcal/mol, respectively. The number of hydrogen bonds was six for riboflavin, Ile316 (3.41 Å), Tyr312 (2.73, 2.86, and 3.01 Å), Asp138 (2.74 Å), Gln115 (3.12 Å). For thiamine, four hydrogen bonds were formed with Ile290 (3.81 Å), Ser323 (2.75 Å), Asp138 (2.59, 2.84 Å), and two π-stacking with Trp287. Hydrophobic interactions were also involved, such as Val230 and Ile294 for riboflavin and Val108, Trp287, Ile290, Ile294, and Tyr320 for thiamin. Gabapentin demonstrated a docking score of -7.06 kcal/mol. It interacted with the binding residues Ile316, Asp138, Gln115, and Gly319 through hydrogen bonds, besides Val108, Met142, Trp287, and Tyr320, via hydrophobic interactions. The best docking poses of the campesterol against 6B73 are represented in Fig. 1c.

3.1.4. Docking with G protein-coupled receptor, 5ZTY

Docking results of the interactions between nominated compounds and G protein-coupled receptor (5ZTY) are depicted in the last column of Table 1, and analyses of the best docked poses are represented in Fig. 1d. Campesterol showed the most potent inhibitory effect with a docking score of -13.08 kcal/mol. It formed two hydrogen bonds with Asp101 and Lys109 and hydrophobic interactions with Phe87, Phe91, Phe94, Phe106, Phe281, Lys109, Ile110, Val113, and Phe183 residues. The next good docking interaction of 5ZTY was with α-tocopherol and δ-tocopherol, which both showed a docking score of -12.04. α-tocopherol shared a hydrogen bond with Leu182, hydrophobic interactions with Phe87, Phe91, Phe94, Phe106, Val113, Phe117, Phe183, Pro184, Tyr190, Leu191, Trp194, and a π-stacking with His95. Meanwhile, δ-tocopherol formed a hydrogen bond with Tyr25, a π-stacking with His95, and hydrophobic interactions with Tyr25, Phe91, Phe94, Val113, Phe183, Tyr190, Trp194, Val261, Lys278, Phe281, Ala282. Δ- tocopherol and α-lactucerol also exhibited high docking scores, -11.23 and − 11.01 kcal/mol, respectively. Δ- tocopherol shared a hydrogen bond with Leu182, a π-stacking with His95, and hydrophobic interactions with Phe87, Phe91, Phe94, Val113, Phe117, Phe183, Ile186, Tyr190, Leu191, Trp194. α-lactucerol fitted to the receptor active site only by forming hydrophobic interactions with Tyr25, Phe87, Phe91, Ile110, Val113, Leu182, Phe183, Lys278, and Phe281 residues.

Table 1

Antipruritic docking results for the selected bioactive compounds of lettuce against the receptors.
Compound	4DJH [35]			6VI4						5ZTY
	DS \(\text{k}\text{c}\text{a}\text{l}/\text{m}\text{o}\text{l}\)	No. of H-B	Ki µM	DS \(\text{k}\text{c}\text{a}\text{l}/\text{m}\text{o}\text{l}\)	No. of H-B	Ki µM	DS \(\text{k}\text{c}\text{a}\text{l}/\text{m}\text{o}\text{l}\)	No. of H-B	Ki µM	DS kcal/mol	No. of H-B	Ki µM
Alanine	-3.24	3	4220	-3.05	3	5790	-3.51	2	2680	-3.74	4	1820
α-Lactucerol	-9.66	2	0.083	-11.84	1	0.002	-10.26	1	0.029	-11.01	0	0.008
α-Linolenic acid	-6.50	1	17.32	-7.06	1	6.74	-6.91	2	8.55	-7.61	2	2.65
α-Tocopherol	-10.84	1	0.011	-11.22	1	0.006	-11.29	1	0.005	-12.04	1	0.001
Arginine	-5.20	6	154.21	-5.00	6	215.29	-5.13	4	172.88	-4.29	4	714.98
Ascorbic acid	-4.61	4	416.13	-4.12	3	954.13	-4.65	5	390.86	-4.64	5	395.81
Aspartic acid	-2.33	4	19480	-2.53	6	13910	-2.71	4	7110	-3.05	4	5770
Betaine	-2.77	1	9260	-2.46	3	15630	-2.88	1	7740	-2.64	1	11600
Caffeic acid	-5.33	4	123.69	-4.60	4	422.52	-5.32	3	125.62	-5.21	3	151.41
Campesterol	-11.24	1	0.005	-12.49	1	0.0007	-12.38	2	0.000	-13.08	2	0.000
Choline	-3.29	1	3900	-3.21	2	4410	-3.42	1	3130	-2.68	1	10780
Δ-Tocopherol	-11.29	2	0.005	-10.86	2	0.011	-10.17	2	0.034	-11.23	1	0.006
δ-Tocopherol	-11.72	1	0.002	-11.04	2	0.008	-10.81	1	0.012	-12.04	1	0.001
Glutamic acid	-3.27	4	4040	-3.25	1	4170	-2.68	4	13520	-2.58	3	12890
Niacin	-3.76	1	1740	-3.80	1	1630	-3.81	2	1620	-3.98	1	1200
Pantothenic acid	-3.99	4	1200	-3.84	5	1530	-4.06	4	1060	-4.66	2	384.52
Pyridoxine	-5.02	4	210.20	-4.66	3	380.82	-5.36	3	118.22	-5.02	4	210.39
Riboflavin	-7.93	3	1.54	-9.05	5	0.233	-7.57	6	2.81	-9.00	7	0.25
Thiamine	-7.38	2	3.90	-7.45	3	3.45	-7.38	4	3.89	-6.97	3	7.79
Gabapentin*	-5.68	3	68.98	-5.66	2	70.82	-7.06	4	6.68	-5.38	2	113.83

*Gabapentin is the antipruritic reference drug.

Riboflavin with a docking score of -9.00 kcal/mol docked to the receptor via seven hydrogen binding with Leu182, Ser285, and His95, hydrophobic interactions with Phe106 and Val113, and a π-stacking with Phe91. According to the results, gabapentin demonstrated weaker binding energies than the ligands against G protein-coupled receptors. Gabapentin (DS= -5.38 kcal/mol) docked to the receptor through two hydrogen bonds with Glu181 and Tyr25 and hydrophobic interactions with Tyr25, Phe94, and Leu182. Figure 1d demonstrated the interaction between campesterol and the 5ZTY receptor.

3.2. Molecular dynamic simulation analysis

The molecular dynamic simulation was carried out on the campesterol–5ZTY complex to validate the docking results. The simulation results obtained from Gromacs software were investigated in root mean square deviations (RMSD), root mean square fluctuations (RMSF), the radius of gyration (RoG), and the number of hydrogen interactions using gmx rms, gmx rmsf, gmx gyrate, and gmx hbond modules.

The RMSD between created structures during molecular dynamics simulation is a standard measure to ensure the structural stability of the natural and mutated proteins. The results of the RMSD changes are shown in Fig. 2a. As can be seen, first, the RMSD diagram was calculated for all amino acids of the protein (black diagram). The graph shows a very high fluctuation during the simulation. In the second diagram (red diagram), the extramembrane domain of the protein was removed, and the RMSD diagram was calculated for the amino acids inside the membrane. This RMSD diagram increased at the beginning, and after 75 ns, it reached about 0.2 nm, which remained until the end.

The RMSF of alpha carbon atoms was considered to investigate the structural flexibility of the protein in the presence of the ligand. Figure 2b shows that the flexibility in the extramembrane area (amino acids 1000–1160) is higher than in the inside membrane. According to this figure, the fluctuation value of most amino acids in the inner membrane region is less than 0.3 nm, and the flexibility is high only in the first and last amino acids. Meanwhile, in the extramembrane region (amino acids 1020–1060), the fluctuation value has reached 0.6 nm, and actually, the high flexibility of this region has caused the high fluctuation of the RMSD black diagram.

The radius of gyration is one of the critical parameters indicating the compactness of protein during simulation. Figure 2c shows that at the beginning, the RoG is around 96.2 nm, fluctuating around this value until the end, and does not reveal a noticeable change during the simulation.

One of the crucial factors in the stability of the ligand in the binding site of the protein is the number of hydrogen bonds. During the MD simulation, the number of hydrogen interactions between the protein and the ligand is 1 in most cases (Fig. 2d). It is not far from expected since there is only one hydroxyl functional group in the ligand.

Figure 3a shows the conformation of the ligand in the protein binding site before and after the simulation. The ligand has only slightly changed at the end of the process, but its orientation is preserved. A two-dimensional representation of the amino acids involved in the interaction of campesterol with 5ZTY protein, before and after simulation, is indicated in Fig. 3b.

3.3. Binding energy contributions

In this study, the complex of campesterol with 5ZTY was subjected to MD analyses and subsequent MMPBSA-based prediction of total relative binding energy. The PB model extracted and analyzed five hundred frames from the simulation. The energy contributions are summarized in Table 2. According to the theoretical calculation results, the van der Waals energy value (-52.79 kcal/mol) plays a prominent role in binding the ligand to the protein. The electrostatic energy is zero, and so is the polar contribution of solvation energy (ΔE_POLAR). The nonpolar contribution of solvation energy (ΔE_NPOLAR) equals − 38.04 kcal/mol. It is reasonably expected as campesterol forms just one hydrogen bond with Asp101. The amount of gas-phase molecular mechanics free energy is -52.80 kcal/mol, and the solvation free energy is 31.32 kcal/mol, which has an unfavorable contribution. Finally, the total binding free energy of MMPBSA for this complex is calculated as -21.47 kcal/mol. These calculations quantitatively show that the strong hydrogen bond formation between campesterol and the key residue Asp101 was recognized and postulated to be the reason for the high association potential of the ligand and increases the change in the total binding energy.

Table 2

Energy contribution on the association of the ligand to the protein obtained by the MMPBSA method.
Energy Contribution	ΔE_VDW	ΔE_EEL	ΔE_POLAR	ΔE_NPOLAR	ΔE_DISPER	ΔG_GAS	ΔG_SOLV	∆G_MMPBSA
Values (kcal/mol)	-52.79	0	0	-38.04	69.36	-52.80	31.32	-21.47

ΔE_VDW: Change in van der Waals interaction on ligand associations

ΔE_EEL: Change in electrostatic interaction on ligand associations

ΔE_POLAR: Polar contribution to the solvation energy

ΔE_NPOLAR: Nonpolar contribution of repulsive solute-solvent interactions to the solvation energy

ΔE_DISPER: Nonpolar contribution of attractive solute-solvent interactions to the solvation energy

ΔG_GAS: Total gas phase molecular mechanics energy

ΔG_SOLV: Total solvation energy

ΔG_MMPBSA: Total binding free energy of MMPBSA

3.4. Physicochemical analysis

The phytochemical descriptors of a set of natural compounds from lettuce were calculated and analyzed using ADME assay through the SWISSADME online server. Molecular weight, number of rotatable bonds, number of H-bond donors, number of H-bond acceptors, lipophilicity (log P), water solubility (log S), and GI absorption were screened to evaluate their drug-like properties.

Nineteen bioactive compounds from lettuce were fitted well in the range of defined filters, and their ADME properties are given in Table 3. Molecular weight for all molecules was < 500, also, the number of H-bond donors (≤ 5) and H-bond acceptors (≤ 10) sufficiently passed through the filters. Most candidates showed good absorption ability due to their high solubility levels. Some ligands had low GI absorptions or high lipophilicities, but these compounds agree with Lipinski's rule of five.

Table 3

List of physicochemical properties of lettuce bioactive compounds [35].
Compound	PID	MW (g/mol)	RB	HBD	HBA	Log P	Log S	GA	Lipinski Violation
Alanine	5950	89.09	1	2	3	0.34	1.54	high	0
α-Lactucerol	115250	426.72	0	1	1	4.67	-8.24	low	1
α-Linolenic acid	5280934	278.43	13	1	2	3.36	-4.78	high	1
α-Tocopherol	14985	430.71	12	1	2	6.04	-8.60	low	1
Arginine	6322	174.20	5	4	4	0.27	2.05	low	0
Ascorbic acid	5460067	176.12	2	4	6	-0.31	0.23	high	0
Aspartic acid	5960	133.10	3	3	5	-0.09	1.98	high	0
Betaine	247	117.15	2	0	2	-2.19	-0.35	low	0
Caffeic acid	689043	180.16	2	3	4	0.97	-1.89	high	0
Campesterol	173183	400.68	5	1	1	4.97	-7.54	low	1
Choline	305	104.17	2	1	1	-2.14	-0.10	low	0
Δ-Tocopherol	92094	402.65	12	1	2	5.44	-7.98	low	1
δ-Tocopherol	92729	416.68	12	1	2	5.16	-8.29	low	1
Glutamic acid	33032	147.13	4	3	5	0.40	1.84	high	0
Niacin	938	123.11	1	1	3	0.86	-1.26	high	0
Pantothenic acid	6613	219.23	7	4	5	0.95	-0.06	high	0
Pyridoxine	1054	169.18	2	3	4	0.80	-0.64	high	0
Riboflavin	493570	376.36	5	5	8	1.63	-1.31	low	0
Thiamine	1130	265.35	4	2	3	-1.60	-2.32	high	0

PID: PubChem ID, MW: Molecular weight, RB: Number of rotatable bonds, HBA: Number of H-bond acceptors, HBD: Number of H-bond donors, TPSA: Total polar surface area, MR: Molar refractivity, log P: Lipophilicity, log S: Water solubility, GA: Gastrointestinal absorption.

3.5. PASS analysis: biological activity predictions

The PASS prediction screening program studied the compounds with the best interaction results toward the kappa opioid receptor for their anti-inflammatory, antipruritic, antiallergic, and analgesic activities. Table 4 shows that the ligands from lettuce revealed the same biological activities along with kappa-opioid receptor inhibitory. Pa values ranged from 0.220 to 0.814, and higher Pa values than Pi showed potent activity. The reference drug, gabapentin, exhibited kappa-opioid inhibition potential, validating the predicted results.

Table 4

PASS prediction of the nominated bioactive compounds of lettuce and the standard drug.
	α-Lactucerol		α-Tocopherol		Campesterol		Δ-Tocopherol		δ-Tocopherol		Gabapentin
Biological Activity	Pa	Pi	Pa	Pi	Pa	Pi	Pa	Pi	Pa	Pi	Pa	Pi
Antiinflammatory	0.749	0.010	0.814	0.006	0.502	0.056	0.720	0.013	0.775	0.008	0.320	0.144
Antipruritic	0.660	0.010	0.599	0.016	0.774	0.004	0.351	0.076	0.511	0.033	0.307	0.109
Antiallergic	―	―	0.328	0.080	―	―	―	―	0.220	0.164	―	―
Analgesic	―	―	―	―	―	―	―	―	―	―	0.708	0.009
Kappa opioid receptor antagonist	0.283	0.103	―	―	―	―	0.251	0.151	0.223	0.205	0.357	0.040

This study performed a comparative molecular docking analysis on 19 bioactive compounds of lettuce possessing anti-inflammatory, antipruritic, antihistaminic, antiallergic, or analgesic properties. In an in-silico approach, these ligands and the anti-ich drug gabapentin were docked against several kappa-opioid and G protein-coupled receptors related to pruritus. According to the results, most of the nominated ligands were considerably more potent than gabapentin. Campesterol exhibited the best docking scores, highlighting the remarkable antipruritic ability of this bioactive ligand. In addition, δ-tocopherol, α-tocopherol, Δ-tocopherol, and α-lactucerol ligands blocked the active site of the receptors efficiently, proposing that they have good agonist behavior in the management of pruritus. MD simulation studies also confirmed the stability of the campesterol–5ZTY complex based on RMSF and RMSD values. These valuable outcomes can be further investigated by in-vitro or in-vivo studies and provide a lead in exploring appropriate therapy for uremic pruritus.

Statements and Declarations

Funding

This research received no specific grant from public, commercial, or not-for-profit funding agencies.

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author Contributions

All authors contributed to the study's conception and design. Dr. Sepideh Habibzadeh wrote the first draft of the manuscript, and all authors read and approved the final manuscript. Zistpooya Biocomputing Institute performed molecular dynamic simulations.

Acknowledgments

The authors would like to acknowledge Dr. M. Fereidoonnezhad (Ahvaz Jundishapur University of Medical Sciences) for his guidance and encouragement. Also, further thanks to Dr. M. M. Parvizi (Shiraz University of Medical Sciences) for his valuable medicinal comments.

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Scheme 1-2 is available in supplementary section.

No competing interests reported.

Scheme01.png
Scheme 1. Some chemical compounds in Topical therapies for pruritis.
Scheme02.png
Scheme 2. Some chemical compounds in Herbal therapies for pruritis.

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Study the Activity of Natural Ligands of Lettuce as Agonists in the Management of Uremic Pruritus: An In-Silico Simulation Survey

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Ligand selection and preparation

2.2. Receptor preparation

2.3. Binding site prediction

2.4. Molecular docking

2.5. Molecular dynamics simulation

2.6. Binding energy calculations

2.6. Physicochemical properties

2.7. PASS predictions

3. Results And Discussion

3.1. Molecular docking analysis for antipruritic study

3.1.1. Docking with human kappa opioid receptor, 4DHJ

3.1.2. Docking with kappa opioid receptor, 6VI4

3.1.3. Docking with kappa opioid receptor, 6B73

3.1.4. Docking with G protein-coupled receptor, 5ZTY

3.2. Molecular dynamic simulation analysis

3.3. Binding energy contributions

3.4. Physicochemical analysis

3.5. PASS analysis: biological activity predictions

4. Conclusion

Declarations

References

Scheme

Additional Declarations

Supplementary Files

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