INTRODUCTION
Sleep is a pivotal restorative state that is characterized by altered consciousness and reduced sensory activity. This chronobiological adaptation is crucial for maintaining physiological and cognitive well-being [1]. Anomalies in this circadian machinery led to a wide array of chronobiological disorders, which are commonly called sleep disorders [2]. Sleep disorders disrupt daily routines and have detrimental effects on human health, productivity, and overall quality of life [3]. Among various types of sleep disorders, insomnia is the prevalent. It is generally characterized by strain in initiating sleep, maintaining sleep continuity, and poor sleep quality [4]. The Diagnostic and Statistical Manual of Mental Disorders-5 classifies insomnia as episodic if it lasts less than a month and persistent if it lasts more than 3 months [5]. Evidence suggests that 10%–30% of people suffer from chronic insomnia worldwide [6]. Insomnia can be classified as either primary (idiopathic) or secondary (comorbid), depending on whether it is associated with other medical or psychiatric problems [7]. It is frequently associated with other comorbidities, such as chronic pain, neuroinflammation, and hypertension [8]. Psychiatric conditions such as depression and anxiety are the most associated comorbidities related to sleeplessness. It is believed that comorbid psychiatric conditions affect 40% of people with insomnia [9]. The US National Comorbidity Survey revealed a significant correlation between insomnia and psychiatric conditions [10]. According to a study by Breslau et al. [11], people who suffer from insomnia showed four times higher susceptibility towards developing major depression over upcoming 3–5 years. A meta-analysis of 34 cohort studies which involed 150,000 participants reported that people suffering with insomnia are at greater risk of developing anxiety and depression [12].
The causes of insomnia are multifaceted and involve physiological, psychological, environmental, and genetic factors. Neuroinflammation, chronic pain, respiratory illnesses, neurological disorders, and endocrine abnormalities are potential causes of sleep disruptions [13]. It has been reported that smoking, drinking alcohol, watching television for a long period, and a lack of physical exercise are key factors strongly linked to increased rates of insomnia which subsequently results into obesity, type 2 diabetes, and cardiovascular diseases [14,15]. Compared with men, women are more susceptible to this sleeping disorder, as they undergo unique hormonal changes. Additionally, onset of menstruation and menopause onset could increase insomnia prevalency in women [16]. Older people are more susceptible to developing insomnia due to age-related physiologic changes, which lead to fragmented sleep and early morning wakefulness. As people age, their chances of suffering from insomnia increase significantly [17]. Aging adults produce less melatonin hormones, which regulate the sleep–wake cycle [18]. Physical exercise enhances the quality of sleep by lowering the mental, chemical, and physical arousal caused by stress and depression [19].
Studies on humans and animals suggest that genetic factors play crucial roles in the etiology of insomnia [20]. The homozygous clock gene and the 5-HTTP short (s) allele are prominent case of gene variations which has been identified via candidate gene studies. These two gene variations play important roles in insomnia pathophysiology [21]. Furthermore, many single-nucleotide polymorphisms (SNPs) were shown to be strongly correlated with symptoms of insomnia in a genome-wide association study [22]. Genes related to neuroplasticity (e.g., ROR1 [receptor tyrosine kinase like orphan receptor 1], PLCB1 [phospholipase C beta 1], and CACNA1A [calcium voltage-gated channel subunit alpha1 A]), stress reactivity (e.g., STK39 [serine/threonine kinase 39], USP25 [ubiquitin specific peptidase 25]), neuronal excitability (e.g., DLG2 [discs large MAGUK scaffold protein 2]), and mental health (e.g., NPAS3 [neuronal PAS domain protein 3]) have been identified among the most significant SNPs [23].
Serotonin (5-HT) is an important neurotransmitter of the nervous system that has long been known to influence sleep [24]. The implication of 5-HT in sleep regulation has been widely explored suggesting that 5-HT promotes wakefulness (W) and regulates rapid eye movement (REM) sleep [25]. Serotonin receptors are classified into several subclasses on the basis of their distribution and function. According to autoradiographic and immunohistochemical research, 5-HT2 receptors are located in distinct areas of the brain and are divided into three subtypes: 5-HT2A, 5-HT2B, and 5-HT2C [26]. 5-HT2A receptors (5HT2AR), a subtype of 5-HT2, can be found in the basal forebrain, ventral tegmental region, and median raphe nuclei of the brain, all of which promote the awakening state [27]. The activation of the 5HT2AR results in excitatory signals, increasing cortical arousal and disrupting sleep architecture, particularly by reducing slow-wave sleep (SWS) and REM sleep [28]. 5-HT2A inhibitors counteract these excitatory effects by blocking 5HT2AR. Antagonist of 5-HT2A reduce excitatory neurotransmission, decrease cortical arousal, and facilitate sleep onset and maintenance of sleep architecture [29].
Insomnia can be treated with cognitive behavioral therapy (CBT), medications, or through combination of both [30]. Acute insomnia is treated mostly with medication, but chronic insomnia is treated with CBT techniques such as sleep restriction, sensory control, relaxation, and cognitive strategies [31]. However, the 2005 US National Institutes of Health State of the Science Conference on Insomnia concluded that both CBT and medication are more effective in controlling chronic insomnia than CBT alone [32].
Benzodiazepines, nonbenzodiazepine GABAA receptor stimulators (Z-drugs), melatonin receptor activators, ORAs (suvorexant and lemborexant), and antidepressants (low-dose doxepin) are all advised as sleep aids (hypnotics) for insomnia. Benzodiazepines are a type of medication that targets many GABAA receptor subtypes [33]. In the past, benzodiazepines including flurazepam, brotizolam, temazepam, triazolam, estazolam, and quazepam were commonly used to treat insomnia [34]. Although the usefulness of these medications has been well established, their usage is limited by side effects such as daytime sedation (such as hangover in the morning or the next day), cognitive impairment (including anterograde amnesia), motor incoordination, misuse potential, and dependence [35]. As several studies have shown 5HT2AR inhibition can alleviate depression [36], anxiety, and insomnia [37,38], targeting this receptor system with some newly explored phytochemicals is crucial for developing more effective therapeutics for the treatment of insomnia without major side effects [39].
Rauwolfia serpentina is an evergreen shrub commonly known as Indian snakeroot or Sarpagandha. This plant is native to the Indian subcontinent and Eastern Asia. It has long been appreciated in traditional medical systems such as Ayurveda, Unani, and traditional Chinese medicine because of its diverse medicinal characteristics [40]. Its various alkaloids are responsible for a wide range of pharmacological effects, such as the active root alkaloid reserpine. Even psychopharmacological studies have described reserpine primarily as a sedative. However, subsequent research indicated that reserpine is a tranquillizer rather than a sleep promoter [41]. Phytochemical studies have revealed that numerous active compounds that exhibit pharmacological activities can strongly contribute to sleep induction and maintenance [42]. These compounds are supposed to inhibit 5HT2AR, thereby modulating neurotransmitter pathways involved in the regulation of sleep–wake cycles [43]. A study suggested anxiolytic property of R. serpentina as it attenuates stress-induced behavioral deficits and improves locomotor activity [44].
In this study, we conducted in silico analysis to recognize potent phytochemicals present in R. serpentina for their therapeutic effects on insomnia. We selected a suitable plant source in relation to the disease with the help of the Diseases Plants Eliminate (DISPEL; https://compbio.iitr.ac.in/dispel/) server. Furthermore, the phytochemicals present in R. serpentina were assessed via the Indian Medicinal Plants, Phytochemistry And Therapeutics 2.0 (IMPPAT 2.0; https://cb.imsc.res.in/imppat/) server. The structure-data files (SDF) of the phytochemicals present in the selected plants were retrieved from the PubChem database. The threedimensional crystal structure of 5HT2AR was retrieved from the protein databank. Furthermore, we carried out molecular docking, molecular dynamic (MD) simulation, and bioactivity prediction. Absorption, distribution, metabolism, excretion, and toxicity (ADME/T) analysis to determine the preclinical efficacy and safety of phytochemicals. The compounds identified through in silico investigations could serve as prospective drug candidates for the treatment of insomnia.
METHODS
Prediction of plant species
DISPEL aided in the identification of suitable plant species whose compounds could serve as therapeutic candidates for the treatment of insomnia [45]. The DISPEL database comprises comprehensive data covering more than 60,000 links between medicinal plants and diseases. It allows users to probe specific plants or diseases and perform comparative studies. The search data of “insomnia” revealed R. serpentina as one of the most promising plant species for the treatment of this sleep disorder.
Evaluation of phytochemicals present in R. serpentina
The IMPPAT 2.0 server was employed to evaluate the phytochemicals present in R. serpentina [46]. IMMPAT 2.0 is a meticulously designed database that provides detailed insight into medicinal plants in India.
Retrieval of phytochemicals and their preparation
Phytochemicals (ligands) present in R. serpentina were accessed from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) database in SDF file format. The ligand then underwent a preparatory step using UCSF Chimera 1.15 [47]. The energy of the ligands was minimized via the “minimize structure” option in Chimera. The steep descent steps and conjugate gradient steps were set to default settings, a Gasteiger charge was added, and the AMBERff14SB force field was used [48].
Retrieval of the target protein and its preparation
The three-dimensional crystal structure of the target protein, serotonin 2A receptor (5HT2AR), was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB; https://www.rcsb.org/) in PDB file format (PDB ID-6A93). The protein structure was prepared via PyMOL 3.0 [49]. The heteroatoms and water molecules were eliminated, and polar hydrogens were added to the protein.
Molecular docking and virtual screening
AutoDock Vina 1.2.0 [50], which is integrated into PyRx 0.8 [51], was used for molecular docking and virtual screening of the ligands with the target protein structure. Open Babel [52] was used to convert the SDF files of the ligands into pdbqt files to make them executable for AutoDock Vina. For molecular docking, the prepared protein structure was first designated as a macromolecule. The converted pdbqt files of the ligands were used for docking. The grid box used for docking measured 96.65×40.76×49.99 Å, with coordinates of 35.55, -0.46, and 59.20. Exhaustiveness was set to 8 by default.
Blood–brain barrier permeability
Blood–brain barrier (BBB) permeability is an important property that must be taken into consideration when investigating phytochemicals for their drug-likeness properties in the context of brain-related disorders. Phytochemicals must be able to cross the BBB to interact with their target proteins for their manifestation. The BBB permeability of the ligands was assessed via Brain or IntestinaL EstimateD permeation method (BOILED-EGG) analysis via the SWISS ADME server [53].
Physicochemical properties of the phytochemicals
The SWISS ADME [54] webserver was used to evaluate the physiochemical properties of the phytochemicals. Parameters such as the molecular weight, total carbon count of the molecule (fraction Csp3), rotatable bonds, hydrogen-bond acceptors, hydrogen-bond donors, molar refractivity, and topological polar surface area (TPSA) were taken into consideration. These parameters aid in the prediction and identification of the fate of the compounds.
Prediction of the biological activity of phytochemicals
The biological activity of the phytochemicals was analyzed via the Molinspiration Cheminformatics server [55]. As the target protein belongs to the g-protein coupled receptor (GPCR) family, the probability of “GPCR ligand” activity was taken into consideration and analyzed via the aforementioned server.
Analysis of the drug-likeness properties of phytochemicals
Lipinski’s rule of five [56] was used as a benchmark to evaluate the drug-likeness of the phytochemicals. The analysis was performed via the Supercomputing Facility for Bioinformatics & Computational Biology, IIT Delhi server (http://www.scfbio-iitd.res.in/software/drugdesign/lipinski.jsp) [57].
MD simulation
An MD simulation study was performed to assess the stability of the protein–ligand complex obtained after molecular docking. Molecular docking confirms the binding affinity of the ligands with the target protein, whereas MD simulation aids in analyzing the stability of the receptor–ligand complex after docking. GROMACS from the WEBGRO server (https://simlab.uams.edu/index.php) was used to perform the MD simulations. The GROMOS96 43A1 forcefield was used, the water model type was set to simple point charge, and the triclinic box type was selected. Furthermore, the steepest descent integrator was used for energy minimization, and the number of minimization steps was set to 5,000. The equilibration and run parameters of the MD simulation, such as the temperature, pressure bar, and MD integrator, were set to 300 K, 1.0 bar, and leap-frog, respectively. The MD simulation yielded root mean square deviation (RMSD), radius of gyration (Rg), and solvent accessible surface area (SASA) trajectories, which are key indicators of the compactness and stability of the protein–ligand complex. SASA is an important parameter to assess drug-protein interaction. It analyzes the surface area of the target protein which is exposed to a solvent, and thus aid in assessment of binding strength and hydrophobic/hydrophilic balance. It serves an insight into stability and conformation changes that occur in the protein in presence of a ligand thus making it a key parameter for improving pharmacological profile of the drug compound [60-62]. These parameters were used to analyze the stability of phytochemicals and the 5HT2AR complex, which further aided in the evaluation of the antagonistic properties of these phytochemicals against 5HT2AR.
Normal mode analysis
The iMODS server was used to perform normal mode analysis (NMA) [63]. NMA coordinates generate the cumulative functional dynamics of the biological macromolecule. “HA” coarse grains were selected, which considers all heavy atoms for NMA. It provides output in the form of a B-factor, deformability plot, eigenvalues, variance, covariance, and elastic network models. The atomic displacement or flexibility of a protein structure can be measured with the use of the B factor. Increased flexibility is indicated by its high value. According to a comparison of the NMA and PDB Bfactors, the protein’s dynamic behavior is accurately reflected by the normal modes. The deformability plot shows how easily the molecule can be distorted, with peaks denoting locations with greater flexibility. Understanding the functional motions within the protein structure depends on these specific areas. The rigidity of the modes is revealed by the eigenvalue plot. Modes that are more prone to deformation are associated with lower eigenvalues. The cumulative variation explained by the modes is shown on the variance graph. More complex motions are captured by higher modes. This cumulative graph helps determine the number of modes required to explain a significant amount of the dynamic behavior of the protein. Darker areas indicate greater interactions between atoms in the grayscale map of pairwise interaction strengths provided by the elastic network model. The structural integrity of the target protein is preserved by the highlighted areas, which implies greater stiffness. Moreover, coupled motions between residue pairs are revealed by the covariance map. Residues moving together are shown by substantial positive correlations in the red regions. Negative correlations, or opposing movements, are indicated by the blue patches. This knowledge is essential for comprehending the collective movements that occur inside the protein and locating important residues that are engaged in these movements.
Visualization of interactions
The interaction between the phytochemical and the active site of the target protein was visualized and studied via Discovery Studio 2021 [64] and PyMOL 3.0. These tools facilitate the projection of 2D and 3D visualizations of protein–ligand interactions. Interacting amino acids and types of interactions were studied, which provided crucial information for analyzing the binding affinity and stability of the complexes.
RESULTS
Virtual screening and molecular docking
The results of the molecular docking and virtual screening performed via PyRx 0.8 are summarized in Table 1. The lowest docking score was observed for serpentinine, followed by 3-oxorhazinilam. The docking score ranged between -9.9 and -7.1 kcal/mol, while the mean docking score was found to be -8.87 kcal/mol. All the chosen phytochemicals showed significant binding affinity (docking score) with the target protein. Typically, lower docking scores (less than -7 kcal/mol) suggest stronger binding affinity [65].
BBB permeability
The permeability of the BBB to phytochemicals was assessed via BOILED-EGG analysis. The results of the analysis are depicted in Figure 1. The ligands that fall in the yellow (yolk) region are considered to be BBB permeant, whereas those beyond this region do not have this property. A simplified representation of molecules with BBB permeability is shown in Figure 2.
From the analysis, we conclude that most of the phytochemicals that were screened possess BBB permeability. Moreover, only the top three phytochemicals on the basis of their low docking score and BBB permeability were selected for further analysis. The selected phytochemicals are 3-oxorhazinilam, isorauhimbine, and vinburnine.
Physicochemical properties of selected phytochemicals
The physicochemical properties of the selected phytochemicals were analyzed to determine the fate of the compounds and aid in evaluating their safety profiles. The physiochemical properties of the selected phytochemicals were assessed via the SWISS ADME server. The results of the assessment are summarized in Table 2. According to the results obtained from SWISS ADME, all the selected phytochemicals had favorable outcomes. A comparative representation of the observed values to the standard values of the physiochemical parameters is depicted in Figure 3.
The optimal range for each property is represented by the pink area in the radar plot: XLOGP3 for lipophilicity should be between -0.7 and +5.0; the molecular weight for size should fall between 150 and 500 g/mol; the TPSA should be between 20 and 130 Å2; solubility (logS) should not exceed 6; the fraction of carbons in the Sp3 hybridization for saturation should be at least 0.25; and the number of rotatable bonds for flexibility should not exceed 9. From the comparative radar plot and the data in Table 2, we conclude that the selected phytochemicals exhibit favorable physiochemical properties in terms of the parameters considered.
ADME/T analysis of selected phytochemicals
ADME/T was performed via the Deep-PK and ProTox 3.0 servers. The results of the ADME/T analysis for the Deep-PK server are detailed in Table 3. The ADME/T profile of the selected phytochemicals reveals information about their pharmacokinetics and toxicological profile, which are crucial in the drug design domain. All three compounds exhibit good human oral bioavailability and high probabilities of intestinal absorption, indicating effective absorption when administered orally. Their ability to penetrate the BBB is advantageous for treating brain-related disorders. However, differences in plasma protein binding percentages (53.65 for 3-oxorhazinilam, 7.34 for isorauhimbine, and 40.1 for vinburnine) could influence the distribution and efficacy of these compounds. Higher binding can limit the free drug available for therapeutic action, although it can also prolong the drug’s presence in the bloodstream. The metabolic profiles are variable, with 3-oxorhazinilam being an inhibitor of CYP 2C19 and CYP 2C9 and sorauhimbine and vinburnine inhibiting CYP 2D6. All three compounds are substrates for multiple CYP enzymes, affecting their metabolic stability and potential for drug–drug interactions. Effective drug candidates should ideally have balanced metabolism to avoid rapid degradation or accumulation. The clearance rates (4.31 for 3-oxorhazinilam, 11.96 for ssorauhimbine, and 8.36 for vinburnine) and predicted half-lives of less than 3 hours suggest that these compounds are cleared relatively quickly from the body. Rapid clearance reduces the risk of toxicity. The safety profile is prominent for all three compounds, as prediction indicates that they are nonmutagenic, noncarcinogenic, and safe for the eyes and avian species. A favorable toxicity profile is essential for any drug candidate. The results of the ADME/T analysis revealed that all three phytochemicals have potential as drug candidates.
The human-related toxicity profiles of these phytochemicals were further analyzed via the ProTox 3.0 server. ProTox 3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance [66].
The result yielded by the server is illustrated in the form of a radar plot (Figure 4). Different criteria such as organ toxicity, toxicity end points, Tox21 Nuclear receptor signalling pathways, Tox21 stress response pathways, molecular initiating events, and metabolism were taken into consideration. Remarkably, none of these phytochemicals are potentially toxic to human health as per the in silico analysis.
Drug-likeness analysis
The Lipinski rule of five was used as a benchmark to evaluate the drug likeness of the selected phytochemicals. The results of the analysis are depicted in Figure 5. The observed values of parameters such as molecular weight, logP (lipophilicity), hydrogen bond donors, hydrogen bond acceptors, and molar refractivity are plotted along with their ranges. Optimum range for a compound to be considered as drug candidate are as follows: the molecular mass of the compound should be less than 500 Da, the value of logP (lipophilicity) should be less than 5, hydrogen bond donor and acceptor value should be less than 5 and 10, respectively, while molar refractivity value should lie between 40 to 130 [67]. The values of the aforementioned parameters for all three phytochemicals fall within the defined range and hence follow Lipinski’s rule of five. We conclude that these selected ligands are drug candidates.
Biological activity of phytochemicals
The “GPCR ligand” properties of the selected phytochemicals were evaluated via the MolInspiration server, as the target protein belongs to the GPCR family. The activity scores were classified as follows: active (bioactivity score >0), moderately active (bioactivity score between -5.0 and 0.0), and inactive (bioactivity score <-5.0). The results of the evaluation are given in Figure 6. All the selected phytochemicals exhibited GPCR ligand properties, and their bioactivity scores were greater than 0.
MD simulation
MD simulation provides detailed insight into the stability of the receptor–ligand complex. The MD simulation, which spanned 50 ns, yielded the RMSD, Rg, and SASA trajectories of the complexes. These trajectories were used to assess the compactness and stability of the complexes. Figure 7 depicts the RMSD trajectory of the phytochemicals with respect to the target protein. The RMSD value of the isorauhimbine-5HT2AR complex was the lowest, whereas the highest RMSD value was exhibited by 3-oxorhazinilam. Although the RMSD value of 3-oxorhazinilam was relatively high, the fluctuation in the RMSD trajectory was uniform and acceptable as the RMSD value is less than 4 angstrom (0.4 nm) [68]. A low RMSD value and fluctuations in its trajectory signifies greater stability of the complex [69]. Thus, we conclude that vinburnine and isorauhimbine are more stable than 3-oxorhazinilam and hence could serve as potential 5HT2AR antagonists.
Furthermore, the Rg trajectory was studied to analyze the compactness of 5HT2AR in the presence of the selected phytochemicals. A lower Rg value signifies a more compact structure and vice versa. Figure 8 depicts the Rg trajectory of the protein structure in the presence of selected phytochemicals. The Rg values of vin-burnine and isorauhimbine were the lowest, whereas that of 3-oxorhazinilam was greater. Thus, from the Rg trajectory, we can conclude that the complexes of vinburnine and isorauhimbine with the target structure were more compact, implying their antagonist potential against 5HT2AR.
The SASA trajectory of the complexes was analyzed to evaluate the surface area of the protein. An increase in the SASA value indicates an increase in the surface area of the protein, whereas a decrease in the SASA value implies a reduction in its volume. All the complexes exhibited similar patterns in the SASA trajectory, as depicted in Figure 9.
Normal mode analysis
NMA provided insight into the structural dynamics and stability of the complexes (Figure 10). All three selected complexes with subtle differences generally exhibit low deformability. This indicates the overall rigidity of the complexes. Isorauhimbine shows deformability mostly below 0.6, with some peaks reaching 0.8, whereas both 3-oxorhazinilam and vinburnine have deformability mostly below 0.4. However, vinburnine exhibited sharp peaks at 1.0, suggesting that it has the most localized flexibility among the three samples. The B-factor plots for all complexes demonstrate a significant correlation between the experimental (PDB) and computed (NMA) values, validating the analysis. Analysis of the eigenvalues revealed striking differences. 3-Oxorhazinilam has the lowest first eigenvalue (7.850119e-05), indicating that it requires the least energy for its easy deformation. In contrast, vinburnine and isorauhimbine have similar, higher first eigenvalues (4.087177e-04 and 4.09726e-04, respectively), suggesting that they need more energy for their initial deformation and hence are more stable than 3-oxorhazinilam. The variance analysis further demonstrated the complexes. The first mode of 3-oxorhazinilam contributes more than 50% of the overall variance, which indicates dominant large-scale motion. With the first mode accounting for approximately 25%, vinburnine exhibited significant contributions from the first few modes. Moreover, isorauhimbine displays a more gradual increase in cumulative variance across the modes. Despite these differences in flexibility and dominant motions, all three complexes exhibited organized interaction networks, as depicted by their elastic network and covariance maps. These maps revealed distinct patterns of correlated motions, which suggested that all complexes maintain structured organizations crucial for their functions. Comparatively, 3-oxorhazinilam appeared to be the most dynamic, as it displayed a very low first eigenvalue and highly dominant first mode, suggesting that it might undergo large-scale conformational changes more easily than the other compounds. Isorauhimbine and vinburnine show more similar profiles to each other, with higher first eigenvalues and more distributed variance across modes, indicating their stability.
Visualization of interactions
2D and 3D schematic representations of the interactions be-tween phytochemicals and the active site of the protein were generated via Discovery Studio 2021 and PyMOL 3.0. Figure 11 schematically shows the interactions between the selected phytochemicals used as ligands and the active site of the target protein. Furthermore, Table 4 provides detailed insight into the amino acid interactions of active proteins and the types of interactions they exhibit.
DISCUSSION
Insomnia is a debilitating sleep disorder that has adverse effects on overall quality of life. In this study, we undertook a computational approach to delineate the therapeutic potential of R. serpentina against insomnia. The 5HT2AR was chosen as a therapeutic target, and the antagonistic properties of phytochemicals present in R. serpentina were evaluated. Molecular docking and molecular dynamics simulations were performed to assess the binding affinity and stability of the receptor–ligand complex, respectively. Normal mode analysis provided an in-depth comprehension of the dynamic nature of the complexes. Furthermore, the BBB permeability of phytochemicals was evaluated to elucidate their potential as drug candidates for the treatment of brain-related disorders. ADME/T analysis was performed to analyze the preclinical efficacy and safety of the phytochemicals. Molecular docking study delineated those compounds from R. serpentina which showed strong binding affinity with the target protein. The compounds were further assessed for their BBB permeability as it is a crucial parameter for those drug candidates which are used in treatment of brain related disorders. Furthermore, top three compounds on the basis of low docking score and also having BBB permeability were selected for further analysis. The physiochemical properties of top three compounds namely 3-oxorhazinilam, vinburnine, and isorauhimbine was assessed which is crucial parameter for evaluation of safety profile of the drug compounds. Moreover, ADME/T analysis of these compounds provided an insight into their pharmacokinetic and toxicological profile which are important in the domain of drug designing. Lipinski rule of five parameter was employed to analyze drug-likeness property of these compounds. It incorporates parameters such as molecular mass, logP, number of hydrogen bond donor, number of hydrogen bond acceptor, and molar refractivity. A compound must fall within the defined range of these parameters in order to be considered as a drug candidate. Subsequently, the biological activity of the compounds was assessed to confirm their property of being a GPCR ligand. The target protein, that is 5HT2AR, belongs to GPCR family, and hence, the compounds must be a ligand to that protein in order to exhibited binding affinity. Finally, MD simulation was performed for 50 ns to analyze the binding stability of the compounds with the target protein. MD simulation yielded RMSD, Rg, and SASA trajectories which provide comprehensive insight into protein-ligand stability. By analyzing the cumulative results of the aforementioned parameters, we conclude that isorauhimbine and vinburnine are potential antagonists of 5HT2AR and are concurrently favorable drug candidates for the treatment of insomnia.
The study utilised computational tools for analyzing the therapeutic potential the compounds in context of insomnia. Further testing via in vitro and in vivo experiments may validate the efficacy and potency of these compounds as therapeutics for treatment of insomnia.
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