Liver X receptor alpha (LXRα) is a nuclear receptor family member that is expressed in the liver, and has gained pharmacological interest for its role in the reduction of atherosclerosis, a long-term inflammatory disorder that affects the artery wall and is characterized by alterations in lipid metabolism. LXRα activators appear to be promising targets for preventing and/or treating these pathological conditions. The current work intends to perform a computational analysis of the IMPPAT database with LXRα. This comprehensive study will serve as a foundation for the investigation of novel plant-derived LXRα agonists against atherosclerosis. LXRα (PDB ID: 3IPU) was used as a target molecule for docking analysis. The docking scores of top five phytocompounds from the IMPAAT database, namely Betaxanthin, Adlumidiceine, Miraxanthin III S, Lupinisoflavone E, and p-Coumaroyl triacetic acid, ranged from - 35.88 kcal/mol to - 32.46 kcal/mol. The docking analysis was validated further by a 100 ns molecular dynamics study, which revealed fewer fluctuations in RMSD along with stable intermolecular interactions of lead phytocompounds with the targeted LXRα protein. The enrichment calculation metrics also validated the efficiency of the docking procedure. As evidenced by the MM-GBSA study, the binding free energies (ΔGbind) score up to -71.219 kcal/mol, with no violations in terms of drug likeliness and ADMET predictions, indicated the promising nature of these phytocompounds as drug candidates against atherosclerosis. These in silico studies, in particular, aid in analyzing phytocompounds roles as potential lead drugs, paving the way for in vitro and in vivo assessments for the development of novel anti-atherosclerosis drugs.
Research Article
Screening of potential phytocompounds against Atherosclerosis by directing Liver X Receptor alpha (LXRα): A computational manoeuvre
https://doi.org/10.21203/rs.3.rs-3129030/v1
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Atherosclerosis
Liver X Receptor alpha
IMPPAT database
Computational docking
Molecular simulation
ADMET.
- LXRα is important in the attenuation of various process underlying Atherosclerosis plaque progression.
- Computational method to screen IMPAAT phytocompounds database as potential LXRα agonist.
- The phytocompounds: Betaxanthin, Adlumidiceine, Miraxanthin III S, Lupinisoflavone E and p- Coumaroyl triacetic acid have high binding affinities towards LXRα protein.
- The study incorporates a novel approach towards atherosclerosis management using Liver X Receptor targeted therapeutics aided by computational analysis tool.
According to WHO coronary heart disease, cerebrovascular disease, atherosclerosis, rheumatic heart disease, and other heart and blood vessel illnesses are all classified as cardiovascular diseases (CVDs) which are the prime source of death globally, killing a predicted 17.9 million people every year. Recognizing people at high risk for CVDs and assuring that they receive appropriate treatment can help to reduce premature deaths (WHO, 2022). Atherosclerosis is characterized by chronic inflammation followed by focal narrowing of the arterial lumen due to inflammatory cells deposition and cholesterol followed by the proliferation of smooth muscle cells in the arterial wall. The condition is particularized by the arteries supplying less blood to the heart muscle, which can lead to heart failure or death. The establishment of an atherosclerotic plaque and its progression to acute coronary syndromes are aided by immune dysregulation and inflammation (Ross et al. 1999; Fradera et al., 2010).
The mechanism underlying the presence and progression of atherosclerosis remains a source of fierce scientific interest. A recent study investigated the effect of lipid-lowering interventions on atherosclerotic disease had a significant implication for understanding the role of intensive lipid lowering therapies in reducing cardiovascular risk, especially atherosclerosis (Di Giovanni et al., 2023). Several studies in this direction assist in establishing this nascent field. In this context, among the nuclear hormone receptors, the liver X receptors has surfaced as vital drug target. The nuclear receptor family of ligand-dependent transcription factors, includes the LXRs that regulate gene expression that control lipid and cholesterol metabolism (Jaye et al., 2003). The Liver X receptors which are nuclear receptors that sense cholesterol are crucial controllers of lipid homeostasis (Im et al., 2011).
As a part of superfamily of transcription factors, the nuclear hormone receptors act as mediators of hormonal and nutritional stimuli that are activated by ligands. To exert their effect, the nuclear hormone receptors bind to regulatory areas of target genes to activate or repress gene expression, which is necessary for nearly all phases of development, reproduction, cell growth, metabolic activity, immune function, and inflammatory response (El-Gendy et al., 2018; Jakobsson et al., 2012). LXR are attractive therapeutic targets since they are essential regulators of metabolism and ligand dependent transcription factors. LXR agonists have the potential to be used as cholesterol-lowering drugs and as treatments for atherosclerosis (Xu et al., 2021). Initially in mid 1990s, the LXR were identified as an orphan nuclear receptor which existed in two isoforms: LXRα and LXRβ. LXRα has 447 amino acids and is mainly expressed in the liver in humans, while LXR β has 460 amino acids and is expressed in nearly all tissues and organs. (She et al., 2022). Encouraging evidence shows a strong association between LXR and its interference with atherosclerosis progression (Michael et al., 2012).
Various observational studies in experimental animal models demonstrated activation of LXR to prevent the development of atherosclerosis (Joseph et al., 2002; Naik et al., 2006; Jaye et al., 2003). Subsequent in silico approaches studied reinforced the theme of Liver X receptor targeted therapeutics (Savla et al., 2022; Dhanalakshmi et al., 2015).
A strategy for correcting lipid metabolism disturbances is extensively adopted to target atherosclerosis. Statins are the most commonly used anti-atherosclerotic medications because they display multiple anti-atherosclerotic properties. A prolonged administration of this group, however, may increase the risk of undesirable side effects, particularly in patients with multiple medical co-morbidities (Ramkumar et al., 2016).
Several studies have indicated the use of plant extract in the drug discovery process. Plant-derived phytocompounds have long been thought to be a source of non-toxic bioactive compounds. (Cazarolli et al., 2008; Savla et al., 2022). Pharmacological compounds derived from medicinal plants are an encouraging source of bioactive agents and hit compounds for drug formulation (Hiebl et al., 2018). Medicinal plant derived phytocompounds/formulations display fewer side effects and more safety, consequently, they can be considered as one of the prospective anti-atherosclerotic potent treatments (Kirichenko et al., 2020).
A number of lines of evidence suggest plant alkaloid known as berberine purified from Cortidis rhizoma reduced atherosclerosis by controlling many aspects of the disease progression activity by enhancing LXRα (Xing et al., 2021; Chi et al., 2014; Lee et al., 2010). Additional investigators demonstrated punicalagin, purified from pomegranate peels polyphenols increased the expression of LXRα receptor in a dose dependent manner in HL 7702 steatosis hepatic cell model highlighting the lipid lowering effect of pomegranate peels (Zhao et al., 2014). Study by Singh et al., 2021, demonstrated a similar role of curcumin, a natural polyphenolic compound present in Curcuma longa L rhizomes, as a potential anti-atherosclerotic agent. In addition, several phytocompounds have been investigated by in-silico analysis for their anti-atherosclerotic potential (Corchorus aestuans leaves and Solanum melongena peel), using LXR (Dhanalakshmi et al., 2015; Suresh Kumar et al., 2021). Luo et al., 2022 led researchers to a better understanding of the phytochemical saponins potential in atherosclerosis prevention and treatment. Similar studies showed the ability of total saponins of garlic in the prevention and treatment of atherosclerosis (Miao et al., 2020). In their study, Hesari and coworkers (2021) has described the various naturally occurring phytoconstituents having preventive effects of atherosclerosis. In this regard, information on the molecular structures of phytocompounds (from IMPPAT data base) offers an opportunity to identify lead molecules against atherosclerosis by regulating the LXRα.
Molecular docking, and in silico analysis, assists in predicting the binding modalities and molecular interactions of compounds in contact with a specific protein receptor. The methodology paves the way for ranking the lead compounds on a hierarchy specified by certain score system. Computational modelling thus has the potential to be used to generate new approaches to augment the existing treatment methods within the short time period and are less expensive. Moreover, estimating the pharmacokinetics (such as absorption, distribution, metabolism, excretion, and toxicity) characteristics of the promising drug compound is a significant challenge in the drug development process. Thus, targeting atherosclerosis possibly by the unique strategy of modifying LXRα by phytocompounds would give rise to more alternative strategies in the drug development process. Screening of potential phytocompounds is essential for the optimization of drugs properties and selection of better candidates which can be incorporated in clinical implication. Therefore, the present study focuses on the in silico analysis using molecular docking, molecular dynamics, ADMET prediction, free energy, and enrichment calculations of phytocompounds from IMPAAT database against LXRα in order to find out promising lead molecules to regulate atherosclerosis.
2.1. In silico preparation of LXRα crystal structure
In this study, we select LXRα protein, one of the LXR subtypes belonging to the nuclear receptor superfamily, which regulates inflammatory processes, cancer, fatty acid metabolism, and central regulators of cholesterol homeostasis in humans. LXRα is predominantly expressed in the intestine, macrophages, adipose, and liver cells. As a result, because it governs cholesterol homeostasis, LXRα is considered to be an excellent mark for pharmaceutical agents aiming to improve cholesterol levels. The crystal structure of the LXR domain was retrieved from RCSB-PDB (Protein Data Bank) database (PDB ID: 3IPU) with 2.40 Å resolution .The LXRα domain comprises, two homodimer chains (A and B) with a sequence length of 446 amino acids (Fradera et al., 2010). By employing Protein Preparation Wizard from the Schrodinger Software, Chain A was prepared and used for further examination. Using the OPLS-2005(Optimized Potential for Liquid Simulations) force field, the crystal structure and side chain atoms of LXRα were further optimised (Lokhande et al., 2022a).
2.2. Phytocompounds from IMPPAT database
The Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) database is a library of Indian herb phytocompounds. Through the details in IMPPAT, we created a ligand library of 14,011 phytocompounds for this study (Mohanraj et al., 2018). The potential drug-like molecules in the library of 14,011 phytocompounds were then screened using Lipinski's rule of five (RO5), a widely utilized drug-like filter. The drug-like filter RO5 has resulted 8,450 drug-like phytocompounds. We then used the IMPPAT database to retrieve the three-dimensional Structure Data File (3D SDF) structure of these filtered phytocompounds. The IMPPAT phytocompounds, were energetically optimized using the Maestro software by applying the OPLS-2005 force field to get energetically stable confirmation of each ligand (Verma et al., 2021). The FlexX of Lead IT software was employed to execute molecular docking of these stable and refined phytomolecules with LXRα.
2.3. Molecular docking calculation
Ligand-fit is a computational molecular docking analysis for accurately docking selected molecules/ligands into targeted protein-binding catalytic sites and also for HTHS (high-throughput hit screening). During molecular docking analysis, the targeted protein LXRα (PDB ID: 3IPU) are treated as rigid structure while docking calculations. FlexX software generated protein preparation wizard file was used to define the binding cavity in the LXRα. To identify the binding cavity of LXRα, the residues which are within 6.5 Å of co-crystal ligand at its crystalline location in PDB ID: 3IPU are selected. Further, to forecast the binding poses of the phytocompounds, all 8,450 phytocompounds were docked into the binding cavity site of the LXRα. The computational docking initiates with the phytocompounds conformational expansion, followed by its insertion in the LXRα receptor site. An alpha triangle was employed to construct a collection of poses from a pool of docked phytocompounds conformations, yielding various potential conformations depending on the quantity of rotatable bonds present in the LXRα molecular structure. The docking score, interacting residues, binding interaction, and bond distance determined the optimal poses for a particular docked phytocompounds within the catalytic domain of the LXRα.
2.4. In silico ADMET property calculation
All lead compounds are subjected to preclinical and clinical studies in which their ADMET (absorption, distribution, metabolism, excretion, and toxicity) characteristics are assessed therapeutically against targeted proteins. When choosing a novel therapeutic drug candidate, the pharmaceutical kinetics and drug-likeness characteristics of the lead drug chosen must be addressed (Kar & Leszczynski, 2020). By employing pkCSM (http://biosig.unimelb.edu.au/pkcsm/prediction) and SwissADME (http://www.swissadme.ch/) web server, the pharmaceutical kinetics and drug-likeness of IMPAAT lead phytocompounds were computed (Pires et al., 2015; Daina et al., 2017). This helps to prevent pharmacological drug rejection in clinical studies.
2.5. Molecular dynamics (MD) simulation analysis
After docking in the binding pocket of LXRα as shown by conformational dynamics, evaluate the binding strength of 3IPU crystal ligand and screened lead IMPAAT phytocompounds. Using Desmond computational tool, we performed100 ns full-scale molecular dynamic simulations for lead IMPAAT phytocompounds such as Betaxanthin, Adlumidiceine, Miraxanthin III S, Lupiniso flavone E and p-Coumaroyl tri-acetic acid in complex with the LXRα crystal structure (Bowers et al., 2006). The calculation of energies and the computation of LXRα residues mobility are supported by molecular dynamics simulations. The lead IMPAAT phytocompounds complexed with LXRα was placed along with TIP3P explicit (water) solvent model in a cubic box with periodic boundary conditions. The system's absolute charge was then neutralized by incorporating a Na+/Cl- counter.
The overall energy minimization was achieved using the steepest descent approach with an OPLS 2005 (Kollar & Frecer, 2017). MDS computation at 100 ns was carried out for every complex (LXRα complexed all five lead phytocompounds) at constant pressure, atomic number, and temperature ensemble. The computed system was equilibrated at 1 atm pressure (1 atm), and 300 K temperature with MTK (Martyna, Tobias and Klein) barostat, and Nose-Hoover chain thermostat process, respectively. The length of hydrogen atom bonds was restricted with M-SHAKE algorithm. The coulomb short-range interaction cut-off radius was kept at 9.0, and the equations of motion were incorporated using the leap-frog approach with a time step of 2 fs. Once the system has been equilibrated, the stable conformation trajectories are collected and evaluated in order to assess interaction stability. The first conformations of the LXRα crystal structure were compared to the conformational change of the C-α backbone. Furthermore, we used trajectories obtained during a 100 ns MD simulation to perform MD trajectory clustering analysis to establish the binding mode of the selected ligand within the binding pocket of LXRα (Lokhande et al., 2022a)
2.6. LXRα and lead IMPAAT phytocompounds binding free energy
The computational procedure MM/GBSA (Generalised Born Model and Solvent Accessibility) is often employed to assess the free energy of binding (ΔGbind) of prospective lead phytocompounds to their particular receptor protein, here LXRα. This examination is generally utilises receptor protein-ligand complex molecular dynamics simulations (MDS). Over the trajectories created from the 100 ns MD simulation, the DGbind is computed as the total of the entropic contributions, potential energy, solvation free energy, and gas-phase energy (Lokhande et al., 2022b). For this study the Schrodinger software Prime module was used (Jacobson et al., 2004). The free energy changes caused by ligand binding were calculated using the equation below.
ΔGbind = Gcomplex – (Gprotein + Gligand)
G = EMM + GSGB + GNP
Where, Gcomplex denotes the energy of the protein LXRα-phytocompound complex, Gprotein the energy of the LXRα protein receptor, and Gligand the energy of the unbound phytocompound. While EMM stands for molecular mechanics energies, GSGB is for SGB polar solvation tool, and GNP stands for non-polar solvation.
2.7. Enrichment Calculation
We used Schrodinger’s enrichment calculator to test the parameters that determine whether docking software can select active ligands over inactive ligands (decoys). Further these calculations assisted in the selection of active ligands in top percentage of ranked databases. In our study, Schrodinger based standard decoy were used. Furthermore, the receiver operating characteristics ROC was used for calculating area under curve or enrichment factor was used to perform the enrichment calculation. When compared to a random or by chance ligand molecule selection, the enrichment calculation is a critical benchmark for predicting the reliability of scoring and molecular docking. (Carregal et al., 2017). The following equation has been used to define the enrichment factor (EF).
Where,
‘a’ = number of actives found in sample size ‘n’,
‘A’ = total number of active compounds in the database,
‘N’ = total number of phytocompounds used for enrichment calculations (decoys and actives).
In this investigation, we have generated 1000 decoys by providing smiles of the lead phytocompounds to the DUD.E database for LXRα (Mysinger et al., 2012). From the receiver operating characteristic (ROC) curve study, the area under the curve (AUC) computed which specifies the probability of replicating the (TP) true-positive results before (FP) false-positive results (Carregal et al., 2017). The perfect AUC range is 0–1, with a value near ≤ 0.5 implies that the protocol chooses the TP and FP at random, and a value close to 1 suggests that the TP results are more likely to be replicated before the FP results.
3.1. The LXRα.
Previous research by Huang et al., 2010, has shown the LXRα LBD generally as 12 helices organized around an innermost hydrophobic pocket. The NCBI CD search of LXRα receptor revealed its homodimeric form with chain A & B. The structural components of LXR α (PDB ID: 3IPU) has been illustrated utilizing Maestro software. The Fig. 1 shows Chain A crystal structure of LXRα with natural ligand oxysterol O40. The catalytically site of the ligand binding domain shows amino acid Arg232, Leu260, Phe257, Ala261, Ser264, Met298, Ile295, Thr302, Arg305, Phe315, Phe326, Phe335, and His421 are actively involved in the binding pocket as per the PDBsum database (http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/) analysis.
3.2. Molecular docking analysis
Out of the 8450 phytomolecules from IMPAAT data base, 7472 phytocompounds were successfully docked with LXRα with the docking score range − 35.884 kcal/mol to + 7.7067 kcal/mol. The docking energy of the promising phytocompounds from the IMPAAT database into the LXR crystal structure's binding cavity (PDB ID: 3IPU) is summarized in Table 1 by using Maestro software. According to molecular docking experiments, Betaxanthin exhibits a high binding affinity (-35.884 kcal/mol) for the LXRα target.
| Phytocompounds | PubChem CID | Docking Score | Interacting Residues | Bond Type | Bond Distance (Å) |
|---|---|---|---|---|---|
| Betaxanthin | 25245127 | -35.884 | Phe 257 | HB | 1.78 |
| Meth298 | HB | 2.27 | |||
| Arg305 | 3 HB | 2.26, 1.97, 1.96 | |||
| Arg305 | 2 Salt-Bridge | 2.79, 4.76 | |||
| Phe257 | Aromatic- HB | 2.63 | |||
| Adlumidiceine | 30777492 | -34.110 | Arg232 | 2 Pi-cation | 3.16, 5.83 |
| Arg305 | 3 HB | 1.86, 1.96, 2.28 | |||
| Arg305 | Salt-Bridge | 3.54 | |||
| Arg305 | 2 Pi-cation | 3.63, 3.64 | |||
| Lys317 | HB | 2.60 | |||
| Lys317 | Salt-Bridge | 3.94 | |||
| Miraxanthin III S | 441627 | -33.659 | Arg232 | Salt-Bridge | 4.46 |
| Arg232 | Pi-cation | 4.18 | |||
| Arg305 | 3 HB | 1.85, 2.20, 2.43 | |||
| Arg305 | 2 Salt-Bridge | 3.18, 4.46 | |||
| Phe315 | HB | 1.82 | |||
| Lupinisoflavone E | 93373-44-5 | -32.884 | Arg232 | Pi-cation | 3.50 |
| Met298 | HB | 1.83 | |||
| Arg305 | HB | 1.71 | |||
| Phe315 | HB | 1.86 | |||
| p-Coumaroyl Tri-acetic acid | 25245921 | -32.465 | Arg232 | HB | 2.19 |
| Glu267 | HB | 2.01 | |||
| Arg305 | 3 HB | 1. 71, 2.12, 2.15 | |||
| Arg305 | Salt-Bridge | 3.27 | |||
| Phe315 | HB | 2.05 | |||
| Ser264 | Aromatic- HB | 2.35 | |||
| O40 | Co crystal ligand of LXRα | -16.535 | Arg232 | Pi-cation | 3.43 |
| Arg305 | Pi-cation | 3.16 | |||
| Phe257 | HB | 1.95 |
Figure 2 shows the first lead phytocompound Betaxanthin has high binding affinity creating five hydrogen bonds, one aromatic hydrogen bond and two salt bridges with LXRα catalytic site. It manifests three hydrogen bonds with Arg305 residue at 2.26 Å, 1.97 Å and 1.96 Å bond distances. Also, Phe257 and Meth298 are involved in the formation of two hydrogen bonds at 1.78 Å and 2.27 Å bond distance, respectively. Arg305 participates in formation of two salt-bridges at 2.79 Å and 4.76 Å bond distance with Betaxanthin. Phe257 participates in formation of aromatic hydrogen bond with Betaxanthin with a bond length of 2.63 Å.
Similarly, for Aldumidiceine binding affinity towards LXRα was observed to be -34.110 kcal/mol (Fig. 3). The binding pattern displayed formation of four hydrogen bonds, four Pi-cation bonds, and two salt bridges with the LXRα catalytical domain. Arg232 forms two Pi-cation bonds at 3.16 Å and 5.83 Å bond distance with Adlumidiceine. Arg305 participates in formation of three hydrogen bonds at 1.86 Å, 1.96 Å and 2.28 Å bond length respectively. Arg305 again forms a salt bridge with Adlumidiceine at 3.54 Å bond length. Also, Arg305 forms two Pi-cation bonds with Adlumidiceine at 3.63 Å and 3.64 Å bond length respectively. Lys317 forms one hydrogen bond at 2.06 Å bond distance and it also forms one salt-bridge at 3.94 Å bond distance with the said lead compound.
In the case of Miraxanthin III S, the binding affinity towards LXRα was noted as -33.659 kcal/mol. The analysis of intermolecular interactions is displayed in Table 1. Figure 4, shows the binding pose of Miraxanthin III S with LXRα binding pocket through four hydrogen bonds, three salt bridges and one Pi-cation interaction. Arg305 forms three hydrogen bonds at 1.85 Å, 2.20 Å and 2.43 Å bond distances, respectively. Arg305 also forms two salt bridges at 3.18 Å and 4.46 Å, respectively with Miraxanthin III S. Arg 232 forms one salt bridge at 4.46 Å bond distance. Also, Arg 232 forms one Pi-cation bond at 4.18 Å bond distance with the said lead compound. Amino acid Phe315 forms one hydrogen bond at 1.82 Å bond distance.
Similarly, in Fig. 5, lead compound Lupinisoflavone E was observed to bind with LXRα with docking energy of -32.8846 kcal/mol. Residue Met298 forms one hydrogen bond at 1.83 Å bond distance with the phytocompound. Arg305 forms one hydrogen bond at 1.71 Å bond distance. Similarly, Phe315 forms one hydrogen bond with 1.86 Å bond distance. However, Arg232 forms one Pi-cation bond at 3.50 Å bond distance.
Figure 6, displays the binding type of p-Coumaroyl triacetic acid within the LXRα catalytic cavity. This lead phyocompound forms six hydrogen bonds, one aromatic hydrogen bond and one salt bridge with a -32.465 kcal/mol docking score. The Arg232 residue from LXRα pocket interacts with one hydrogen bond at 2.19 Å bond distance. Glu267 forms one hydrogen bond at 2.01 Å bond length. Similarly, Arg305 forms three hydrogen bonds at 1.71 Å, 2. 12 Å and 2.15 Å bond length respectively. Residue Arg 305 forms one salt bridge at 3.27 Å bond distance and Phe315 participates in formation of one hydrogen bond at 2.05 Å bond distance. Similarly, Ser 264 forms one aromatic hydrogen bond at 2.35 Å bond distance.
Based on the docking analysis, it is evident that the selected lead phytocompounds have a potential binding affinity for LXRα and that the following amino acid residues: Phe257, Phe315, Met298, Arg232, Arg305, Lys317, Glu267, and Ser264 are the essential interacting residues. Our results are consistent with pervious work of Fradera et al., 2010 with similar active amino acids in binding pocket of LXRα. As such the lead compounds that have been reported have higher binding affinity than their endogenous co crystal (in-bound) ligand Oxysterol (O40).(Fig. 7)
3.3. Post molecular docking enrichment calculations
Docking study methodologies determine the 3D orientation of bound molecules within a protein receptor's binding pocket cavity. To ensure the sensitivity and specificity of docking analysis result, enrichment calculations were carried out using decoy molecules. To completely eradicate false-positive docking analysis results, enrichment calculations were performed using a set of decoy molecules (1000 compounds) from the D.UDE database. To determine the enrichment factor, the top 20 docking compounds were combined with 1000 decoy sets and re-docked against the LXR using the FlexX technique. Enrichment analysis revealed that 20 compounds out of the total actives were retrieved in the case of the LXR i.e., 100 percent of the top 5% of decoys produced remarkable ROC values (R2 = 0.98) Fig. 8. This docking validation protocol indicates that the FlexX docking protocol used to retrieve the active phytocompounds is satisfactory as the ROC value greater than 0.9 is considered excellent (Hevener et al., 2009). The ROC curve shows that the method used to identify active compounds was precise and sensitive, and that the lead compounds from the study can be used to develop new medications. The validation factor like receiver operation curve (ROC) results are shown in Table 2.
| Receptors | Enrichment calculations | Count and percentage of actives in top N% of decoy results | ROC (R2) | ||||
| % Decoys | 1% | 2% | 5% | 10% | 20% | ||
| LXR | # Actives | 5 | 10 | 20 | 20 | 20 | 0.98 |
| % Actives | 25.0 | 50.0 | 100.0 | 100.0 | 100.0 | ||
3.4. In silico ADME prediction
In silico prediction of molecular physic-chemical parameters, bioavailability & pharmacokinetics is becoming increasingly relevant in the search of promising therapeutics. During the drug molecule synthesis process, adjusting the balance between drug similarity and ADMET can assist in the prevention of drug candidate failure later in the clinical trial process. (Kar et al., 2020). The estimated ADMET properties of selected phytocompounds are outlined in Table 3.
| Property | Model Name | Predicted Value | ||||
| Phytocompounds | ||||||
| Betaxanthine | Adlumidiceine | Miraxanthin III S | Lupiniso flavone E | p-Coumaroyl triacetic acid. | ||
| Absorption | Water solubility [Numeric (log mol/L)] | -2.359 | -3.738 | -3.738 | -4.337 | -2.119 |
| Caco-2 permeability [Numeric (log Papp in 10− 6 cm/s)] | 0.155 | 0.34 | 0.348 | 1.213 | 0.14 | |
| Intestinal absorption (human) [Numeric (% Absorbed)] | 27.876 | 64.629 | 64.629 | 85.759 | 49.223 | |
| Skin Permeability [Numeric (log Kp)] | -2.735 | -2.734 | -2.734 | -2.737 | -2.825 | |
| Distribution | VDss (human) [Numeric (log L/kg)] | -0.664 | -0.966 | -0.966 | -0.115 | -0.656 |
| Fraction unbund (human) [Numeric (Fu)] | 0.382 | 0.211 | 0.211 | 0.007 | 0.36 | |
| BBB permeability [Numeric (log BB)] | -0.583 | -0.863 | -0.863 | -1.04 | -0.401 | |
| CNS permeability [Numeric (log PS)] | -3.152 | -3.582 | -3.582 | -3.337 | -3.11 | |
| Metabolism | CYP2D6 substrate [Categorical (Yes/No)] | No | No | No | No | No |
| CYP3A4 substrate [Categorical (Yes/No)] | No | No | No | Yes | No | |
| CYP1A2 inhibitor [Categorical (Yes/No)] | No | No | No | Yes | No | |
| CYP2C19 inhibitor [Categorical (Yes/No)] | No | No | No | Yes | No | |
| CYP2C9 inhibitor [Categorical (Yes/No)] | No | No | No | Yes | No | |
| CYP2D6 inhibitor [Categorical (Yes/No)] | No | No | No | Yes | No | |
| CYP3A4 inhibitor [Categorical (Yes/No)] | No | No | No | Yes | No | |
| Excretion | Total Clearance [Numeric (log ml/min/kg)] | 0.675 | 1.065 | 1.065 | -0.117 | 0.449 |
| Renal OCT2 substrate [Categorical (Yes/No)] | No | No | No | No | No | |
| Toxicity | AMES toxicity [Categorical (Yes/No)] | No | No | No | No | No |
| Max. tolerated dose (human) [Numeric (log mg/kg/day)] | 0.205 | 0.589 | 0.589 | -0.113 | 0.318 | |
| hERG I inhibitor [Categorical (Yes/No)] | No | No | No | No | No | |
| hERG II inhibitor [Categorical (Yes/No)] | No | No | No | Yes | No | |
| Oral Rat Chronic Toxicity [Numeric (log mg/kg, bw/day)] | 1.533 | 1.329 | 1.329 | 1.53 | 1.624 | |
| Skin Sensitization [Categorical (Yes/No)] | No | No | No | No | No | |
| Tetrahymena pyriformis toxicity [Numeric (log ug/L)] | 0.21 | 0.287 | 0.287 | 0.289 | 0.26 | |
| Minnow toxicity [Numeric (log mM)] | 1.275 | 1.004 | 1.004 | 1.334 | 0.46 | |
The determination of intestinal absorption, Caco-2 permeability, and skin permeability is an important parameter for determining the efficacy of a lead compound as an oral drug. According to the current study, the Caco-2 permeability for all selected compounds is positive with almost 50% intestinal absorption for maximum lead phytocompounds. The selected compound skin permeability is less than − 2.73 log kp implying poor skin permeability. Similarly, the dissemination of the selected lead phytocompound in the body was assessed using four different assays: BBB (Blood Brain Barrier) permeability, VDss (Volume of distribution), fraction unbound, and CNS (Central nervous system) permeability. The volume of distribution is determined by comparing the uniformity of drug distribution in the bloodstream to the total amount of medication. The drug’s dispersion is deemed acceptable if the values are greater than 0.45 log and poor if values are less than − 0.15 log (Prajapati et al., 2021) in our study all screened lead phytocompounds displayed low distribution volume. Each of the lead phyocompounds have logBB values less than 0.3, indicating that they are all satisfactory candidates since they are unable to cross the BBB. The Cytochrome models have been utilized to predict the metabolism of lead compounds such as CYP2D6, CYP3A4, CYPIA2, CYP2CI9, CYP2C9, CYP2D6 and CYP3A4, as these enzymes play a major role in the metabolism of many drug molecules. All the selected lead phytocompounds are inactive for inhibition by above mentioned cytochrome models. In addition, these phytocompounds displayed the total clearance rate greater than 0.449 logml/min/kg, and neither of them functioned as OCT2 (Organic cation transporter) substrate. Also, all of the lead compounds that were tested were neither mutagenic nor carcinogenic, as they did not exhibit AMES toxicity, or skin sensitization (Lipinski et al., 2001). We can infer from these ADMET experiments that the lead phytocompounds that passed the screening process have the potential to be promising pharmaceutical candidates.
Furthermore, the drug likeness (Lipinski rule of five) of the selected compounds were calculated using the SWISS ADMET online tool Table 4, in which all the compounds were found to have good pharmacokinetic profile and all they met drug likeness (Daina et al., 2017).
| Phytocompounds | Mol Wt | Mol Formula | Log P | HBA | HBD | nRB | tPSA | MR | NOV |
|---|---|---|---|---|---|---|---|---|---|
| Betaxanthin(CID: 25245127) | 358.35 | C18H18N2O6 | 1.48 | 7 | 2 | 8 | 134.11 | 94.4 | 0 |
| Adlumidiceine(CID: 30777492) | 399.39 | C21H21NO7 | 2.41 | 7 | 1 | 7 | 98.56 | 101.81 | 0 |
| Miraxanthin III S (441627) | 328.32 | C17H16N2O5 | 1.31 | 6 | 2 | 7 | 124.88 | 87.91 | 0 |
| Lupinisoflavone E(CID: 93373-44-5) | 438.47 | C25H26O7 | 4.1 | 7 | 3 | 3 | 109.36 | 119.87 | 0 |
| p-Coumaroyl triacetic acid (CID: 25245921) | 289.26 | C15H13O6 | 1.36 | 6 | 2 | 7 | 114.73 | 73.53 | 0 |
| Mol Wt: Molecular Weight (≤ 500 Da); Mol Formula: Molecular Formula; Log P: The octanol/water partition coefficient (≤ 5); HBA: Hydrogen bond acceptor (≤ 10); HBD: Hydrogen Bond Donor (≤ 5); nRB: Number of Rotatable Bonds (≤ 10); tPSA: Topological polar surface area (≤ 150 Ų); MR: Molar refractivity (≤ 155) and NOV: Number of Violations | |||||||||
3.5. Dynamic behavior of phytocompounds with LXRα.
The protein crystal structure used in the molecular docking calculations were assumed to be rigid. Using molecular dynamic simulation, the kinetic nature of the receptor's active site (LXR) and selected hit phytocompounds associations were investigated for the strength of bond configuration after coupling of lead phytocompounds inside the catalytic cavity to achieve more confirm nature of interactions occurring in the binding pocket (Hollingsworth et al., 2018; Durrant et al., 2011). To accomplish this, LXR and docked complexes were solvated in an explicit (TIP3 water model) PBC box for 100 ns using the FlexX protein-ligand docking calculations. Table 5.
| Compound Name | #TIP3P molecules | Total Charge of the system | #Counter Ions | Concentration of Counter Ions [µM] |
|---|---|---|---|---|
| Betaxanthin | 9745 | -6 | 6 Na+ | 11.195 |
| Adlumidiceine | 9763 | -4 | 4 Na+ | 7.449 |
| Miraxanthin III S | 9748 | -6 | 6 Na+ | 11.191 |
| Lupinisoflavone E | 9756 | -4 | 4 Na+ | 7.455 |
| p-Coumaroyl triacetic acid | 9757 | -5 | 5 Na+ | 9.317 |
This analysis used a duration of 100ns because it allows enough time for C-α atom rearrangements in LXRα clusters containing lead phytocompounds. Three statistical parameters were calculated to reveal complex stability: The Root Mean Square (RMSD), Root Mean Square Fluctuations (RMSF) and Hydrogen bond monitoring. The RMSD and RMSF parameters are important as they offer details about the overall structural rigidity and structural adaptability of C-α atom throughout the MD simulation (Lokhande et al., 2021). The RMSD and RMSF parameters of the C-α atom from LXRα were calculated to show thermodynamic conformational stability at thermodynamic level for 100 ns. Furthermore, 1000 trajectories generated during the simulation were captured and then overlapped on the preliminary crystal structures (docked pose) of LXRα using Schrodinger Simulation Event Analysis (SEA) panel, which produces an output dat in “.dat” format. The RMSF and RMSD results acquired were utilized to create RMSD and RMSF plots. (Fig. 9 and Fig. 10).
RMSD plot of LXR C-α atoms complexes with phytocompounds during 100ns timescale simulation shown in Fig. 9. This figure shows that during the 100ns MD simulation period, all selected phytocompounds and LXRα complex remained in equilibrium ranging from 1.5 Å to 3 Å RMDS. This signifies the extent to which each lead phytocompound is tightly bound within LXRα cavity.
From the RMSD graph (Fig. 9), it is clear that all the complex structures (except LXR_ p-Coumaroyl triacetic acid complex) began equilibrating from 10ns to 75ns within the 1.5 Å to 3 Å range of RMSD, offering an aggregated RMSD score of 1.5 Å. For LXR_ p-Coumaroyl triacetic acid complex, it started equilibrating from 30ns to 100ns. The RMSF values were calculated to investigate the consequence of phytocompounds on LXR C- atom compliance. Table 6 shows the average RMSF values of binding site residues. According to the RMSF graph (Fig. 10), the fluctuation in the LXR C-α atoms is minimal within the RMSF value 2.5 Å where it binds with the screened lead phytocompounds. The fluctuations in certain regions reach 3.5 Å, but the variations in the binding site region are very low, indicating a stable protein-ligand complex by phytocompounds and LXR throughout the entire 100ns MD simulation.
| Binding site residues | Betaxanthin | Adlumidiceine | Miraxanthin III S | Lupiniso flavone E | p-Coumaroyl tri-acetic acid |
|---|---|---|---|---|---|
| Phe257 | 0.668 | 0.917 | 0.921 | 0.69 | 0.709 |
| Phe 315 | 0.83 | 0.711 | 1.027 | 0.872 | 0.798 |
| Met298 | 0.518 | 0.568 | 0.642 | 0.556 | 0.579 |
| Arg232 | 0.912 | 1.043 | 1.341 | 0.951 | 1.068 |
| Arg305 | 0.654 | 0.565 | 0.672 | 0.769 | 0.672 |
| Lys317 | 1.176 | 1.307 | 1.392 | 1.483 | 1.316 |
| Glu267 | 0.669 | 0.725 | 0.78 | 0.656 | 0.734 |
| Ser264 | 0.626 | 0.981 | 0.948 | 0.816 | 0.856 |
3.6. Intermolecular H-bond surveillance during 100ns MD simulation
The affinity of the interaction between lead phytocompounds with LXRα protein was also evaluated using 1000 frames produced by a 100ns MD simulation timeline. The analysis was performed using simulation event analysis program of Maestro, which produced intermolecular H bonding trend.
Figure 11a depicts intermolecular H-bonding between Betaxanthin and LXRα formed more than nine hydrogen bonds in less than 10ns of MD simulation. Advancing further to 10ns, the mean number of hydrogen bonds generated by this complex is four. More stability is observed from 40ns onwards in the 100ns simulation. Figure 11b shows during 0 to 20 ns, the H-bonding between Adlumidiceine and LXRα is 10 bonds which increase till 80 ns. This combination establishes a total of 6 hydrogen bonds on average. The next lead compound, Miraxanthin III S shows a good H bonding pattern with the receptor in the initial stages of simulation Fig. 11c. It shows a stable pattern from 0 to 70 ns followed by comparatively lesser participation in H bounding. Intermolecular H bound between Lupinisoflavone E and receptor protein display stability after 50 ns till 100 ns simulation. The mean number of hydrogen bonds generated is seven, as shown in Fig. 11d. For the lead compound p-Coumaroyl triacetic acid, within the first 20ns of MD simulation, less interactions were noted, followed by a steady bounding pattern from 60ns to 100ns. The average number of H bond produced by this complex was three Fig. 11e.
3.7. Prime MM/GBSA binding free energies of LXR α-phytocompound complex
To validate the docking results, MM/GBSA studies are performed to calculate the binding free energies i.e. ΔGbind which yields the result in terms of solvation, vDW, hydrophobic and vDW components (Jacobson et al., 2004). The binding free energy is measured to better understand the docking stability of lead phytocompounds bound to the LXRα cavity. Strong, weak binders, as well as random molecules, can be distinguished using the MM/GBSA computations (Chen et al., 2020). Binding free energy (prime MM/GBSA) calculations were performed on docked phytocompounds complexes with LXR (PDB: 3IPU) for a 100ns time scale molecular dynamic simulation. Table 7 shows the computed energies of the screened five phytocompounds complexes with LXR.
| Compound Name | MMGBSA dG Bind (kcal/mol) | MMGBSA dG Bind Coulomb(kcal/mol) | MMGBSA dG Bind Solv GB (kcal/mol) | MMGBSA dG Bind VdW(kcal/mol) | Complex Energy (kcal/mol) |
|---|---|---|---|---|---|
| Betaxanthin | -39.985 ± 3.916 | 17.289 ± 6.966 | 4.919 ± 7.565 | -45.000 ± 1.138 | -7267.940 ± 3.579 |
| Adlumidiceine | -57.986 ± 5.068 | -27.646 ± 7.881 | 37.335 ± 6.235 | -49.765 ± 1.986 | -7225.880 ± 5.085 |
| Miraxanthin III S | -26.765 ± 9.999 | 5.262 ± 14.351 | 12.431 ± 14.22 | -33.540 ± 5.390 | -7179.530 ± 9.377 |
| Lupinisoflavone E | -71.219 ± 3.485 | -5.019 ± 4.441 | 20.710 ± 1.775 | -57.130 ± 0.798 | -7258.110 ± 2.650 |
| p-Coumaroyl triacetic acid | -46.171 ± 7.021 | -8.816 ± 4.672 | 17.826 ± 2.906 | -40.508 ± 3.627 | -7360.500 ± 5.816 |
Whenever the binding free energies are extra negative, it is assumed that the receptor-ligand complex is highly stable (Lokhande et al., 2021). The calculated binding free energy of Lupinisoflavone E with LXRα was − 71.219 ± 3.485 kcal/mol and the rest phytocompounds displayed less binding free energies than this score. The resulting ΔGbind score of Betaxanthin is -39.985 ± 3.916 kcal/mol, for Adlumidiceine it is -57.986 ± 5.068 kcal/mol, for Miraxanthin III S score is -26.765 ± 9.999 kcal/mol and p-Coumaroyl triacetic acid is -46.171 ± 7.021 kcal/mol respectively.
The results indicate that all the screened lead phytocompounds formed a stable interaction with the binding cavity of LXRα giving best docking poses. (Sun et al., 2014) The main energy source of the Gbind count, which enhances the intensity of the investigated phytocompounds' binding affinity for the active domain of the targeted LXRα protein, are hydrogen bond, Coulomb interaction, Vander Waals and Lipophilic energy. For simulated LXR-lead phytocompound complexes, various energy components including coulomb energy (G Bind Coulomb), covalent binding energy (G Bind Covalent), hydrogen bonding correction (G Bind Hbond), generalized born electrostatic solvation energy (G Bind Solv GB), lipophilic energy (G Bind Lipo), and van der waals energy (G Bind vdW) have been determined. Thus the findings indicate that the LXRα protein and lead phytocompounds possess thermodynamically favorable binding status.
Viable solutions for either preventative or treatment approaches seem important considering the prevalence of CVDs. Data from previous decades indicated that several herbal and natural medicines have the ability to treat CVDs while having fewer negative effects than chemical treatments (Hesari et al., 2021). Biological products and bioactive phytochemicals are considered to offer enhanced atherosclerosis treatment potential. Several studies have shown that diet high in plant-based foods are protective against a variety of cardiovascular diseases as phytocompounds present in them have an cholesterol level reducing effect (Maheshwari et al., 2020). Similar such role of various plant extracts as anti-atherosclerotic agent was investigated by Kirichenko et al., 2020, their study suggested effect of glabridin isolated from licorice (Glycyrrhiza. Glabra.L.) as potential anti-atherosclerotic phyto-agent. Medicinal plants treat atherosclerosis through a wide range of processes and action mechanisms. A variety of plants with prohylactic action for the treatment of atherosclerosis have already been identified.
In particular the health promoting effects of Betaxanthine in avoiding emergence of cardiovascular disease are documented in previous studies supplementing the findings and making it a very promising lead phytocompound for future studies (Milton-Laskibar et al., 2021; Sadowska-Bartosz et al., 2021). Moreover Adlumidiceine has been implied to have beneficial effects on the prevention and control of CVD (Li. J et al., 2022). Furthermore, Miraxanthin III S has been linked to variety of health benefits. (Gandía-Herrero et al., 2005). Collectively reports also indicated the assertive role of Lupinisoflavone E in traditional medicine. (Gerometta, E et al., 2020). Furthermore, as indicated by Boo et al., 2019, p-Coumaroyl triacetic acid has beneficial properties in human health augmentation. Plant flavonoids have been studied for their effects on atherosclerosis as well as anti-hypertensive properties (Salvamani et al., 2014; Zhang et al., 2020). Thus, natural plants and herbs can be used to make drugs that have fewer side effects and are more bioavailable (Tabassum et al., 2011). Consequently our in silico studies establishes the candidature of five phytocompounds as lead molecules for drug development process.
Atherosclerosis is a chronic inflammatory condition that affects the blood vessels. Inflammation progression involves numerous signaling pathways, which contributes to the high morbidity and mortality in patients. As dysregulation of cholesterol homeostasis may lead to atherosclerosis, extensive research is carried out to examine the role of LXRα in atherosclerosis. LXRα serve as master regulators of liver fat biotransformation in many ways. An LXRα agonist helps promote the mobility of deposited cholesterol from the periphery to the liver, where it is bio-transformed and discharged into the bile by coordinating the expression of target genes in multiple tissues. LXRs function as master regulators of hepatic lipid metabolism in many ways. LXRα agonists of plant origin could be useful for anti-atherosclerotic effects. As anti-atherosclerotic agents, medicinal plants have been explored extensively, our study emphasizes the possibility of interaction of five lead phytocompounds (selected from IMPPAT database) towards LXRα target protein as a potential interventional strategy for treating atherosclerosis. In particular it is shown from the results that the selected leads of plant origin obtained by computational screening namely: Betaxanthin, Adlumidiceine, Miraxanthin III S, Lupinisoflavone E and p-Coumaroyl triacetic acid have a thermodynamically favorable binding pattern toward LXRα target. The in silico ADMET results further highlight the lead compounds as gleaming, contemporary and potent in the treatment of atherosclerosis, which must be validated by further in vitro and in vivo assessment.
LXR: Liver X Receptor
IMPPAT: Indian Medicinal Plants, Phytochemistry and Therapeutic
ADMET: Absorption, Distribution, Metabolism, Excretion and Toxicity
LBD: Ligand binding domain
ROC: Receiver Operation Curve
Conflict of interest
The author declares no conflict of interest.
Consent statement/Ethical approval
Not required.
Authors contribution
Kiran Bharat Lokhande: Methodology, Resources, Software, Investigation, Writing-original draft, Writing-review and Editing
Sarika Vishnu Pawar: Methodology, Resources, Writing-original draft, Writing-review, Editing and Analysis.
Neelu Nawani: Writing-review and Editing.
Minal Wani: Writing-review and Editing.
Swamy K. Venkateswara: Writing-review and Editing
Payel Ghosh: Writing-review and Editing
Arti Abhijit Kale: Conceptualization, Methodology, Supervision, Writing-original draft, Writing-review, Editing and Analysis.
Acknowledgements:
The authors are thankful to Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y.Patil Vidyapeeth Tathawade, Pune, for providing the infrastructure and facilities to perform this study. The authors also acknowledge the DST-SERB, Govt. of India, New Delhi, (File Number: YSS/2015/002035) for Optimized Supercomputer facility for dynamics calculations. Dr. Kiran Bharat Lokhande acknowledges the ICMR (Indian Council of Medical Research), New Delhi, India for Senior Research Fellowship (Project ID: 2019-3458; File: ISRM/11(54)/2019).
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No competing interests reported.