Enhancing Quality of Life in Multiple Sclerosis Patients Through Coadministration of FDA-Approved Immunomodulators and Melatonin

Cenk Serhan Ozverel; Emine Erdag

doi:10.33069/cim.2024.0024

Chronobiol Med > Volume 6(3); 2024 > Article

Ozverel and Erdag: Enhancing Quality of Life in Multiple Sclerosis Patients Through Coadministration of FDA-Approved Immunomodulators and Melatonin

Original Article

Chronobiology in Medicine 2024;6(3):135-142.

Published online: September 30, 2024

DOI: https://doi.org/10.33069/cim.2024.0024

Enhancing Quality of Life in Multiple Sclerosis Patients Through Coadministration of FDA-Approved Immunomodulators and Melatonin

Cenk Serhan Ozverel¹

, Emine Erdag²

¹DESAM Research Institute, Near East University, Nicosia, Mersin-10, Türkiye

²Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Near East University, Nicosia, Mersin-10, Türkiye

Corresponding author: Emine Erdag, PhD, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Near East University, Near East Boulevard, 99138 Nicosia, Mersin-10, Türkiye.
Tel: 90-5338898921, E-mail: emine.erdag@neu.edu.tr

Received August 1, 2024 Revised August 14, 2024 Accepted September 13, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective

The primary goal of this study was to assess the binding characteristics and affinities of melatonin and certain U.S. Food and Drug Administration (FDA)-approved lipophilic drugs used in the treatment of multiple sclerosis (MS) with the brain and muscle Arnt-like 1 (BMAL1) clock protein. Additionally, the study aimed to investigate their potential as modulators of BMAL1 activity for therapeutic applications in MS.

Methods

Molecular docking and simulation studies were conducted to investigate the interactions between the BMAL1 protein and the selected agents as ligands. Docking simulations were performed using AutoDock Vina, and key interactions were analyzed with Biovia Discovery Studio Visualizer. A molecular dynamics simulation was conducted using GROMACS 2020.4 and the binding free energies were calculated using the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method. The pharmacokinetic properties of the ligands were predicted using the BOILED-Egg model implemented in the SwissADME web tool.

Results

Melatonin demonstrated the highest binding affinity (-8.5 kcal/mol) and most favorable binding free energy (-245.26±1.27 kJ/mol) with BMAL1, suggesting its potential synergistic activity with other FDA-approved drugs in MS therapy. The pharmacokinetic analysis indicated that all three ligands were likely to cross the blood-brain barrier and exhibit high gastrointestinal absorption, with teriflunomide and melatonin potentially achieving a better profile.

Conclusion

This study highlights the potential of concomitant use of melatonin to modulate BMAL1 activity, providing a foundation for developing targeted chronotherapeutic strategies in MS treatment.

Keywords: Multiple sclerosis; Circadian rhythm; Chronotherapy; Melatonin; Molecular docking

INTRODUCTION

Over 2 million people worldwide suffer from multiple sclerosis (MS), a chronic inflammatory disease of the central nervous system (CNS) [1]. MS often manifests through periods of neurological impairment that can be either completely or partially reversible. Common initial symptoms include optic neuritis, which causes vision loss in one eye, transverse myelitis, resulting in limb weakness or sensory deficits, brainstem dysfunction, leading to double vision, and cerebellar lesions [1,2]. MS involves an abnormal immune response, where the body’s immune system attacks its protective myelin sheath around nerve fibers, leading to disrupted nerve signals. Numerous immune cells, including T cells and B cells, manage this autoimmune response and help to produce inflammatory lesions that lead to neurodegeneration [3].

Immune homeostasis depends on circadian rhythms, the body’s internal clock that controls physiological functions during the day [4]. Circadian rhythms are regulated by a molecular clock within cells, driven by transcriptional-translational feedback loops involving core clock proteins. Two primary proteins, brain and muscle Arnt-like 1 (BMAL1) and circadian locomotor output cycles kaput (CLOCK), form a heterodimer that binds to E-box elements in the promoters of various genes to regulate their expression [4,5]. This heterodimer initiates the transcription of key clock genes, including Period (Per) and Cryptochrome (Cry), which eventually inhibit BMAL1 activity, creating a feedback loop that cycles approximately every 24 hours [5]. This cycle influences various physiological processes, including sleep-wake cycles, hormone release, and metabolism. Disruptions in circadian rhythms are associated with various diseases, including immune-mediated conditions like MS [6].

Recent studies have highlighted the critical role of BMAL1 in modulating immune responses, demonstrating that its loss in myeloid cells exacerbates neuroinflammation and disease severity in experimental autoimmune encephalomyelitis (EAE), a model for MS [7,8]. By coordinating 24-hour physiological rhythms, BMAL1 sustains anti-inflammatory responses and reduces T cell polarization within the CNS. In EAE models, mice deficient in BMAL1 in myeloid cells exhibited exacerbated disease severity characterized by increased infiltration of inflammatory monocytes and T cells, leading to increased production of pro-inflammatory cytokines such as interleukin (IL)-1β and interferon (IFN)-γ [8]. This research conducted by Sutton et al. [8] emphasized the connection between circadian rhythms and autoimmune pathology in MS, showing that BMAL1 in myeloid cells helps to reduce neuroinflammation and autoimmune responses.

Melatonin, a hormone that regulates sleep-wake cycles, exhibits anti-inflammatory and antioxidant properties, making it a promising candidate for MS therapy [9]. Melatonin modulates cytokine profiles by decreasing pro-inflammatory cytokines, including TNF-α and IL-1β while increasing anti-inflammatory cytokines such as IL-10 [9,10]. By targeting clock proteins such as BMAL1, melatonin can help restore circadian regulation and modulate immune responses [11]. Chronotherapy, which aligns treatment with the body’s internal clock [12], can enhance the efficacy of melatonin, potentially improving sleep quality, immune function, and overall health in MS patients. Moreover, lipophilic oral MS therapeutics approved by the U.S. Food and Drug Administration (FDA), such as fingolimod (Gilenya) and teriflunomide (Aubagio), demonstrate efficacy in traversing the bloodbrain barrier (BBB) and modulating immune responses within the central nervous system (CNS) [13]. Fingolimod and teriflunomide exhibit immunomodulatory effects by altering lymphocyte dynamics and functions, thereby modulating immune responses within the CNS. Fingolimod functions as a sphingosine-1-phosphate receptor modulator, sequestering lymphocytes in lymph nodes and reducing their egress into the bloodstream, which decreases the infiltration of autoreactive lymphocytes into the CNS [13]. This action diminishes CNS inflammation and provides neuroprotective benefits by potentially mitigating neurodegenerative processes. Additionally, fingolimod impedes lymphocyte migration across the BBB [13]. Teriflunomide, an inhibitor of dihydroorotate dehydrogenase, impairs de novo pyrimidine synthesis, leading to the inhibition of rapidly proliferating T and B lymphocytes. By reducing the proliferation and activation of these immune cells, teriflunomide decreases the autoimmune-mediated inflammatory responses within the CNS [13]. Combining these drugs with melatonin could enhance therapeutic outcomes by leveraging both direct neuroprotective effects and circadian regulation of immune responses. These drugs are known to exert immunosuppressive effects [14,15]. Consequently, to prevent excessive immune system activation, melatonin could serve as an adjunctive therapeutic option and may be recommended for combination therapy with these medications.

Additionally, the precise role of melatonin in BMAL1 regulation and its potential synergistic effects when coadministered with these immunosuppressive drugs in the treatment of MS remains to be fully elucidated. This study aims to evaluate the binding characteristics and affinities of melatonin and oral MS therapeutics by targeting the BMAL1 clock protein through molecular docking and simulation methods and to identify potential chronotherapy for MS treatment.

METHODS

Selection and preparation of data set

The FDA-approved lipophilic drugs that are well-known for their activity in MS and the circadian hormone melatonin were chosen as ligands for binding the BMAL1 protein before performing in silico analysis. The distinct chemical structures of ligands are represented by the Simplified Molecular Input Line Entry System (SMILES) codes, which were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Then, using LigandScout 4.0 (Inte:Ligand GmbH, Vienna, Austria), the two-dimensional (2D) structures of ligands were transformed into mol2 files [16]. The study utilized the following ligands and their respective PubChem CID codes: fingolimod (PubChem CID 107970), teriflunomide (PubChem CID 54684141), and melatonin (PubChem CID 896). The 2D structures of tested ligands alongside their SMILES codes are indicated in Table 1.

Several refining procedures for the three-dimensional (3D) structures of ligands should be taken into account to prepare them for molecular docking [17]. Initially, each heteroatom and water molecule was eliminated. After adding polar hydrogens to the structures, the partial charges on each atom were calculated using the Gasteiger charge method [17]. These preliminary actions were essential for the molecular docking study that followed. A Protein Data Bank (PDB; https://www.wwpdb.org/) file containing the 3D structure of human CLOCK-BMAL1, with a resolution of 2.40 Å and with an entry code of 4H10, was retrieved for molecular docking. The choice of PDB entry 4H10 was due to its highresolution structure, which provides detailed insights into the binding domains of BMAL1, and its relevance, as it contains the crystallographic structure of the human CLOCK-BMAL1 complex bound to DNA, making it particularly suitable for our docking studies (PDB ID: 4H10, https://doi.org/10.2210/pdb4H10/pdb). Additionally, the presence of the E-box DNA in the 4H10 structure is crucial, as it mimics the physiological environment where BMAL1 exerts its regulatory functions [4]. By using a structure that closely represents the active state of BMAL1 in its natural environment, the study aims to ensure that the docking results are as realistic and applicable as possible to the physiological conditions of BMAL1’s function. This choice may enhance the validity of the findings related to the potential modulation of BMAL1 by melatonin and MS therapeutics.

Identification of binding sites

The CASTp (Computed Atlas of Surface Topography of Proteins) tool [18] was utilized to identify potential ligand binding sites on the human BMAL1 structure. This comprehensive analysis includes the calculation of the area and volume of these sites, providing valuable data for docking simulations. The tool integrates both alpha shape theory and the pocket algorithm, which together enhance its ability to detect even the smallest or deeply buried pockets, which might be missed by other tools [18]. A thorough mapping of the surface topography of the protein is provided by CASTp. This method is essential for selecting binding sites for more research since it not only recognizes the pockets but also assigns a rank to them according to factors including size, shape, and ability to hold ligands [18]. Therefore, CASTp was selected for its accuracy in identifying and measuring concave surface regions on 3D protein structures, such as pockets and cavities, which are potential ligand binding sites.

First, the BMAL1 protein structure was uploaded to the CASTp server (http://sts.bioe.uic.edu/castp/) in PDB format. The protein surface was examined to locate and describe pockets and cavities using geometric parameters including area and volume. The results indicated the presence of many pockets, with special emphasis on those around crucial amino acids such as PHE50, ARG84, ARG85, and LYS111. These identified pockets were subsequently used to focus docking simulations, with AutoDock Vina (The Scripps Research Institute, USA) [19] employed to perform molecular docking within the specified grid boxes covering the regions of interest. This integrated approach allowed for the precise identification and validation of ligand binding sites on BMAL1, facilitating the design of ligands that could enhance BMAL1 activity and expression.

Molecular docking

Consistent grid box sizes of 20×20×20 Å were employed in all docking operations. The grid box parameters, various docking settings, and input files (proteins and ligands) were specified in the configuration file for AutoDock Vina. The configuration file that had been produced was used to launch AutoDock Vina, which ran docking simulations to estimate the binding affinities and poses of ligands with BMAL1. Using the Biovia Discovery Studio Visualizer 2021 (Dassault Systèmes, Vélizy-Villacoublay, France, https://www.3ds.com), significant interactions between the ligands and the amino acids inside the binding sites were investigated. The ligands with the most potential for increasing BMAL1 activity were determined by analyzing their binding affinities and poses.

Molecular dynamics and molecular mechanics/ Poisson-Boltzmann surface area evaluations

A molecular dynamics (MD) simulation running for 100 ns was used to study the interactions between docked ligands and the structure of the human BMAL1 protein. The GROMACS 2020.4 (GROMACS Development Team, https://www.gromacs.org) MD program was used to carry out the simulation assessment. Na+ and Cl– ions were added to the ligand-protein complexes to preserve charge neutrality. Furthermore, the aqueous environment was simulated by modeling water molecules using the simple point charge (SPC) water model [11,20]. To achieve accuracy, a minimum of 1.5 nm was kept between the simulation box edges and the protein [11]. Pressure control was accomplished by the use of the Parrinello-Rahman pressure coupling technique, and the simulation settings were set at 300 kelvin (K) and 1 bar of pressure [21]. The stability of the ligand-protein complexes during the simulation was assessed by analyzing the root-mean-square deviation (RMSD) of the protein backbone atoms.

Using the previously mentioned molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) approach, the binding free energies of the complexes were computed [22-24]. To achieve equilibrium and precise binding affinity predictions, the MM/PBSA computations were run on snapshots taken from the final 20 ns of the MD simulation. The MM/PBSA method computes the binding free energy (ΔG_bind) by deducting the sum of the protein and ligand-free energies (ΔG_protein+ΔG_ligand) from the free energy of the protein-ligand complex (ΔG_complex), as indicated in the following equation:

(1)

∆Gbind=∆Gcomplex-∆Gprotein+∆Gligand.

In silico prediction of absorption properties

For the prediction of the brain and gastrointestinal absorption properties of the ligands, the BOILED-Egg (Brain Or IntestinaL EstimateD permeation method) model [25] was used which was implemented in the SwissADME web tool (http://www.swissadme.ch/). The BBB penetration and human intestinal absorption (HIA) of a substance are two of its pharmacokinetic characteristics that may be visually represented by the BOILED-Egg plot.

Simplified SMILES format was used to derive the chemical structures of the ligands melatonin, teriflunomide, and fingolimod. After that, the SwissADME tool was used to enter these SMILES codes for additional examination. The tool calculated several physicochemical properties of the ligands, including WlogP (Wildman and Crippen’s logP) and TPSA (topological polar surface area). WlogP is a measure of lipophilicity, which impacts the ability of compounds to cross lipid membranes, while TPSA is a measure of the surface area occupied by polar atoms, influencing solubility and permeability [26]. Then, the calculated WlogP and TPSA values were used to generate the BOILED-Egg plot. In this plot, the yellow region represented a high probability of BBB permeability, while the white region indicated high gastrointestinal absorption. The plot also categorized ligands as PGP+ (P-glycoprotein substrates) or PGP- (non-substrates), based on their interaction with P-glycoprotein, an efflux transporter that can limit drug accumulation in the brain [27].

RESULTS

In the binding domain of BMAL1, the molecular docking study assessed the binding affinities and interactions of three ligands: melatonin, teriflunomide, and fingolimod. The docking data, displayed in Table 2, provide information on the particular amino acid residues involved in the interactions with the ligands as well as the binding energies.

Based on the results, fingolimod showed the lowest binding affinity (ΔG) of -7.2 kcal/mol. The key interacting residues identified include ARG47, PHE50, MET88, ARG84, ARG85, and LYS111 (Figure 1). These interactions suggested significant electrostatic and hydrophobic interactions, contributing to the stability of the ligand-protein complex. The involvement of multiple arginine residues (ARG47, ARG84, and ARG85) indicated robust electrostatic interactions, while PHE50 and MET88 contributed to hydrophobic interactions within the binding pocket. In addition, teriflunomide exhibited a slightly higher binding affinity of -7.8 kcal/mol compared to fingolimod. The interacting residues for teriflunomide were ARG47, PHE50, GLU81, ARG84, ARG85, and LYS111. The presence of GLU81 in the interaction profile suggested potential hydrogen bonding interactions in addition to the electrostatic and hydrophobic interactions observed with fingolimod. This combination of interactions underscored the stronger binding affinity and stability of teriflunomide within the BMAL1 binding region.

On the other hand, melatonin exhibited the highest binding affinity) of -8.5 kcal/mol among the three ligands. The interacting residues identified for melatonin include ARG47, PHE50, MET88, ARG84, LYS111, and LEU112. The involvement of LEU112, along with the common residues, indicated enhanced hydrophobic interactions, which likely play a crucial role in stabilizing the ligand-protein complex.

MM/PBSA calculations were carried out to determine the binding free energies of the ligand-protein complexes to validate the molecular docking results. The results, presented in Table 3, indicated the binding energy values (kJ/mol) along with their standard deviations. According to the calculation results, fingolimod exhibited a binding energy of -186.72±2.03 kJ/mol. The MM/PBSA calculation confirmed the moderate binding affinity of fingolimod, consistent with the docking results. The relatively high standard deviation indicated some variability in the binding interactions, suggesting moderate stability of the ligand-protein complex. In addition, teriflunomide showed a binding energy of -202.23±1.32 kJ/mol. The lower binding energy compared to fingolimod reflected a stronger and more stable interaction with BMAL1. The lower standard deviation suggested a more consistent binding profile, indicating stable interactions within the binding region of BMAL1. Among all ligands, melatonin demonstrated the lowest (most favorable) binding energy of -245.26±1.27 kJ/mol. The findings supported the strongest docking affinity found for melatonin. The minimal standard deviation indicated highly stable interactions, underscoring melatonin’s potential as a potent modulator of BMAL1 activity.

The BOILED-Egg plot visually represented the predicted brain and gastrointestinal absorption properties of the ligands fingolimod, teriflunomide, and melatonin, with WlogP and TPSA as the critical parameters for assessing drug permeability and absorption (Figure 2). Fingolimod, represented by the blue circle, was positioned within the yellow region of the plot, indicating its likelihood to permeate the BBB and suggesting high HIA. However, its placement in the PGP+ category implied that it might be effluxed by P-glycoprotein, potentially affecting its concentration in the brain. Melatonin, indicated by the red circle, also fell within the yellow region and overlapped with the HIA region, suggesting excellent BBB permeability and high gastrointestinal absorption. In addition, being categorized as PGP- suggested that teriflunomide might not be a substrate for P-glycoprotein, which could enhance its retention and efficacy within the brain. Teriflunomide, represented by a red circle, was positioned in the yellow region near the border with the white region, predicting efficient BBB crossing and high gastrointestinal absorption. The PGP- status indicated that teriflunomide was unlikely to be effluxed by P-glycoprotein, favoring its accumulation in the CNS.

DISCUSSION

This study evaluated the binding characteristics and affinities of melatonin and oral MS therapeutics, specifically fingolimod and teriflunomide, by targeting the BMAL1 clock protein through molecular docking and simulation studies. The results highlighted the importance of BMAL1 in circadian rhythm regulation and its potential role in modulating immune responses in MS.

Similar to the present study, previous research utilized similar in silico methods to elucidate the interactions of melatonin with the NR1D1 clock protein, which is known to be important in circadian gene regulation, for the treatment of amyotrophic lateral sclerosis (ALS) [28]. In the study, the main focus was on exploring the potential combination of melatonin with FDA-approved drugs for neuroprotection. However, the current research suggested using melatonin as a complement to immunosuppressive therapies, targeting the BMAL1 clock protein and presenting a new strategy for managing MS. Both studies emphasized the importance of melatonin in chronotherapy. However, the findings of the present study offered a more comprehensive perspective on its dual role in immunomodulation and circadian regulation.

In this study, among the tested ligands, melatonin exhibited the highest binding affinity (-8.5 kcal/mol) and the most favorable binding free energy (-245.26±1.27 kJ/mol) according to MM/PBSA calculations. This suggested that melatonin forms a stable and extensive network of interactions with BMAL1, potentially enhancing its activity. The superior binding affinity of melatonin, compared to fingolimod and teriflunomide, underscores its potential as a potent modulator of BMAL1 activity. This finding is supported by other research demonstrating melatonin’s ability to modulate circadian rhythms and immune responses, making it a promising candidate for MS therapy. Fingolimod and teriflunomide also showed significant binding affinities (-7.2 kcal/mol and -7.8 kcal/mol, respectively) and stable interactions with BMAL1. The MM/PBSA calculations revealed binding energies of -186.72± 2.03 kJ/mol for fingolimod and -202.23±1.32 kJ/mol for teriflunomide. These results align with the known efficacy of these drugs in MS treatment, highlighting their potential to modulate BMAL1 activity through specific interactions with key amino acid residues.

The analysis of the BMAL1 interactions reveals critical insights into the specific roles of certain amino acids in mediating these interactions. This study provided a detailed structural and functional analysis of key amino acids in the BMAL1 and CLOCK interaction. As reported in the study of Wang et al. [29], the amino acid residues ARG46 and ARG47 play significant roles in the specific recognition and binding of E-box DNA sequences. ARG47, in particular, demonstrates a looser interaction with the central guanine base of the E-box compared to ARG85. This differential interaction likely contributed to the fine-tuning of DNA binding specificity and flexibility, which is crucial for the precise regulation of clock-controlled genes [29]. The ability of these arginine residues to form hydrogen bonds and electrostatic interactions with DNA highlighted their importance in maintaining the structural flexibility and stability of the protein complex [29,30]. Similarly, the current study indicated the presence of ARG47 in the interaction profile of each ligand. The interactions of ARG47 might contribute to the flexibility and specificity of the DNA-binding mechanism, allowing CLOCK-BMAL1 to selectively recognize and bind to E-box sequences. This differentiation was believed to be critical for the transcriptional regulation of circadian rhythms.

In addition, the present study investigated another amino acid residue, LYS111, which was presumed to play a role in the overall stability of the BMAL1 structure. As reported before, lysine residues are recognized for their participation in electrostatic interactions and for stabilizing protein structures by interacting with the DNA phosphate backbone or other protein domains [31,32]. Therefore, LYS111 was thought to play a potential role in maintaining the structural integrity of BMAL1. Similar to LYS111, LEU112 indicated in the interaction profile of melatonin was likely involved in stabilizing hydrophobic interactions within the protein structure. Current literature suggests that leucine residues often play a significant role in maintaining protein folds and overall conformational stability, which are essential for protein-protein and protein-DNA interactions [33,34].

In the current work, PHE50 was shown to participate in π-π stacking interactions, which was a unique result in the ligand interaction profile. These interactions typically occur when the aromatic rings of phenylalanine stack against similar structures, such as other aromatic rings or planar regions of the ligands. π-π stacking interactions are known to be stabilizing and can significantly enhance the overall binding affinity and stability of the ligand-protein complex [35,36]. The current study demonstrated the critical involvement of these amino acids in ligand binding and their potential for modulating BMAL1 activity. The results suggested that targeting these residues with specific ligands could enhance BMAL1 expression and activity.

Furthermore, the BOILED-Egg study indicated that there was a high probability of all three ligands having high gastrointestinal absorption and crossing the BBB. Teriflunomide and melatonin, being PGP-, might have an advantage in maintaining higher concentrations in the brain compared to fingolimod, which was indicated as a PGP+ substrate and might be effluxed out of the brain cells. These characteristics make them attractive candidates for potential MS therapy that targets BMAL1.

While this study provides valuable insights into the potential interactions between melatonin, fingolimod, teriflunomide, and the BMAL1 protein, it is essential to acknowledge the inherent limitations of in silico methods. Molecular docking and simulation studies rely on static protein structures, which may not fully capture the dynamic conformational changes that occur in vivo. Therefore, while the computational predictions offer a foundation for understanding potential interactions, experimental validation through in vitro and in vivo studies is crucial to confirm these findings and assess their clinical relevance.

In conclusion, the present study provided a comprehensive analysis of the binding characteristics and affinities of melatonin, fingolimod, and teriflunomide with the BMAL1 clock protein using advanced molecular docking and simulation techniques. Based on the findings, melatonin had the highest binding affinity and binding free energy with BMAL1, suggesting that melatonin might be a potent regulator of BMAL1 function, therefore supporting its synergistic potential with oral lipophilic drugs in the therapy of MS. Future experimental validation and in vivo studies are essential to further explore and confirm the therapeutic potential of these compounds in clinical settings.

NOTES

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Availability of Data and Material

The data generated or analyzed during the study are available from the corresponding author upon reasonable request.

Author Contributions

Conceptualization: Cenk Serhan Ozverel, Emine Erdag. Data curation: Cenk Serhan Ozverel. Investigation: Cenk Serhan Ozverel, Emine Erdag. Methodology: Cenk Serhan Ozverel, Emine Erdag. Software: Emine Erdag. Supervision: Cenk Serhan Ozverel. Visualization: Cenk Serhan Ozverel, Emine Erdag. Writing—original draft: Cenk Serhan Ozverel, Emine Erdag. Writing—review & editing: Cenk Serhan Ozverel, Emine Erdag.

Funding Statement

None

Acknowledgments

None

Figure 1.

The crystallographic structure of the E-box-DNA strand, the core clock proteins-BMAL1, and CLOCK (PDB ID: 4H10). A: The amino acid interactions of melatonin in the active binding site of the BMAL1 region are indicated with different colors. Pink dotted lines indicate electrostatic interactions, while green dotted lines show hydrogen bonding. B: The amino acid interactions of teriflunomide in the active binding site of the BMAL1 region are indicated with different colors. Pink dotted lines indicate electrostatic interactions, green dotted lines show hydrogen bonding, and blue dotted lines of fluorine atoms indicate halogen bonding. C: The amino acid interactions of fingolimod in the active binding site of the BMAL1 region are shown as pink dotted lines, all indicating electrostatic interactions with the corresponding amino acids.

Figure 2.

The BOILED-Egg plot of teriflunomide, fingolimod, and melatonin as a visual representation of their pharmacokinetic properties; blood-brain barrier (BBB) penetration and human intestinal absorption (HIA). WlogP (Wildman and Crippen's logP) and TPSA (topological polar surface area) are considered critical parameters.

Table 1.

Names of tested ligands with their SMILES codes and 2D structures

SMILES, Simplified Molecular Input Line Entry System; 2D, two-dimensional

Table 2.

The binding energy values and interacting amino acids of ligands in the BMAL1 binding region

Name of ligands	Binding affinity, ΔG (kcal/mol)	Interacting amino acid residues
Fingolimod	-7.2	ARG47, PHE50, MET88, ARG84, ARG85, LYS111
Teriflunomide	-7.8	ARG47, PHE50, GLU81, ARG84, ARG85, LYS111
Melatonin	-8.5	ARG47, PHE50, MET88, ARG84, LYS111, LEU112

Table 3.

MM/PBSA calculations of the binding energy values of BMAL1-ligand complexes

Name of ligands	MM/PBSA binding energy (kJ/mol)
Fingolimod	-186.72±2.03
Teriflunomide	-202.23±1.32
Melatonin	-245.26±1.27

Values are presented as mean±standard deviation. MM/PBSA, molecular mechanics/Poisson-Boltzmann surface area

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