{"id":61373,"date":"2024-09-30T11:38:21","date_gmt":"2024-09-30T11:38:21","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=61373"},"modified":"2024-10-09T17:58:21","modified_gmt":"2024-10-09T17:58:21","slug":"identification-of-novel-compounds-targeting-the-liver-x-receptor-lxr-in-silico-studies-screening-molecular-docking-and-chemico-pharmacokinetic-analysis","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no3\/identification-of-novel-compounds-targeting-the-liver-x-receptor-lxr-in-silico-studies-screening-molecular-docking-and-chemico-pharmacokinetic-analysis\/","title":{"rendered":"Identification of Novel Compounds Targeting the Liver X Receptor (LXR): In-silico Studies, Screening, Molecular Docking, and Chemico-pharmacokinetic Analysis"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

Liver\nX receptors (LXR), a member of the nuclear receptor family are important regulators of cholesterol, fatty\nacid, and glucose homeostasis.1<\/sup>\nTo date, two isoforms of LXRs have been discovered and are given the nuclear\nreceptor nomenclature symbols NR1H3 (LXR-\u03b1) and NR1H2 (LXR-\u03b2).1<\/sup> The well-established role of LXRs was\nconfirmed and reaffirmed by the published data on LXRs in gene-disease\nassociation, network, mRNA and protein expression in tissues.2-4<\/sup> LXRs have been shown to function as direct transcriptional regulators for\ngenes involved\nin cholesterol and lipid metabolism regulation, including\nATP Binding Cassette transporter (ABC), Apolipoprotein E (ApoE),\nCholEsterylester Transfer Protein (CETP),\nFatty Acid Synthase (FAS), cholesterol 7\u03b1-hydroxylase (CYP7A1), sterol\nregulatory element binding protein 1c (SREBP-1c), and stearoyl-CoA desaturase-1\n(SCD-1). There are reports LXRs activation increases hepatic VLDL production by 2.5-fold, and\nproduces large TG-rich VLDL particles in the liver.5-8<\/sup> LXR accelerates the\nconversion of cholesterol to bile acids, lowers the amount of cholesterol at the\ncellular level, and enhances reverse cholesterol transport to the liver.9<\/sup> Thus, evidences of links\nbetween LXR dysregulations and the onset of metabolic diseases (e.g.,\nhyperlipidemia,atherosclerosis)9,10<\/sup>\nattracted researchers to the innovation of ligands targeting LXRs.<\/p>\n\n\n\n

In recent years, several LXR agonists have been studied in preclinical trials with the intent to develop new drugs for atherosclerosis, diabetes, anti-inflammation, Alzheimer’s disease, and cancer.11-15<\/sup> Among these reported LXR agonists, T0901317, LXR-623 and GW3965 showed efficacy to reduce cholesterol levels not only in the serum but also in the liver of mice with various diseases.<\/a>15-18<\/sup> A recent study demonstrated that SR9243 reduces intrahepatic inflammation caused by nonalcoholic steatohepatitis and regulates lipid metabolism in cancer cells.19,20<\/sup> In animal models, GSK3987, an agonist of pan LXR-\u03b1\/\u03b2 with EC50s of 40\u201350 nM, decreases triglyceride buildup and increases cellular cholesterol efflux.21<\/sup> There are also LXR agonists that have been tested in clinical trials, including LXR-623, BMS-779788, BMS-852927, and AZ876.22-25<\/sup> Almost all the agonists have been abandoned owing to ineffectiveness or serious side effects in clinical trial.22-25<\/sup> There is an unmet need to develop the most suitable LXR-\u03b1\/\u03b2 selective agonists with fewer side effects, as none of these compounds pass the clinical trial due to ineffectiveness or substantial side effects.<\/p>\n\n\n\n

Molecular docking and\nmolecular dynamic (MD) simulation are two of the most popular, quick,\naffordable, and straightforward computational computer-assisted methods for\ndesigning or finding small molecules for drug development.26-31<\/sup> Docking study is often\nemployed in drug discovery to predict the binding affinity and binding mode of\na small molecule (ligand) with a target protein or nucleic acid.26-28,32,33<\/sup> While MD simulation provides\ninsights into the dynamical behavior of biomolecules including the motion of\natoms and molecules over time.31,34<\/sup>\nCombining these techniques provides a more comprehensive\nunderstanding of the ligand-receptor interaction, accounting for flexibility\nand dynamic changes. To confirm the understandings and determine the true\ntherapeutic potential of the research, a preclinical or clinical experimental\nendorsement is required. <\/p>\n\n\n\n

A web-based virtual\nscreening tool called SwissSimilarity (http:\/\/www.swisssimilarity.ch\/) allows\nquick screening of pharmaceuticals, bioactive small compounds, and commercially\navailable ligands from PubChem, the ZINC database, Drug Bank, and other\nsources.35<\/sup>  SwissSimilarity employs\nmolecular fingerprints and fast nonsuperpositional or superpositional 3D shape\nsimilarity techniques.35<\/sup> In\naddition to SwissSimilarity, another popular web tool for calculating\nphysicochemical attributes, pharmacokinetics assessment, drug-likeness, and\nmedicinal chemistry friendliness of small compounds is SwissADME\n(http:\/\/www.swissadme.ch\/).36<\/sup> Important characteristics of SwissADME are\nLipinski’s rule of five, certain pharmacokinetic factors to help with the early\nphases of the drug discovery process, and forecasts of properties of drug\nabsorption, distribution, metabolism, and excretion (ADME).37,38<\/sup><\/p>\n\n\n\n

Therefore, the goal of this\nwork was to find ligands that have the maximum affinity to the target LXRs by\nusing chemico-pharmacokinetic methods, molecular docking, virtual screening,\nin-silico research, and MD modelling. In addition to being underutilized in\nclinical practice at the moment, these ligands have less chance of side effects\nand may be used to treat cancer, atherosclerosis, and metabolic disorders.\nFurthermore, a thorough investigation was carried out using publically\naccessible databases to reevaluate the levels of mRNA and protein expression of\nLXR-\u03b1 and LXR-\u03b2 in both healthy and diseased tissues. This allowed for a\nrevision of the associations between these proteins and disorders.2-4,39,40<\/sup> Ligands with substantial\nbinding affinities and specificities for LXR can be found and considered as\nviable candidates for additional research by adjusting the interaction between\nthe drug candidate and the LXR target protein.<\/p>\n\n\n\n

Materials and Methods<\/strong><\/p>\n\n\n\n

In-silico study of mRNA and protein expression of LXRs<\/strong><\/p>\n\n\n\n

We performed an in-silico LXRs’ mRNA and protein expression study to learn more about their physiological function with gene IDs 10062 and 736 for LXR-\u03b1 and LXR-\u03b2, respectively. We curated the mRNA expression data for LXR-\u03b1 and LXR-\u03b2 from the HPA RNA-seq database (https:\/\/www.ncbi.nlm.nih.gov\/gene\/) of normal tissues.40<\/sup> The data are shown in Figure 1a. Additionally, we observed the protein level expression using data from the Human Protein Atlas (https:\/\/www.proteinatlas.org\/). 4,39<\/sup> Figure 1b displays the curated protein expression data for LXR-\u03b1 and LXR-\u03b2.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 1: mRNA and protein expression of LXRs in different tissues<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Gene-disease Association Analysis<\/strong><\/p>\n\n\n\n

To observe major diseases involving these genes, we performed gene-disease association (GDA) analysis for LXRs using the web-based platform DisGeNET v7.0 (https:\/\/www.disgenet.org\/), one of the largest publicly accessible collections of genes and variants linked to human diseases.3<\/sup> DisGeNET combines information from expert-curated repositories, GWAS catalogues, animal models, and scientific literature. DisGeNET employs a text-mining technique to prioritise the genotype-phenotype associations.3<\/sup> The GDA analysis data are shown in Figure 2a.<\/p>\n\n\n\n

Analysis of gene networks and pathways<\/strong><\/p>\n\n\n\n

To obtain further insight into the physiological and pathological significance of LXR expression, we conducted a protein-protein interaction analysis, specifically, we examined the interaction using IntAct (https:\/\/www.ebi.ac.uk\/intact\/). To address LXR involvement in biological\/physiological pathways, we performed pathway enrichment analysis using WebGestalt (https:\/\/www.webgestalt.org\/), a functional enrichment analysis web tool.2<\/sup> WebGestalt follows well-established and complementary methods for enrichment analysis, including overrepresentation analysis.2<\/sup><\/p>\n\n\n\n

Target protein <\/em>identification and selection<\/em><\/strong><\/p>\n\n\n\n

Clinical data has shown that LXRs play a\nsignificant role in controlling glucose, fatty acid, and cholesterol\nhomeostasis. Additionally, LXR agonists have been shown to be successful in\ntreating mouse models of cancer, anti-inflammation, atherosclerosis, and\ndiabetes. The aforementioned proteins are highly essential in the overall\nmanagement of atherosclerosis, diabetes, anti-inflammation, Alzheimer’s\ndisease, and cancer due to the complexity of LXRs’ role in the pathophysiology\nand the promising outcomes of using LXR agonists. They are also suitable for in\nsilico studies. Therefore, the target proteins for the current molecular\ndocking investigation are LXR-\u03b1 and LXR-\u03b2, whose PDB IDs are 1uhl and 5ajy,\nrespectively. Ligands chosen according to their affinity for these structures\nhave been redocked for both target LXRs.<\/p>\n\n\n\n

Ligand preparation for docking<\/strong><\/p>\n\n\n\n

Chemical ligands were identified through a\nliterature search: T0901317, AZ876, BMS-779788, BMS-852927, GSK3987, LXR-623,\nSR9243, GW3965, 24S-hydroxycholesterol, 24R-hydroxycholesterol, and\n5,6-epoxycholesterol.18-20,22,25,41,42<\/sup> Using MarvinSketch, a\nChemAxon’s desktop programmes for chemistry programme for sketching and\nvisualising chemical structures, the structures were reprocessed in PDB or Mol2\nformat. We added hydrogen atoms for the compounds lacking hydrogen\natoms and defined rotatable bonds that will be used for flexible docking. The\nligands obtained from a database screening were selected for\ncreating a new structure using ChemAxon software. We cleaned the ligand\nstructure by removing any unwanted atoms or molecules, such as solvent\nmolecules or counter ions. Generated the 3D coordinates for the ligands using\nthe same software or web tool. The ligand structures are optimized by minimizing\nthe energy using a molecular mechanics force field. We saved the ligand\nstructure in a format that is compatible with the docking software, such as PDB\nor Mol2 format.<\/p>\n\n\n\n

Target protein preparation for Docking<\/strong><\/p>\n\n\n\n

The\ntarget protein sequencefor LXR-\u03b1 (1uhl) and LXR-\u03b2 (5jy3) was obtained\nfrom the PDB online platform (https:\/\/www.rcsb.org\/). Then we selected and\ndeleted the ligands and water (LXR-\u03b1\/\u03b2-drug) from the complex using PyMOL2.5\nsoftware,43,44<\/sup> because\nligand-free and water-free cocrystallized 3D protein structure is needed for molecular docking assay.45<\/sup> A\nmolecular insertion study was conducted to\nidentify the binding sites between the target protein and different chelating\nagents. We saved the target protein structure in a format that is\ncompatible with the docking software, such as PDB format. In\norder to identify the binding pockets in the chosen target proteins where the\nligands are most likely to bind with stable free energy, we used the web\napplications 3DligandSite and COACH-D.46,47<\/sup><\/p>\n\n\n\n

Screening of compound libraries<\/strong><\/p>\n\n\n\n

For LXR-\u03b1 and LXR-\u03b2, ligands with comparable chemical structures and therapeutic potential were screened using the online tool SwissSimilarity (http:\/\/www.swisssimilarity.ch\/). SwissSimilarity allows screening a wide range of compound libraries such as DrugBank, ChEMBL, LigandExpo, ZINC, and many more.35<\/sup> The SMILES format was used to submit the query molecule. These ligands were identified using the chemical structures of the compounds that showed the highest affinity for LXR-\u03b1 and LXR-\u03b2 during docking tests. The ZINC provided the ligands in SDF format for download.48<\/sup> The format was changed using OpenBabelGUI 3.0.1 to be compatible with AutoDock 4 and AutoDock Vina. The rotatable bonds were then identified and the required charges were added.45<\/sup><\/p>\n\n\n\n

Molecular Docking Analysis<\/strong><\/p>\n\n\n\n

We\nconducted molecular docking analysis using PyRx (https:\/\/pyrx.sourceforge.io\/),\na virtual suite programme for Computational Drug Discovery.49<\/sup> PyRx is built with many established\nopen source software including AutoDockTools to generate input files,\npython as a programming\/scripting language, wxPython for\ncross-platform GUI, the Visualization ToolKit, Enthought Tool Suite including\nTraits for application building blocks, Open Babel for importing SDF files\nand removing salts and energy minimization, matplotlib for 2D plotting,\nAutoDock 4 and AutoDock Vina for docking.49<\/sup> We uploaded the ligand and protein files to the PyRx\ninterface as mol2 and pdb files, respectively. Protein and ligand files were\nagain cleaned and the structures processed to be devoid of unlikeliness such as\nmissing atoms, protonation states, and water molecule removal before running\nthe docking analysis. Here, we used Open Babel to create a pdbqt file\n(protein.pdbqt, ligand.pdbqt) for the ligand and protein files. To facilitate\nthe docking calculations a 3D grid around the target binding site in the\nreceptor is created using AutoGrid. The grid box dimensions were set x,y,z\ndimension, and the center grid box coordinates x, y, z center, respectively. In\nthis study, the grid box dimensions were set x,y,z as 55, 55, 55 \u00c5, and the coordinates of center x, y, and z\nwere 44.53, -2.4883, and 21.7037 \u00c5,\nrespectively.<\/p>\n\n\n\n

The binding affinity between the ligand and\nreceptor is estimated by the Autodock Vina docking algorithm. The Autodock Vina\nuses scores each ligand poses and ranks the poses to identify potential binding\ncandidates. The scoring function in AutoDock Vina calculates\nintermolecular interactions such as van der Waals forces, hydrogen bonding,\nsteric and electrostatic interactions between the ligand and the protein, and\nthe desolvation energy. Once the calculations are\ndone, results will be populated as seen in the below table with the Binding\nAffinity (kcal\/mol) values. The more negative the numerical values for the binding affinity, the\nbetter the predicted binding between a ligand and a protein. In the context of docking\nevaluation, the RMSD is computed relative to the native ligand to ascertain how\nwell the anticipated posture approaches the crystallographic pose. When it\ncomes to computational molecular docking, algorithms are considered legitimate\nand dependable if they generate poses with RMSD values less than 2 \u00c5, where\nRMSD is determined to the native ligand. A lower RMSD value indicates better\naccuracy of the docking technique and a better fit between the anticipated and\nexperimental positions49<\/sup>. A validation of the results is performed\nusing experimental data or by comparing with known binding modes. Docking results are\nvisualized into 2D\nand 3D images by Pymol and Biovia Discovery\nStudio, both are powerful molecular visualization tools and are used for\ndocking analysis to visualize protein-ligand interactions. <\/p>\n\n\n\n

Validation of docking method <\/strong><\/p>\n\n\n\n

A commonly used strategy is called “pose\nselection,” which entails re-docking a chemical with a known conformation\nand orientation\u2014usually from a co-crystal structure\u2014into the target’s active\nsite using docking algorithms. When programmes can return poses with a RMSD\nvalue from the known conformation\u2014which, depending on ligand size, is\nfrequently 1.5 or less than 2 \u00c5\u2014they are considered effective.<\/p>\n\n\n\n

Chemico-Pharmacokinetic Profile Predictions<\/strong><\/p>\n\n\n\n

Using\nthe online technology provided by SWISSADME (http:\/\/www.swissadme.ch\/), we\nevaluated the pharmacokinetics, drug-likeness, and medicinal chemistry\ncompatibility of small molecules that were identified as the most promising\ncandidates for LXR-\u03b1 and LXR-\u03b2 through molecular docking and virtual screening.\nThe ligands were filtered based on Lipinski’s “Rule of Five,” which\nencompasses criteria such as molecular weight (MW) being less than 500, log P\nbeing less than 5, the number of hydrogen bond donors being less than 5, and\nthe number of hydrogen bond acceptors being less than 10. Additionally, we\nemployed established ADME pharmacokinetics prediction methods, including\nassessment of aqueous solubility (PlogS), blood\/brain permeability (PlogBB),\nintestinal barrier permeability (logHIA), cell permeability (PCaco-2),\nsubstrate\/non-inhibitor status (logPgp), as well as cell permeability (LogPapp)\nand CYP inhibition, to identify the most suitable ligands for LXR-\u03b1 and LXR-\u03b2 targeting. The query molecules were submitted in SMILES format.<\/p>\n\n\n\n

Molecular dynamics simulation<\/strong><\/p>\n\n\n\n

Further assessment of the binding stabilities\nof these putative LXR-\u03b1\/\u03b2-agonists is done using molecular dynamics simulation\nwith QwikMD and NAMD (https:\/\/www.ks.uiuc.edu\/Research\/qwikmd\/). Using the\nSwissParam-generated ligand force field and the CHARMM general force field for\nproteins, the structure of the ligand\u2013receptor complex in the optimal docking\nposition was simulated. The simulation was run with an implicit solvent model\nand a salt concentration of 0.15 M. We present the results of a\n50 ns production simulation of the ligand\u2013LXR-\u03b1\/\u03b2 complexes using the VMD1.9.4a53 toolset. To\nanalyze the results of the simulation in QwikMD, in the simulation setup, we\nload the corresponding .qwikmd file. The pop-up menu provides the option to\nload any generated trajectory (e.g., equilibration, production, etc.<\/em>),\nfrom which we select the \u201cproduction\u201d option and specify a trajectory frame\nstep. For equilibration\nsimulation we set a temperature of 60 K to 300 K at 1.242-second frequency and\nproduction simulation we set a constant temperature of 300 K. The 2.5 ps\nfrequency at which the simulation results were saved was used. During the\nexamination of the simulations, RMSF and RMSD values for the ligand\u2013LXR-\u03b1\/\u03b2\ncomplexes were documented. To ensure greater precision MD simulation was\nrepeated twice for each complex, and the average outcome was employed for\nanalysis. We also determine the nonbond interaction energy (kcal\/mol) between the ligand and protein. Frame 0\nserves as the reference frame in RMSD analysis, and each frame’s structure is\naligned with frame 0’s to measure real structural fluctuations that occur\nthroughout the simulation.<\/p>\n\n\n\n

Results<\/strong><\/p>\n\n\n\n

Many physiological processes and disorders are regulated by LXRs, as evidenced by mRNA and protein expression, gene-disease association, network, and pathway enrichment analysis.<\/strong><\/p>\n\n\n\n

Using the publicly accessible RNA-seq databases (https:\/\/www.ncbi.nlm.nih.gov\/gene\/) and the human protein atlas (https:\/\/www.proteinatlas.org\/), we initially looked at both mRNA and protein expression across organs.4,39,40<\/sup> LXR-\u03b1 expression is restricted to the liver, kidney, gut, fat tissue, lung, and spleen and is predominantly detected in fat. LXR-\u03b2 is expressed in almost all tissues and organs (Figure 1a, b). While LXR-\u03b1\/\u03b2 show overlap in a number of tissues, their tissue distribution patterns diverge greatly. The divergent expression patterns suggest that LXR-\u03b1\/\u03b2 has different functions in regulating physiological processes. Based on DISGENET’s gene-disease association analysis, LXR-\u03b1 has been associated with several metabolic disorders, including atherosclerosis, coronary heart disease, metabolic syndrome, type 2 diabetes, and coronary hyperlipidemia.3<\/sup> LXR-\u03b2 has also been connected to a number of metabolic diseases as well as cancer (Figure 2a).<\/p>\n\n\n\n

Based on network analysis, it was projected that LXRs physically associate or directly interact with many proteins such as transporters (PPARA), transcription factors (RXRG, RXRA, RXRB, NCOR1), enzymes (KDM1A, SUV39H1), and other essential proteins (EDF1, MDFI, CORO2A) (Figure 2b). It is commonly recognised that these transporters, transcription factors, and enzymes regulate lipids and cholesterol. WebGestalt’s (https:\/\/www.webgestalt.org\/) pathway enrichment study also identified the main pathways implicated in LXRs. Nuclear receptors in lipid metabolism and toxicity, cholesterol and lipid homeostasis, cholesterol-derived oxysterols, the PPAR alpha route, the development of white fat cells, and the PPAR signalling pathway are among the pathways (Figure 2c). All of these findings point to the possibility that small molecules can interfere in atherosclerosis through pharmacological LXR activation.<\/p>\n\n\n\n\n\n
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Figure 2: Gene-disease association, networks and pathway analysis for LXRs<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Molecular docking analysis targeting LXR-\u03b1 for the selected ligands shows distinct binding affinities and patterns of interaction with amino acids <\/strong><\/p>\n\n\n\n

The\n3D structure of the ligands was obtained from the literature search. Both the\nligands and active sites of the protein were prepared carefully to meet the\nrequirements of the docking software. Our docking analysis results demonstrated\nthe binding affinity of ligands within the binding pocket and the interacting\namino acid for AZ876,\nBMS-779788, BMS-852927, GSK3987, GW3965, LXR-623, SR9243, T0901317,\n24(S)-hydroxycholesterol and 22(R)-hydroxycholesterol (Figure 3 and Table 1).\nOut of all the ligands investigated, LXR-623 had the highest binding affinity\nfor LXR-\u03b1, indicating the most potential for a robust interaction with LXR-\u03b1. <\/p>\n\n\n\n

Our results showed the interacting amino acids for\nT0901317 were GLU308, VAL311, GLN313, SER383, ARG387,\nILE389, and LYS435 while 24R-hydroxycholesterol, having the lowest docking score (-6.5\nkcal\/mol), showed interaction with ARG387, ILE389, ARG443. The ARG387 and GLN313 illustrated\ntraditional hydrogen bonds, halogen (fluorine) bonds were represented by\nGLU308, SER383, and VAL311, and Pi-cation and Pi-anion interactions were shown\nby LYS435 in the LXR-\u03b1-T0901317 complex within 4 \u00c5. The ILE389 demonstrated\nPi-alkyl interaction, while the GLU312, HIS386, LEU383, and ASP390 displayed\nvander waals interaction in the LXR-\u03b1-T0901317 complex. <\/p>\n\n\n\n

AZ876 interacts with THR292, ARG369, PRO370, ASN371, ARG415, TRP443, and ASP444 and has an affinity of \u22128.5 kcal\/mol for LXR-\u03b1. BMS-779788 and BMS-852927 interact with PHE229, LEU260, VAL263, SER264, ASP318 ILE370, and ARG373, ALA374, LYS452, ARG497, LEU501, LEU504, PHE503, ALA528, with binding affinities of \u2212 8.3 and 8.2 kcal\/mol, respectively. GSK3987 binds to SER366, VAL386, GLU387, HIS390, PHE404, LEU408, LEU411, and ARG415. Its binding affinity is \u22127.4 kcal\/mol. GW3965 interacted with amino acids ARG226, PHE229, VAL263, ARG305, and TYR306, with an affinity of -7.0 kcal\/mol. Although LXR-623 had a \u221210.6 kcal\/mol affinity for LXR-\u03b1, it also binds to LYS452, ARG497, GLU378, ALA374, ASN377, MET525 and ARG373. <\/p>\n\n\n\n\n\n
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Figure 3. Binding models of LXR\u2013\u03b1 agonist complexes.<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 1: The binding affinity of ligands within the binding pocket and the amino acids that interact with LXR-\u03b1. <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Ligand<\/strong><\/p>\n<\/td>\n

\n

Binding Affinity (Kcal\/mol)<\/strong><\/p>\n<\/td>\n

\n

rmsd\/ub<\/strong><\/p>\n<\/td>\n

\n

rmsd\/lb<\/strong><\/p>\n<\/td>\n

\n

Interacting amino acid<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

LXR-623<\/p>\n<\/td>\n

\n

-10.2<\/p>\n<\/td>\n

\n

2.153<\/p>\n<\/td>\n

\n

1.402<\/p>\n<\/td>\n

\n

GLU308, VAL311, GLN313, SER383, ARG387, ILE389, LYS435<\/p>\n<\/td>\n<\/tr>\n

\n

AZ876<\/p>\n<\/td>\n

\n

-8.5<\/p>\n<\/td>\n

\n

5.52<\/p>\n<\/td>\n

\n

2.891<\/p>\n<\/td>\n

\n

ILE370, ARG373, ALA374, TRP376, LYS452, LEU504, PHE508<\/p>\n<\/td>\n<\/tr>\n

\n

BMS-779788<\/p>\n<\/td>\n

\n

-8.3<\/p>\n<\/td>\n

\n

1.746<\/p>\n<\/td>\n

\n

1.312<\/p>\n<\/td>\n

\n

GLU308, GLU312, HIS386, ARG387, ILE389, LEU396, LYS435<\/p>\n<\/td>\n<\/tr>\n

\n

BMS-852927<\/p>\n<\/td>\n

\n

-8.2<\/p>\n<\/td>\n

\n

30.743<\/p>\n<\/td>\n

\n

26.208<\/p>\n<\/td>\n

\n

ILE370, ARG373, ALA374, LYS452, ARG497, LEU501, LEU504, PHE503, ALA528<\/p>\n<\/td>\n<\/tr>\n

\n

GSK3987<\/p>\n<\/td>\n

\n

-7.4<\/p>\n<\/td>\n

\n

8.99<\/p>\n<\/td>\n

\n

3.51<\/p>\n<\/td>\n

\n

ILE370, ARG373, ALA374, GLU378, LEU501, LEU504, ALA528<\/p>\n<\/td>\n<\/tr>\n

\n

T0901317<\/p>\n<\/td>\n

\n

-7.3<\/p>\n<\/td>\n

\n

4.489<\/p>\n<\/td>\n

\n

2.429<\/p>\n<\/td>\n

\n

ARG373, ALA374, ASN377, GLU378, LYS452, ARG497, LEU501, LEU504, MET525<\/p>\n<\/td>\n<\/tr>\n

\n

SR9243<\/p>\n<\/td>\n

\n

-7.2<\/p>\n<\/td>\n

\n

10.006<\/p>\n<\/td>\n

\n

3.63<\/p>\n<\/td>\n

\n

GLU312, HIS386, ARG387, ILE389, LEU396, LYS435, LEU438<\/p>\n<\/td>\n<\/tr>\n

\n

GW3965<\/p>\n<\/td>\n

\n

-7.0<\/p>\n<\/td>\n

\n

4.685<\/p>\n<\/td>\n

\n

2.761<\/p>\n<\/td>\n

\n

ARG373, ALA374, ASP450, ARG497, LEU504<\/p>\n<\/td>\n<\/tr>\n

\n

24S-hydroxycholesterol<\/p>\n<\/td>\n

\n

-6.9<\/p>\n<\/td>\n

\n

5.959<\/p>\n<\/td>\n

\n

4.397<\/p>\n<\/td>\n

\n

ILE445, TYR468, LEU490, LEU493, PRO494<\/p>\n<\/td>\n<\/tr>\n

\n

24R-hydroxycholesterol<\/p>\n<\/td>\n

\n

-6.5<\/p>\n<\/td>\n

\n

2.06<\/p>\n<\/td>\n

\n

1.466<\/p>\n<\/td>\n

\n

ARG387, ILE389, ARG443<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

RMSD (root mean square deviation), rmsd\/lb (root mean square deviation\/upper bound), rmsd\/lb(root mean square deviation\/lower bound) are calculated by the AutoDock Vina of PyRx tool (total runs=9).<\/p>\n\n\n

Molecular docking analysis targeting LXR-\u03b2 for the selected compounds shows distinct binding affinities and patterns of interaction with amino acids <\/strong><\/p>\n\n\n\n

LXR-\u03b2 complexed with BMS-852927 (PDB\nID 5jy3) was redocked, binding affinities and the RMSD were computed for AZ876,\nBMS-779788, GSK3987, GW3965, LXR-623, SR9243, T0901317,\n24(S)-hydroxycholesterol\nand 22(R)-hydroxycholesterol, following a similar protocol to that for LXR-\u03b1. Table\n2 displays the affinities for LXR-\u03b2 that all of the chosen compounds have shown. Of the ligands that were\nstudied, AZ876 interacts with the amino acids PHE268, PHE271, THR271, LEU274,\nALA275, SER278 MET312, LEU313, GLU315, THR316, ARG319, PHE329, PHE340, LEU345,\nPHE349, ILE 350, ILE353, HIS435 and had the highest affinity of \u221210.8 kcal\/mol\nfor LXR-\u03b2 (Figure\n3, Table 2). The interacting residues\ninvolved in the formation of vander waals forces, hydrogen\nbond, pi-sigma, pi-sulfur, amide-pi-stacked, alkyl, and pi-alkyl\ninteractions in the LXR-\u03b2-AZ876 complex within 2.4 \u00c5. Although\nvan der Waals force was demonstrated by SER278 LEU313, GLU315, THR316, PHE340,\nPHE349, and ILE350, PHE271 showed traditional hydrogen bonds; LEU274\nrepresented the carbon-hydrogen bond; alkyl and pi-alkyl were demonstrated by\nPHE268, ALA275, PHE329, LEU345, ILE353, HIS435, pi-sigma by ILE353, and\npi-sulfur by ARG313, MET312. <\/p>\n\n\n\n

The compound BMS-779788,\nwhich interacts with PHE271, ALA275, ILE309, MET312, PHE329, PHE340, LEU345,\nand HIS435; and BMS-852927, which interacts with PHE271, ALA275, LYS305,\nILE309, MET312, PHE329, PHE340, LEU345, HIS435, and SER436; both have lower\naffinity than SR9243, 22R-hydroxycholesterol, GW3965, LXR-623, T0901317 and\nGSK3987 (Table 2). GSK3987 has a binding affinity of -9.8 kcal\/mol and binds to the ALA275,\nLEU345, and HIS435.\nGW3965 and LXR-623 had a similar affinity of -9.8 kcal\/mol, despite the\ndifferences in the amino acid interactions. In LXR-623, the amino acids that\ninteracted were PHE268, LEU274, ALA275, LEU345, ILE353, and HIS435, whereas in\nGW3965, they were PHE271, LEU274, ALA275, MET312, PHE329, PHE340, LEU345, and\nHIS435. GSK3987 has a binding affinity of -9.8 kcal\/mol and\nbinds to the ALA275, LEU345, and HIS435. <\/p>\n\n\n\n

SR9243 and T0901317 bind to the amino acid PHE268, SER274, ALA275, ILE309, MET312, LEU313, PHE329, LEU330, LEU345 and ARG319, THR316, GLU281, PHE329, LEU330, LEU345, ILE353, with affinities of -10.3 and -9.7 kcal\/mol, respectively. Despite being isomers, 24R- and 24S-hydroxycholesterol displayed varying levels of binding affinity. The binding affinity of 24R- and 24S-hydroxycholesterol were -9.8 and 8.1 kcal\/mol, respectively. In relation to 24R-hydroxycholesterol, the interacting amino acids were LEU274, ALA275, SER278, PHE 329, LEU345, and HIS435; in contrast, PHE268, ALA275, MET312, PHE329, LEU274, LEU345, ILE353 and HIS435 were associated with 24S-hydroxycholesterol.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 4: Binding models of LXR\u2013\u03b2 agonist complexes.<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 2: The binding affinity of ligands within the binding pocket and the amino acids that interact with LXR-\u03b2. <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Ligand<\/strong><\/p>\n<\/td>\n

\n

Binding Affinity (Kcal\/mol)<\/strong><\/p>\n<\/td>\n

\n

rmsd\/ub<\/strong><\/p>\n<\/td>\n

\n

rmsd\/lb<\/strong><\/p>\n<\/td>\n

\n

Interacting amino acid<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

AZ876<\/p>\n<\/td>\n

\n

-10.8<\/p>\n<\/td>\n

\n

1.625<\/p>\n<\/td>\n

\n

1.734<\/p>\n<\/td>\n

\n

PHE268, PHE271, LEU274, ALA275, MET312, PHE 329, LEU345, ILE353, HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

SR9243<\/p>\n<\/td>\n

\n

-10.3<\/p>\n<\/td>\n

\n

2.122<\/p>\n<\/td>\n

\n

1.466<\/p>\n<\/td>\n

\n

PHE268, SER274, ALA275, ILE309, MET312, LEU313, PHE329, LEU330, LEU345<\/p>\n<\/td>\n<\/tr>\n

\n

22R-hydroxycholesterol<\/p>\n<\/td>\n

\n

-9.8<\/p>\n<\/td>\n

\n

1.731<\/p>\n<\/td>\n

\n

1.857<\/p>\n<\/td>\n

\n

LEU274, ALA275, SER278, PHE 329, LEU345, HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

GW3965<\/p>\n<\/td>\n

\n

-9.8<\/p>\n<\/td>\n

\n

2.702<\/p>\n<\/td>\n

\n

1.642<\/p>\n<\/td>\n

\n

PHE271, LEU274, ALA275, MET312, PHE329, PHE340, LEU345, HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

LXR-623<\/p>\n<\/td>\n

\n

-9.8<\/p>\n<\/td>\n

\n

1.387<\/p>\n<\/td>\n

\n

1.766<\/p>\n<\/td>\n

\n

PHE268, LEU274, ALA275, LEU345, ILE353, HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

T0901317<\/p>\n<\/td>\n

\n

-9.7<\/p>\n<\/td>\n

\n

3.589<\/p>\n<\/td>\n

\n

2.528<\/p>\n<\/td>\n

\n

PHE272, SER274, ALA275, GLU281, MET312, THR316, ARG319, PHE329, LEU330, LEU345, ILE353<\/p>\n<\/td>\n<\/tr>\n

\n

GSK3987<\/p>\n<\/td>\n

\n

-9.2<\/p>\n<\/td>\n

\n

3.57<\/p>\n<\/td>\n

\n

2.39<\/p>\n<\/td>\n

\n

ALA275, LEU345, HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

BMS-779788<\/p>\n<\/td>\n

\n

-9.0<\/p>\n<\/td>\n

\n

2.213<\/p>\n<\/td>\n

\n

2.81<\/p>\n<\/td>\n

\n

PHE271, ALA275, ILE309, MET312, PHE329, PHE340, LEU345, HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

BMS-852927<\/p>\n<\/td>\n

\n

-8.9<\/p>\n<\/td>\n

\n

3.09<\/p>\n<\/td>\n

\n

2.356<\/p>\n<\/td>\n

\n

PHE271, ALA275, LYS305, ILE309, MET312, PHE329, PHE340, LEU345, HIS435, SER436<\/p>\n<\/td>\n<\/tr>\n

\n

24S-hydroxycholesterol<\/p>\n<\/td>\n

\n

-8.1<\/p>\n<\/td>\n

\n

2.714<\/p>\n<\/td>\n

\n

2.472<\/p>\n<\/td>\n

\n

PHE268, LEU274, ALA275, MET312, PHE329, LEU345, ILE353, HIS435<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

RMSD (root mean square deviation), rmsd\/lb (root mean square deviation\/upper bound), rmsd\/lb(root mean square deviation\/lower bound) are calculated by the AutoDock Vina of PyRx tool (total runs=9).<\/p>\n\n\n

Utilization of virtual screening to find new compounds that target LXR-\u03b1 and LXR-\u03b2<\/strong><\/p>\n\n\n\n

Using the SwissSimilarity\n(http:\/\/www.swisssimilarity.ch\/) online platform, a virtual search was done in\nthe ZINC online database to find new compounds that target LXR-\u03b1 and LXR-\u03b2. SwissSimilarity provides a diverse range of\nsmall molecule databases that can be used for screening purposes. These\ndatabases encompass drugs and clinical candidates, bioactive compounds,\ncommercially available compounds, and synthesizable molecules. Various\nmolecular fingerprints\/vectors, which can either represent the 2D molecular\nstructure or the 3D conformation of a compound, are employed by SwissSimilarity\nto identify structures that bear similarity to the query compound. The 2D\nmethods involve the application of path-based FP2 fingerprint,\nextended-connectivity fingerprint with diameter 4, MinHash fingerprints, 2D\npharmacophore fingerprints, and extended reduced graph fingerprints.\nConversely, the 3D methods encompass Electroshape 5D vectors and extended 3D\nfingerprints. <\/p>\n\n\n\n

SwissSimilarity analysis was employed to discover new compounds that target LXR-\u03b2. This search identified 400 ligands based on the configuration of AZ876, with similarity scores ranging from 0.94 to 0.354 (Supplementary file S1). Based on the structure of LXR-623, we identified 265 compounds in our inquiry with similarity scores ranging from 0.767 to 0.196 (Table 3 and Supplementary file S2). Figure 5 presents the 2D structure of the compounds identified for LXR-\u03b1 in the ZINC database and figure 6 displays the 2D structure of the ten most significant molecules identified and screened for LXR-\u03b2 in the ZINC database. A compound is completely distinct if its score is 0, and it is identical if it is 1 according to the SwissSimilarity study.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 5: The most promising ligands identified for LXR-\u0391 in the ZINC database<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 3: The similarity score obtained from swisssimilarity (https:\/\/www.swisssimilarity.ch\/), binding affinity within the binding pocket and interacting amino acids of new ligands with LXR-\u03b1. <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Ligand<\/strong><\/p>\n<\/td>\n

\n

Similarity <\/strong><\/p>\n

score<\/strong><\/p>\n<\/td>\n

\n

Binding <\/strong><\/p>\n

Affinity (Kcal\/mol)<\/strong><\/p>\n<\/td>\n

\n

rmsd\/ub<\/strong><\/p>\n<\/td>\n

\n

rmsd\/lb<\/strong><\/p>\n<\/td>\n

\n

Interacting amino acid<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000001550221<\/p>\n<\/td>\n

\n

0.985<\/p>\n<\/td>\n

\n

\u201210.2<\/p>\n<\/td>\n

\n

2.153<\/p>\n<\/td>\n

\n

1.402<\/p>\n<\/td>\n

\n

ARG373, ALA374, ASN377, GLU378, LYS452, ARG497, LEU501, LEU504, MET525<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000000985503<\/p>\n<\/td>\n

\n

0.926<\/p>\n<\/td>\n

\n

\u20129.8<\/p>\n<\/td>\n

\n

5.52<\/p>\n<\/td>\n

\n

2.891<\/p>\n<\/td>\n

\n

GLU312, HIS386, ARG387, ILE389, LEU396, LYS435, LEU438<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000058101934<\/p>\n<\/td>\n

\n

0.837<\/p>\n<\/td>\n

\n

\u201210.13<\/p>\n<\/td>\n

\n

1.746<\/p>\n<\/td>\n

\n

1.312<\/p>\n<\/td>\n

\n

ARG373, ALA374, ASP450, ARG497, LEU504<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000095464663<\/p>\n<\/td>\n

\n

0.817<\/p>\n<\/td>\n

\n

\u201212.3<\/p>\n<\/td>\n

\n

3.743<\/p>\n<\/td>\n

\n

2.208<\/p>\n<\/td>\n

\n

ILE445, TYR468, LEU490, LEU493, PRO494<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000003243391<\/p>\n<\/td>\n

\n

0.709<\/p>\n<\/td>\n

\n

\u20129.6<\/p>\n<\/td>\n

\n

8.99<\/p>\n<\/td>\n

\n

3.51<\/p>\n<\/td>\n

\n

GLU378, ARG464, TYR468, PHE486, LEU490, LEU491, PRO494<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000016130131<\/p>\n<\/td>\n

\n

0.637<\/p>\n<\/td>\n

\n

\u20128.2<\/p>\n<\/td>\n

\n

4.489<\/p>\n<\/td>\n

\n

2.429<\/p>\n<\/td>\n

\n

ARG373, ALA374, ASN377, GLU378, LYS452, ARG497, LEU501, LEU504, MET525<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000001042265<\/p>\n<\/td>\n

\n

0.568<\/p>\n<\/td>\n

\n

\u20127.4<\/p>\n<\/td>\n

\n

1.746<\/p>\n<\/td>\n

\n

1.312<\/p>\n<\/td>\n

\n

ARG373, ALA374, ASP450, ARG497, LEU504<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000031669066<\/p>\n<\/td>\n

\n

0.526<\/p>\n<\/td>\n

\n

\u20127.3<\/p>\n<\/td>\n

\n

3.743<\/p>\n<\/td>\n

\n

2.208<\/p>\n<\/td>\n

\n

ILE445, TYR468, LEU490, LEU493, PRO494<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Molecular docking analysis of the novel compounds targeting LXR-\u03b1 and LXR-\u03b2 show distinct binding affinities and patterns of interaction with amino acids <\/strong><\/p>\n\n\n\n

Among the identified novel compounds, ZINC000005399501 presented the highest binding affinity for LXR-\u03b1 as determined by docking analysis.The binding affinity of the novel compounds identified targeting LXR-\u03b1 ranges from -12.3 to -7.3 Kcal\/mol as shown in Table 3. ZINC000005399501 interacts with ARG387, ILE389, ASP390, LYS435, LEU438, GLU308, and GLU312, according to the 2D study of the LXR-\u03b1-ZINC000005399501 complex. The interactions involved were van der Waals, alkyl, pi-alkyl, amide-pi stacked, conventional hydrogen bond, and carbon-hydrogen bond. The predominant interaction observed was the conventional hydrogen bond, followed by pi-sulfur, amide-pi stacked, alkyl, and pi-alkyl interactions involving GLU312, ILE389, LYS435, and LEU438. Fluorine and benzene are commonly used to enhance the properties of chemical compounds and displayed interaction with ARG387, ILE389, GLU308, SER383, HIS386, and GLU312. Specifically, the N\u2010(2\u2010((1R)\u20102,2,2\u2010trifluoro\u20101\u2010hydroxyethyl) phenyl group interacts with GLU308 and SER383, the benzene group interacts with GLU312, ARG387 and ILE389, and the sulfonamide group interacts with HIS386 and LYS435 (Figure 6).<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 6: The ten most promising ligands identified for LXR-\u03b2 in the ZINC database<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

The 2D configuration, the similarity score, and the affinity towards LXR-\u03b2 resulting from the process of docking for the ten most promising compounds that have not been utilized in pharmacotherapies are visually represented in Figure 7. The compound ZINC000021912941 demonstrated the uppermost binding affinity for LXR-\u03b2 in the current virtual screening investigation, indicating its potential as a viable candidate for the development of a drug targeting LXR-\u03b2 due to its potentially effective interaction with the target (as depicted in Figure 7 and Table 4). Previous reports in the scientific literature have documented that the binding affinities of compounds targeting LXR-\u03b2, as determined through in silico studies, fall within a range of -8.1 kcal\/mol to -10.8 kcal\/mol. In the current examination, it is noted that the binding affinity of all the top ten molecules falls within this typical range commonly observed in scientific publications (as illustrated in Figure 8 and Table 4). Notably, the compound ZINC000021912941 exhibits the highest affinity for LXR-\u03b2 and forms interactions with the amino acids PHE271, MET312, PHE329, LEU345, and HIS435 (depicted in Figure 7). While the N-(2,3-dimethylphenyl) group interacts with the amino acids PHE271, LEU345, and HIS435, the 2,3-dihydro-1 lambda6,2-thiazol group in the compound contributes only one interaction with the protein (shown in Figure 8).<\/p>\n\n\n\n\n\n
\"\"<\/td>\nFigure 7: The 2D and 3D formats of the most promising ligands tested in interactions between amino acids of LXR-\u03b1.<\/strong>

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 4: The similarity score obtained from swisssimilarity (https:\/\/www.swisssimilarity.ch\/), binding affinity within the binding pocket and interacting amino acids of new ligands with LXR-\u03b2.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Ligand<\/strong><\/p>\n<\/td>\n

\n

Similarity score<\/strong><\/p>\n<\/td>\n

\n

Binding Affinity (Kcal\/mol)<\/strong><\/p>\n<\/td>\n

\n

rmsd\/ub<\/strong><\/p>\n<\/td>\n

\n

rmsd\/lb<\/strong><\/p>\n<\/td>\n

\n

Interacting amino acid<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000021912941<\/p>\n<\/td>\n

\n

0.96<\/p>\n<\/td>\n

\n

\u201210.7<\/p>\n<\/td>\n

\n

2.122<\/p>\n<\/td>\n

\n

1.466<\/p>\n<\/td>\n

\n

PHE272, SER274, ALA275, GLU281, MET312, THR316, ARG319, PHE329, LEU330, LEU345, ILE353<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000021912951<\/p>\n<\/td>\n

\n

0.795<\/p>\n<\/td>\n

\n

\u201210.2<\/p>\n<\/td>\n

\n

1.625<\/p>\n<\/td>\n

\n

1.734<\/p>\n<\/td>\n

\n

PHE268, LEU274, ALA275, LEU345, ILE353, HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000021913098<\/p>\n<\/td>\n

\n

0.787<\/p>\n<\/td>\n

\n

\u201210.03<\/p>\n<\/td>\n

\n

1.325<\/p>\n<\/td>\n

\n

1.724<\/p>\n<\/td>\n

\n

LEU274, ALA275, LEU345, ILE353,
HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000021913127<\/p>\n<\/td>\n

\n

0.600<\/p>\n<\/td>\n

\n

\u20129.8<\/p>\n<\/td>\n

\n

1.731<\/p>\n<\/td>\n

\n

1.857<\/p>\n<\/td>\n

\n

ALA275, LEU345, HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000095446598<\/p>\n<\/td>\n

\n

0.496<\/p>\n<\/td>\n

\n

\u20129.8<\/p>\n<\/td>\n

\n

2.702<\/p>\n<\/td>\n

\n

1.642<\/p>\n<\/td>\n

\n

PHE271, ALA275, ILE309, MET312, PHE329, PHE340, LEU345, HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000036398658<\/p>\n<\/td>\n

\n

0.485<\/p>\n<\/td>\n

\n

\u20129.5<\/p>\n<\/td>\n

\n

1.625<\/p>\n<\/td>\n

\n

1.734<\/p>\n<\/td>\n

\n

PHE268, LEU274, ALA275, LEU345, ILE353,
HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000037207255<\/p>\n<\/td>\n

\n

0.48<\/p>\n<\/td>\n

\n

-9.2<\/p>\n<\/td>\n

\n

2.531<\/p>\n<\/td>\n

\n

1.257<\/p>\n<\/td>\n

\n

PHE272, LEU274, ALA275, LEU345, ILE353,
HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000040555976<\/p>\n<\/td>\n

\n

0.47<\/p>\n<\/td>\n

\n

-8.7<\/p>\n<\/td>\n

\n

1.724<\/p>\n<\/td>\n

\n

1.625<\/p>\n<\/td>\n

\n

PHE268, LEU274, ALA275, LEU345, ILE353,
HIS435<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000014043132<\/p>\n<\/td>\n

\n

0.447<\/p>\n<\/td>\n

\n

-9.1<\/p>\n<\/td>\n

\n

1.857<\/p>\n<\/td>\n

\n

1.325<\/p>\n<\/td>\n

\n

GLU281, MET312, THR316, ARG319, PHE329, LEU330, LEU345, ILE353<\/p>\n<\/td>\n<\/tr>\n

\n

ZINC000019867701<\/p>\n<\/td>\n

\n

0.443<\/p>\n<\/td>\n

\n

-8.9<\/p>\n<\/td>\n

\n

1.642<\/p>\n<\/td>\n

\n

1.731<\/p>\n<\/td>\n

\n

SER274, ALA275, GLU281, MET312, THR316, ARG319, PHE329<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 8: The 2D and 3D formats of the most promising ligands tested in interactions between amino acids of LXR-\u03b2<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

In silico evaluation of the chemico-pharmacokinetic profile of the known and newly identified compounds<\/strong><\/p>\n\n\n\n

In the process of finding new drugs, it is\nvital to anticipate the pharmacokinetic characteristics, medicinal chemistry,\ndruglike nature, and ADME parameters of one or more small molecules by\ncomputing physicochemical descriptors. We utilized SwissADME to evaluate the\nchemico-pharmacokinetic characteristics of newly discovered and existing\ncandidates in silico<\/em>. Table 5-9 and Supplementary file S3-S5 display the\nresults of the chemico-pharmacokinetic parameters that were determined after\nthe data was processed and entered into the SwissADME system. The molecular weight of the 8 molecules identified by Swissimilarity\nfor LXR-\u03b1 were less than 500 Da, there are less than five\nhydrogen-bond donors or less than ten hydrogen-bond acceptors (nitrogen and\noxygen atoms), the computed logP (ClogP) is less than 5.  They are either inhibitors or non-inhibitors\nof five major isoforms (CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4) of the cytochrome P450 enzyme\nsystems. We observed all 8 molecules are not only non-permeable to the blood-brain barrier but\nalso low in GI absorption, and non-substrate of p-gp (Supplementary\nfile S4).\nSimilar to LXR-\u03b1, the 400 molecules found by Swissimilarity for LXR-\u03b2 had chemicopharmacokinetics\ncharacteristics that were smaller than 500 Da. The H-bond acceptor and donor\nscores were 3, and 9, respectively. The consensus ClogP is less than 5, our\nfindings revealed a variety of characteristics of these ligands in relation to\nthe cytochrome P450 enzyme systems (Supplementary file S5). <\/p>\n\n\n\n

The molecular weight of LXR-623, ZINC000005399501, AZ876 and ZINC000021912941 were 481.33 399.31, 439.57 and 414.43 Da, respectively, which reflects their suitability for oral drug development (Table 5). These compounds not only meet Lipinski’s criteria of molecular mass of less than 500 Da but also have no more than 5 hydrogen bond donors, no more than 10 hydrogen bond acceptors, and calculated implicit log P (iLOGP) that does not exceed 5 (or MlogP>4.15) (Table 5). In addition to Lipinski’s criteria, the MR (molecular refractivity) from 40 to 130 indicates additional features that increase druglikeness, and the TPSA (topological polar surface area) of a molecule greater than 1.40 nm2<\/sup> tends to be poor at permeating cell membranes, while TPSA less than 0.90 nm2<\/sup> is usually needed to penetrate the blood\u2013brain barrier. Thus, except ZINC000021912941, all LXR-623, ZINC000005399501, and AZ876 have the potential to penetrate the blood-brain barrier (Table 5). Lipophilicity or hydrophilicity are also important parameters of a molecule with a limited number of hydrogen bond donors and acceptors are more likely to achieve GI abroption because they can easily pass through the intestines’ cellular membrane and enter the bloodstream.33-35<\/sup> Based on the calculated logP values all tested compounds proved to be lipophilic with consensus values ranging 3.6\u20134.98 (Table 6) There is less absorption of a medicine the more water soluble it is. All the selected candidates analysed water-solubility demonstrates moderately-soluble (Table 7). <\/p>\n\n\n\n

Table 5: The basic physicochemical properties and computational descriptors of LXR-623, AZ876, and the newly identified molecules, as determined in SwissADME.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Features<\/strong><\/p>\n<\/td>\n

\n

LXR-623<\/strong><\/p>\n<\/td>\n

\n

ZINC000005399501<\/strong><\/p>\n<\/td>\n

\n

AZ876<\/strong><\/p>\n<\/td>\n

\n

ZINC000021912941<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

\n

Heavy atoms<\/p>\n<\/td>\n

\n

29<\/p>\n<\/td>\n

\n

26<\/p>\n<\/td>\n

\n

31<\/p>\n<\/td>\n

\n

29<\/p>\n<\/td>\n<\/tr>\n

\n

Aromatic heavy atoms<\/p>\n<\/td>\n

\n

21<\/p>\n<\/td>\n

\n

12<\/p>\n<\/td>\n

\n

12<\/p>\n<\/td>\n

\n

12<\/p>\n<\/td>\n<\/tr>\n

\n

Fraction Csp3<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

0.2<\/p>\n<\/td>\n

\n

0.38<\/p>\n<\/td>\n

\n

0.15<\/p>\n<\/td>\n<\/tr>\n

\n

Rotatable bonds<\/p>\n<\/td>\n

\n

4<\/p>\n<\/td>\n

\n

6<\/p>\n<\/td>\n

\n

5<\/p>\n<\/td>\n

\n

7<\/p>\n<\/td>\n<\/tr>\n

\n

H-bond acceptors<\/p>\n<\/td>\n

\n

6<\/p>\n<\/td>\n

\n

9<\/p>\n<\/td>\n

\n

3<\/p>\n<\/td>\n

\n

6<\/p>\n<\/td>\n<\/tr>\n

\n

H-bond donors<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

MR<\/p>\n<\/td>\n

\n

100.85<\/p>\n<\/td>\n

\n

79.68<\/p>\n<\/td>\n

\n

132.74<\/p>\n<\/td>\n

\n

109.93<\/p>\n<\/td>\n<\/tr>\n

\n

TPSA (\u00c52<\/sup>)<\/p>\n<\/td>\n

\n

17.82<\/p>\n<\/td>\n

\n

74.78<\/p>\n<\/td>\n

\n

78.1<\/p>\n<\/td>\n

\n

118.23<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Table 6: Lipophilicity of LXR-623, AZ876, and the newly identified molecule, determined in SwissADME.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Features<\/strong><\/p>\n<\/td>\n

\n

LXR-623<\/strong><\/p>\n<\/td>\n

\n

ZINC000005399501<\/strong><\/p>\n<\/td>\n

\n

AZ876<\/strong><\/p>\n<\/td>\n

\n

ZINC000021912941<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

\n

iLOGP<\/p>\n<\/td>\n

\n

3.53<\/p>\n<\/td>\n

\n

1.92<\/p>\n<\/td>\n

\n

3.7<\/p>\n<\/td>\n

\n

2.83<\/p>\n<\/td>\n<\/tr>\n

\n

XLOGP3<\/p>\n<\/td>\n

\n

6.54<\/p>\n<\/td>\n

\n

4.14<\/p>\n<\/td>\n

\n

4.51<\/p>\n<\/td>\n

\n

2.49<\/p>\n<\/td>\n<\/tr>\n

\n

WLOGP<\/p>\n<\/td>\n

\n

8.69<\/p>\n<\/td>\n

\n

7.08<\/p>\n<\/td>\n

\n

4.56<\/p>\n<\/td>\n

\n

7.52<\/p>\n<\/td>\n<\/tr>\n

\n

MLOGP<\/p>\n<\/td>\n

\n

5.94<\/p>\n<\/td>\n

\n

3.13<\/p>\n<\/td>\n

\n

2.41<\/p>\n<\/td>\n

\n

0.62<\/p>\n<\/td>\n<\/tr>\n

\n

Silicos-IT Log P<\/p>\n<\/td>\n

\n

6.88<\/p>\n<\/td>\n

\n

3.26<\/p>\n<\/td>\n

\n

2.81<\/p>\n<\/td>\n

\n

1.73<\/p>\n<\/td>\n<\/tr>\n

\n

Consensus Log P<\/p>\n<\/td>\n

\n

6.32<\/p>\n<\/td>\n

\n

3.91<\/p>\n<\/td>\n

\n

3.6<\/p>\n<\/td>\n

\n

4.04<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 7: Water solubility prediction values of LXR-623, AZ876, and the newly identified molecule, determined in SwissADME.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Features<\/strong><\/p>\n<\/td>\n

\n

LXR-623<\/strong><\/p>\n<\/td>\n

\n

ZINC000005399501<\/strong><\/p>\n<\/td>\n

\n

AZ876<\/strong><\/p>\n<\/td>\n

\n

ZINC000021912941<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

\n

ESOL Log S<\/p>\n<\/td>\n

\n

-6.85<\/p>\n<\/td>\n

\n

-4.87<\/p>\n<\/td>\n

\n

-5.36<\/p>\n<\/td>\n

\n

-3.82<\/p>\n<\/td>\n<\/tr>\n

\n

ESOL Solubility (mg\/ml)<\/p>\n<\/td>\n

\n

5.93E-05<\/p>\n<\/td>\n

\n

5.39E-03<\/p>\n<\/td>\n

\n

1.91E-03<\/p>\n<\/td>\n

\n

6.24E-02<\/p>\n<\/td>\n<\/tr>\n

\n

ESOL Solubility (mol\/l)<\/p>\n<\/td>\n

\n

1.40E-07<\/p>\n<\/td>\n

\n

1.35E-05<\/p>\n<\/td>\n

\n

4.33E-06<\/p>\n<\/td>\n

\n

1.51E-04<\/p>\n<\/td>\n<\/tr>\n

\n

ESOL Class<\/p>\n<\/td>\n

\n

Poorly soluble<\/p>\n<\/td>\n

\n

Moderately soluble<\/p>\n<\/td>\n

\n

Moderately soluble<\/p>\n<\/td>\n

\n

Moderately Soluble<\/p>\n<\/td>\n<\/tr>\n

\n

Ali Log S<\/p>\n<\/td>\n

\n

-6.71<\/p>\n<\/td>\n

\n

-5.42<\/p>\n<\/td>\n

\n

-5.87<\/p>\n<\/td>\n

\n

-4.62<\/p>\n<\/td>\n<\/tr>\n

\n

Ali Solubility (mg\/ml)<\/p>\n<\/td>\n

\n

8.21E-05<\/p>\n<\/td>\n

\n

1.53E-03<\/p>\n<\/td>\n

\n

5.91E-04<\/p>\n<\/td>\n

\n

9.99E-03<\/p>\n<\/td>\n<\/tr>\n

\n

Ali Solubility (mol\/l)<\/p>\n<\/td>\n

\n

1.94E-07<\/p>\n<\/td>\n

\n

3.82E-06<\/p>\n<\/td>\n

\n

1.34E-06<\/p>\n<\/td>\n

\n

2.41E-05<\/p>\n<\/td>\n<\/tr>\n

\n

Ali Class<\/p>\n<\/td>\n

\n

Poorly soluble<\/p>\n<\/td>\n

\n

Moderately soluble<\/p>\n<\/td>\n

\n

Moderately soluble<\/p>\n<\/td>\n

\n

Moderately soluble<\/p>\n<\/td>\n<\/tr>\n

\n

Silicos-IT LogSw<\/p>\n<\/td>\n

\n

-9.49<\/p>\n<\/td>\n

\n

-6.06<\/p>\n<\/td>\n

\n

-6.74<\/p>\n<\/td>\n

\n

-5.64<\/p>\n<\/td>\n<\/tr>\n

\n

Silicos-IT Solubility (mg\/ml)<\/p>\n<\/td>\n

\n

1.38E-07<\/p>\n<\/td>\n

\n

3.48E-04<\/p>\n<\/td>\n

\n

8.05E-05<\/p>\n<\/td>\n

\n

9.43E-04<\/p>\n<\/td>\n<\/tr>\n

\n

Silicos-IT Solubility (mol\/l)<\/p>\n<\/td>\n

\n

3.26E-10<\/p>\n<\/td>\n

\n

8.71E-07<\/p>\n<\/td>\n

\n

1.83E-07<\/p>\n<\/td>\n

\n

2.28E-06<\/p>\n<\/td>\n<\/tr>\n

\n

Silicos-IT class<\/p>\n<\/td>\n

\n

Poorly soluble<\/p>\n<\/td>\n

\n

Poorly soluble<\/p>\n<\/td>\n

\n

Poorly soluble<\/p>\n<\/td>\n

\n

Moderately soluble<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Given the undeniable\nbenefits of the oral route of administration, LXR-623 and ZINC000005399501 are\nexpected to have low GI absorption, but AZ876 and ZINC000021912941 exhibit high\nGI absorption, which is a highly beneficial feature of a drug candidate. With\nvery few exceptions, none of these substances are anticipated to function as\nCYP1A2 and CYP2D6 inhibitors, which are involved in the biotransformation of a\nnumber of significant medication classes. It is anticipated that all four of\nthe substances under test will inhibit CYP3A4, CYP2C9, and CYP2C19. This could\nbe detrimental because these CYPs are involved in the metabolism and excretion\nof numerous clinically prescribed medications. The target chemicals are not\nshown to be able to pass the BBB by the computational data. <\/p>\n\n\n\n

The drug similarity, medicinal chemistry, and lead-likeness properties of the examined compounds were also computed. The characteristics of drug likelinesses are demonstrated by all of these compounds, with very few exceptions for Ghose, Egan, and Muegge breaches. The Abbot bioavailability score, which indicates the probability of a chemical exhibiting considerable Caco-2 permeability, was computed. Table 9 displays the data indicating that all examined compounds had a 0.55 likelihood of reaching the previously mentioned bioavailability for gastrointestinal absorption based on total charge, TPSA, and violation of the Lipinski. Synthetic accessibility of these compounds was within the range of 2.66-4.25 with no PAINS alerts and Brenk alerts (Table 9)     <\/p>\n\n\n\n

Table 8: Predicted pharmacokinetic parameters of the tested compounds.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Features<\/strong><\/p>\n<\/td>\n

\n

LXR-623<\/strong><\/p>\n<\/td>\n

\n

ZINC000005399501<\/strong><\/p>\n<\/td>\n

\n

AZ876<\/strong><\/p>\n<\/td>\n

\n

ZINC000021912941<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

\n

GI absorption<\/p>\n<\/td>\n

\n

Low<\/p>\n<\/td>\n

\n

Low<\/p>\n<\/td>\n

\n

High<\/p>\n<\/td>\n

\n

High<\/p>\n<\/td>\n<\/tr>\n

\n

BBB permeant<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n<\/tr>\n

\n

Pgp substrate<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n<\/tr>\n

\n

CYP1A2 inhibitor<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C19 inhibitor<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2C9 inhibitor<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n<\/tr>\n

\n

CYP2D6 inhibitor<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n<\/tr>\n

\n

CYP3A4 inhibitor<\/p>\n<\/td>\n

\n

No<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n<\/tr>\n

\n

logKp (cm\/s)<\/p>\n<\/td>\n

\n

\u20124.24<\/p>\n<\/td>\n

\n

\u20125.8<\/p>\n<\/td>\n

\n

\u20125.78<\/p>\n<\/td>\n

\n

\u20127.06<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Table 9: Drug likeness, medicinal chemistry, and lead-likeness parameters for the tested compounds.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Features<\/strong><\/p>\n<\/td>\n

\n

LXR-623<\/strong><\/p>\n<\/td>\n

\n

ZINC000005399501<\/strong><\/p>\n<\/td>\n

\n

AZ876<\/strong><\/p>\n<\/td>\n

\n

ZINC000021912941<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

\n

Lipinski violations<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Ghose violations<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Veber violations<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Egan violations<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Muegge violations<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Bioavailability Score<\/p>\n<\/td>\n

\n

0.55<\/p>\n<\/td>\n

\n

0.55<\/p>\n<\/td>\n

\n

0.55<\/p>\n<\/td>\n

\n

0.55<\/p>\n<\/td>\n<\/tr>\n

\n

PAINS alerts<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Brenk alerts<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n<\/tr>\n

\n

Leadlikeness violations<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

Synthetic Accessibility<\/p>\n<\/td>\n

\n

2.94<\/p>\n<\/td>\n

\n

3.07<\/p>\n<\/td>\n

\n

4.25<\/p>\n<\/td>\n

\n

3.63<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

MD simulation of the known vs the newly selected compounds with LXR-\u03b1\/\u03b2 complexes <\/strong><\/p>\n\n\n\n

MD simulation was conducted to investigate the complexes of LXR-\u03b1-LXR-623, LXR-\u03b2-AZ876, and two novel ligand-proteins. Specifically, the RMSD of alpha carbon atoms, RMSF of all amino acid residues, and the number of hydrogen bonds of the selected drug-protein complexes were calculated for a duration of 50 ns. The RMSD of LXR-\u03b1-ZINC000005399501 and LXR-\u03b2-ZINC000021912941 showed stabilization after 10 ns of simulation, with average values of 1.95\u00b10.20 \u00c5 and 1.85\u00b10.33 \u00c5, respectively (Table 10). However, the RMSD of LXR-\u03b1-LXR-623 became unstable after 45 ns of MD simulation, similar to the trend observed in the LXR-\u03b2-AZ876 complex (Figure 9). RMSF analysis, which explores the impact of drug molecule binding on the behavior of amino acid residues, revealed low RMSF values for LXR-\u03b1- ZINC000005399501 and LXR-\u03b2- ZINC000021912941, indicating reduced flexibility compared to the known protein-ligand complex. Additionally, the total number of hydrogen bonds between protein-drug complexes, which significantly contribute to the conformational stability of the complex, was calculated (Table 10). The LXR-\u03b1-ZINC000005399501 and LXR-\u03b2-ZINC000021912941 complexes exhibited more hydrogen bonds throughout the 50 ns simulation. Furthermore, binding free energy calculations were performed for each complex. The average binding free energy values were determined to be -28.33\u00b10.39, -24.36\u00b10.63, -15.21\u00b10.06, and -17.89\u00b10.35 kcal\/mol for ZINC000005399501, ZINC000021912941, LXR-623 and AZ876, respectively.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 9: RMSDs and RMSF profile of the protein backbone of the first MD simulation for each LXR\u2013ligand system for 50 ns<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 10: RMSD and binding free energy of the selected drugs\u2013LXRs complexes, determined in QwikMD.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n
\n

Complex<\/strong><\/p>\n<\/td>\n

\n

RMSD (\u00c5)<\/strong><\/p>\n<\/td>\n

\n

Binding free energy (kcal\/mol)<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

LXR-\u03b1- ZINC000005399501<\/p>\n<\/td>\n

\n

1.81\u00b10.3<\/p>\n<\/td>\n

\n

\u201323.33\u00b10.39<\/p>\n<\/td>\n<\/tr>\n

\n

LXR-\u03b2- ZINC000021912941<\/p>\n<\/td>\n

\n

1.9\u00b10.32<\/p>\n<\/td>\n

\n

\u201322.36\u00b10.63<\/p>\n<\/td>\n<\/tr>\n

\n

LXR-\u03b1-LXR-623<\/p>\n<\/td>\n

\n

2.49\u00b10.71<\/p>\n<\/td>\n

\n

\u201313.79\u00b11.03<\/p>\n<\/td>\n<\/tr>\n

\n

LXR-\u03b2-AZ876<\/p>\n<\/td>\n

\n

2.24\u00b10.41<\/p>\n<\/td>\n

\n

\u201317.89\u00b10.35<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Discussion<\/strong><\/p>\n\n\n\n

Because of important regulator of glucose, fatty acid, and cholesterol homeostasis, LXRs are crucial for several major physiological processes including the metabolic system.10,50,51<\/sup> Our in-silico study results of the distinct mRNA and protein expression of LXR\u03b1 and LXR\u03b2 across the tissues also echoed their importance in physiological processes (Figure 1). There are milieu of reports stated the dysregulation of LXRs and the development of disease including atherosclerosis, Parkinsons disease, other metabolic disorders and cancer.10,12,52-55<\/sup> These reports are aligned with our results of Gene disease association study, networks, and pathway enrichment analysis (Figure 2). LXR-\u03b1 and LXR-\u03b2 are linked to metabolic diseases like atherosclerosis and coronary heart disease, and are essential for physiological functions, interacting with enzymes, transcription factors, transporters, ion channels, and receptors (Figure 2).<\/p>\n\n\n\n

Previous reports and our analysis of LXRs in physiological function and disease association intrigued us to find potential LXR modulators. Thus, we looked through the literatures and found out both endogenous and small molecule ligands targeting LXRs. Our search identified a list of LXR ligands and presented in table 1 and 2. Many of these ligands have been tested in preclinical phases.10,12,18,53-59<\/sup> Only few of them are identified through screening or computer aided designing and tested in in-vitro settings.18,50,51,56,58<\/sup> Therefore, our results of docking study as such binding affinities and presented interacting amino acids within  4 angstrom of the binding pocket would be important data addition to these known compounds (Table 1 and 2).   <\/p>\n\n\n\n

Our results of molecular docking, virtual screening, and pharmacokinetic in silico techniques indicated that LXR-623 and AZ876 had the best affinity with favorable drug-like features for LXR-\u03b1\/\u03b2 among these compounds (Figure 3, 4 and Table 5-9). For screening new ligands for LXR-\u03b1\/\u03b2, we used a web-based server (http:\/\/www.swisssimilarity.ch\/index.php). Swisssimilarity is integrated with many chemical structure computational models (e.g- FP2, ECFP4, MHFP6, Pharmacophore, ErG, Scaffold, Generic Scaffold, Electroshape, E3FP, etc.) and allows many chemical databases (e.g. ZINC, Drug Bank, ChemBL) for new ligand searching. In our study, we have identified two novel small molecules ZINC000095464663 and ZINC000021912925 from the ZINC drug-like database that exhibit the highest affinity for LXR, -12.3 and -11.7 Kcal\/mol, respectively (Figure 6, 8 and Table 3, 4).     <\/p>\n\n\n\n

Basic physicochemical properties, ADME parameters, pharmacokinetic properties, druglike nature and medicinal chemistry friendliness of LXR-623, AZ876, andthe newly identified molecules are also favorable to the new drug molecule selection criteria for future drug development process (Table 5-9). Although, these estimations are acquired through the use of molecular descriptors derived from the molecules’ chemical structures and machine-learning methodologies. It is imperative to stress that, while in silico techniques for evaluating a drug’s ADME qualities can be useful in the early phases of drug development, in vivo investigations should always take precedence.37,41<\/sup> Rather, these methods serve as a faster and more cost-effective means of investigating the pharmacokinetic characteristics of small molecules.41<\/sup><\/p>\n\n\n\n

Presently, the exploration\nand creation of novel drugs involves the use of computational technologies\nincluding MD simulations, virtual screening, pharmacokinetic investigations,\nand molecular docking.26-31,34-37,41<\/sup> A\ndeeper understanding of drug-protein interactions, the ability to improve the\ndevelopment and optimization of novel molecules, and the ability to predict the\nbinding affinity between potentially active molecules and their target proteins\nthrough precise molecular simulation models and analyses are just a few of the\nmajor benefits of in-silico studies. These studies also offer cost and time savings compared\nto traditional wet lab experiments, as they allow for rapid evaluation of a\nlarge number of candidate molecules.38<\/sup>\nThe present study also leveraged these techniques to investigate the\ninteraction with three LXR isoforms, leading to the identification of two\ncompounds that hold promise for further laboratory studies on atherosclerosis.\nThese compounds can be thoroughly evaluated for their safety and efficacy\nprofile.<\/p>\n\n\n\n

There are certain limitations on the types of evaluations\nconducted within this study of molecular docking, MD simulation, virtual\nscreening and Chemico-pharmacokinetic strategies to identify novel compounds\ntargeting LXRs.29<\/sup> Molecular\ndocking analysis has provided valuable insights into interactions between the\ntarget compounds and the LXRs; however, it remains essential to experimentally\nverify these findings. The results of this dry lab\nstudy may not always match the wet lab experimental validation technique’s\npredictions, and the procedure can be expensive and time-consuming. The\nproteins are highly dynamic in the biological environment and can experience\nsignificant conformational changes when bound by a ligand, molecular docking\nanalyses concentrate on a single static interaction between the ligand and its\ntarget protein. Molecular docking analysis has been dependent on multiple\napproximations, including the exclusion of other proteins from the cellular\nmilieu and the disregard of solvent impact. <\/p>\n\n\n\n

The accuracy of the\nsimulations may be compromised by these assumptions, which could lead to\nerroneous estimates. Given these constraints, there is potential to improve the\nstudy’s design by investigating new research directions using network\npharmacology and molecular dynamics, which take into consideration the dynamic\nproperties of proteins and possible interactions, the effects of solvents and\nions, and the simulation of other cellular structures that might have an impact\non the interaction.27,28,30<\/sup><\/p>\n\n\n\n

Conclusions<\/strong><\/p>\n\n\n\n

Both LXR-\u03b1 and LXR-\u03b2 exhibit distinct roles\nin the regulation of physiological cholesterol and lipids, as suggested by\ntheir distinct expression patterns, the associations between genes and\ndiseases, and the enrichment of networks and pathways in both healthy and\npathological tissues. <\/p>\n\n\n\n

Through a comprehensive examination of the\nliterature and a detailed analysis of molecular docking, simulation, and\nchemico-pharmacokinetic features, we narrated two new ligands (ZINC000095464663\nand ZINC000021912925) with known agonists that have the ability to modulate\nLXRs. These findings may provide intuition for further\ninvestigation not only in-vitro<\/em> but also in-vivo<\/em> research for the\nfuture development of innovative therapeutic agents targeting LXRs.<\/p>\n\n\n\n

Acknowledgment<\/strong><\/p>\n\n\n\n

We acknowledge the Department of Pharmacy, World University of Bangladesh, Dhaka for facilitating the project. <\/p>\n\n\n\n

Conflicts of Interest<\/strong> <\/p>\n\n\n\n

The authors do not have any conflict of interest <\/p>\n\n\n\n

Funding\nSources<\/strong><\/p>\n\n\n\n

The author(s) received no financial support for the research, authorship, and\/or publication of this article <\/strong><\/p>\n\n\n\n

Data\nAvailability Statement<\/strong> <\/p>\n\n\n\n

The datasets\ngenerated and\/or analysed in this study are available from the corresponding\nauthor upon reasonable request.<\/p>\n\n\n\n

Ethics approval <\/strong><\/p>\n\n\n\n

This research did not involve human participants, animal\nsubjects, or any material that requires ethical approval<\/p>\n\n\n\n

Informed Consent Statement<\/strong><\/p>\n\n\n\n

This study did not involve human participants, and therefore, informed consent was not required<\/p>\n\n\n\n

Authors’ Contributions<\/strong><\/p>\n\n\n\n

Sarder Arifuzzaman conceived the study, interpreted the data and wrote the manuscript. Sarder Arifuzzaman revised the manuscript. Zubair Khalid Labu, Md. Harun-Or-Rashid, Farhina Rahman Laboni, Mst. Reshma Khatun, Md Sajib Ali, Shadek Hossain and Nargis Sultana Chowdhurywere involved in proofreading and revising the manuscript.All of the authors read and approved the final manuscript.  <\/p>\n\n\n\n

References<\/strong><\/p>\n\n\n\n

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