Identification of Novel Compounds Targeting the Liver X Receptor (LXR): in-silico studies, screening, molecular docking, and chemico-pharmacokinetic analysis

doi:10.21203/rs.3.rs-3688739/v1

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Research Article

Identification of Novel Compounds Targeting the Liver X Receptor (LXR): in-silico studies, screening, molecular docking, and chemico-pharmacokinetic analysis

https://doi.org/10.21203/rs.3.rs-3688739/v1

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Background

Studies have shown that LXR activity is linked to the development of many diseases, including metabolic diseases. Several LXR agonists have been discovered, but none of the agonists have entered human use due to undesirable side effects.

Method

In this study, we used multiple biological data repositories (e.g., RNA-seq, human protein atlas, DisGeNET, WebGestalt, and many more) to examine the mRNA and protein expression of LXRs across the tissues and performed network and pathway analysis to redefine their physiological function and disease association. By using in silico research, the current research searches the literature, concentrating on the discovery of new, potentially useful compounds targeting LXRs. We performed molecular docking analysis on LXR agonists that are either approved for preclinical trials or in advanced stages of research. This was carried out using AutoDockTools, ligand-based virtual screening, in-silico studies, screening, molecular docking, and chemico-pharmacokinetic analysis

Results

Our research implies that the various physiological roles of LXRs and the pharmacological modification of LXRs by small molecules may offer pharmacotherapeutic approaches for disease intervention. After conducting molecular docking analysis and in silico searches, we selected T0901317 and AZ876 for additional screening because they showed the highest affinity for LXR-α and LXR-β. We later conducted a global screening for novel compounds for the LXRs, guided by the previously established chemical structures of T0901317 and AZ876, as well as chemico-pharmacokinetic analysis. Finally, ZINC000095464663 and ZINC000021912925 were found to have the highest binding affinities (− 12.3 and − 11.7 kcal/mol), and potentially useful compounds with favorable chemico-pharmacokinetic features for LXR-α and LXR-β, respectively.

Conclusion

In summary, the use of SwissSimilarity, molecular docking analysis, and SwissADME for in silico chemico-pharmacokinetic assessment revealed two new and ten previously reported small molecules with potential for oral administration that target LXR-α and LXR-β. This could lead to the development of medium- and long-term pharmacotherapeutic solutions for these molecules.

metabolic disease

LXR agonists

atherosclerosis

protein targets

molecular docking

virtual screening

The liver X receptor (LXR) is a nuclear receptor family and is closely related to other nuclear receptors such as PPARs, FXR, and RXR [1]. There are two isoforms of LXRs, LXR-α, and LXR-β, and are given the nuclear receptor nomenclature symbols NR1H3 and NR1H2, respectively [1]. The well-established role that LXRs play in controlling the body's levels of glucose, fatty acids, cholesterol, and other substances was confirmed and reaffirmed by published data on LXRs in gene-disease association, network, and mRNA and protein expression in tissues. Dysregulations of LXRs are associated with the development of metabolic disorders such as inflammation, hyperlipidemia, and atherosclerosis both in human and nonhuman primates [2–7]. LXR agonists are found to be effective in treating murine models of various diseases, including atherosclerosis, diabetes, anti-inflammation, Alzheimer's disease, and cancer [8–15].

Treatment with LXR agonists, such as T0901317 and GW3965 has been shown to lower cholesterol levels in serum and liver and inhibits the development of atherosclerosis in murine disease models [13–20] Other studies showed that SR9243 had not only the modulation ability of lipid metabolism in cancer cells, but also suppressed nonalcoholic steatohepatitis intrahepatic inflammation and fibrosis [17, 21–23]. An agonist of pan LXR-α/β, GSK3987 has EC50s of 40–50 nM reduces triglyceride accumulation and enhances cellular cholesterol efflux in mouse models [24]. Several LXR agonists tested in clinical trials for metabolic diseases including LXR-623, BMS-779788, BMS-852927, and AZ876 [18, 25–30]. Despite LXR agonist’s efficacy, there continue to be several unmet needs in its development and application. Efforts to identify LXR-α/β selective agonists with fewer adverse effects are continuing.

Molecular docking studies are valuable because they can provide insights into the potential binding affinity of small drug molecules to target proteins, but the experimental endorsement is needed to validate the results and determine their genuine therapeutic potential [31]. SwissSimilarity allows the rapid screening of small to very large-scale libraries of drugs, bioactive small molecules, commercially available compounds, and an ultra-large library of virtual compounds readily synthesizable from commercially available reagents such as PubChem, ZINC database, Drug Bank, and others [32, 33]. Molecular fingerprints and fast non-super positional or super positional 3D shape similarity techniques are used for the virtual screening in SwissSimilarity [32]. In addition, SwissADME is a useful web tool to compute the physicochemistry and evaluate pharmacokinetics, drug-likeness, and medicinal chemistry friendliness of small molecules with efficacy and safety [34, 35].

Therefore, the goal of this study was to use molecular docking, virtual screening, and chemico-pharmacokinetic in silico strategies to identify compounds with the highest affinity to the target protein, transforming substances not currently used in medical practice into substances with potential use in the treatment of metabolic diseases, atherosclerosis, and cancer with fewer adverse effects. We also searched the publicly available database to reevaluate the mRNA and protein level expression of LXR-α and LXR-β across the normal and disease tissues and redefine the disease association [36–39]. By modulating the interaction between the drug candidate and the LXR target protein, compounds with strong binding affinities and specificities for LXR can be identified, making them potential candidates for further investigation.

mRNA and protein expression analysis of LXRs

To get insight into the physiological role, we examined the mRNA and protein expression of LXRs. We curated mRNA expression data from the HPA RNA-seq database (https://www.ncbi.nlm.nih.gov/gene/) of normal tissues for LXR-α and LXR-β with gene ID 10062 and 7376, respectively [36]. Data obtained are shown in Fig. 1a. We also searched the Human Protein Atlas (https://www.proteinatlas.org/) to observe the protein level expression [37, 38]. Curated data of protein expression for LXR-α and LXR-β are shown in Fig. 1b.

Gene-disease association analysis

To observe major diseases involved with 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 [39]. DisGeNET integrates data from scientific literature, animal models, GWAS catalogs, and expert-curated repositories. To prioritise the correlations between genotype and phenotype, DisGeNET uses a text-mining technique [39]. GDA analysis data are shown in Fig. 2a

Gene network and pathway analysis

To get more insight into the physiological and pathological role of LXRs expression, we performed a protein-protein interaction analysis, in particular, we tested the interaction using IntAct (https://www.ebi.ac.uk/intact/). To address the LXRs involvement in biological/physiological pathways, we performed pathway enrichment analysis using WebGestalt (https://www.webgestalt.org/), a functional enrichment analysis web tool [40]. WebGestalt follows well-established and complementary methods for enrichment analysis, including Over-Representation Analysis (ORA) [40].

Identification and Selection of Target Proteins

Clinical evidence demonstrated that LXRs are important regulators of cholesterol, fatty acid, and glucose homeostasis and LXR agonists are effective for the treatment of murine models of atherosclerosis, diabetes, anti-inflammation, Alzheimer's disease, and cancer. The complexity of the role of LXRs in the pathophysiology, as well as the promising results obtained from the use of LXR agonists, renders the above-mentioned proteins highly essential in the overall management of atherosclerosis, diabetes, anti-inflammation, Alzheimer's disease, and cancer and makes them suitable for in silico studies. Therefore, LXR-α and LXR-β with PDB IDs 1uhl and 5ajy were selected as target proteins for the present molecular docking study. For both target LXRs ligands selected based on affinity for these structures have been redocked.

Ligand Preparation

A total of 10 chemical compounds— T0901317, AZ876, BMS-779788, BMS-852927, GSK3987, LXR-623, SR9243, GW3965, 24S-hydroxycholesterol, Oxycholesterol - were curated by literature searched, selected and reprocessed the structure in Simulation Description Format (SDF) using the ChemAxon’s desktop applications include Marvin, a free chemistry software for drawing and visualizing chemical structures. Since SDF formatting is not compatible with the molecular docking software used (AutoDock Tools 1.5.7), these files were converted to the specific AutoDock Tools 1.5.7 format (Protein Data Bank, Partial Charge (Q), and Atom Type (T) format). Furthermore, ligands were imported into the AutoDockTools software using the ligand input function to add charges and determine rotatable bonds [41].

Protein Preparation

The data required for the LXR-α (1UHL) and LXR-β (5JY3) studies were obtained from the Research Collaboratory for Structural Bioinformatics PDB online platform. To perform the molecular docking assay, a ligand-free and co-crystallized water-free protein is required, and PyMOL 2.5 software [42] has been used in this regard. Furthermore, polar hydrogens and Gasteiger charges were added using AutoDockTools 1.5.7 [41].

Molecular Docking Analysis

AutoDockTools 1.5.7 was used to perform molecular docking studies, and Discovery Studio Visualizer 4.5 was used to create 2D and 3D images. Additionally, Open Babel GUI 3.0.1 was used in the ligand preparation process, and PyMOL2.5 software was used to analyze the protein preparation workflow [42, 43]. By redocking the substance that the protein co-crystallizes with, the docking procedure was proven effective. The grid box dimensions were set at 55X55X55. The redocking of the native ligand (i.e., the ligand molecule that is co-crystallized with the target protein) back into its own protein binding pocket was carried out using the previously mentioned molecular docking method as a strategy for validating the docking process. The goal of the docking algorithm is to mimic the native ligand's experimentally observed binding conformation. The accuracy of the docking algorithm is assessed by comparing the docked pose to the crystallographic pose. The similarity between the atomic coordinates of the two poses is assessed using the RMSD.

To determine how closely the predicted pose resembles the crystallographic pose in the context of docking evaluation, the RMSD is calculated relative to the native ligand. In the field of computational molecular docking, algorithms are regarded as reliable and valid if they produce poses with RMSD values less than 2 Å, with RMSD calculated relative to the native ligand. An improved match between the predicted and experimental poses is indicated by a lower RMSD value, which shows improved accuracy of the docking algorithm [44]. For all analyses carried out in this study, the RMSD value determined using the AutodockTools software [41] is within the range that has been accepted in the literature (< 2 Å).

Virtual Screening

Using the online tool SwissSimilarity (http://www.swisssimilarity.ch/), compounds with similar chemical structures and therapeutic potential were screened out for LXR-α and LXR-β. The chemical structure of the compounds that demonstrated the highest affinity for LXR-α and LXR-β after docking studies was used to identify these compounds. The ligands were downloaded in .SDF format from the ZINC database [45], converted with OpenBabelGUI 3.0.1 into a format compatible with AutoDock Vina 1.2.0, and with the Autodock Vina 1.2.0 software the necessary charges were added, and the rotatable bonds were determined [41].

Chemico-Pharmacokinetic Profile Predictions

We predicted the chemico-pharmacokinetic profile of the compounds identified as the most promising for LXR-α and LXR-β targeting from molecular docking and virtual screening analyses using the SwissADME (http://www.swissadme.ch/) online tool.

LXRs mRNA and protein expression in tissues, gene-disease association, network and pathway enrichment analysis represents their important roles in regulating physiological function

We first examined both mRNA and protein expression across various tissues using publicly available RNA-seq databases and human protein atlas, respectively [36, 37]. LXR-α expression is primarily found in fat and is limited to the liver, kidney, intestine, fat tissue, lung, and spleen. While, almost every tissue and organ express LXR-β (Fig. 1a, b). LXR-α and LXR-β exhibit overlap in several tissues and their tissue distribution patterns differ significantly. The contrasting expression patterns imply distinct roles for LXR-α and LXR-β in controlling physiological processes. Gene disease association study suggested LXR-α has been linked to multiple metabolic diseases, such as atherosclerosis, coronary heart disease, metabolic syndrome, type 2 diabetes, and coronary hyperlipidemia, as determined by DISGENET [39]. LXR-β also linked to several metabolic disorders and carcinomas (Fig. 2a).

Network analysis predicted that LXRs are either directly interact or physically associate with enzymes (KDM1A and SUV39H1), transcription factors (RXRG, RXRA, RXRB, and NCOR1) transporters (PPARA), and other important proteins (EDF1, MDFI and CORO2A) (Fig. 2b). These enzymes, transcription factors, and transporters are well known for their role in the regulation of cholesterol and lipids. The major pathways involved for LXRs are also established by pathway enrichment analysis by WebGestalt. The pathways include cholesterol metabolism, nuclear receptors in lipid metabolism and toxicity, cholesterol and lipid homeostasis, oxysterols derived from cholesterol, PPAR Alpha Pathway, white fat cell differentiation, and PPAR signaling pathway (Fig. 2c). Together these data suggest that pharmacological LXR activation by small molecules could be a pharmacotherapeutic solution to intervene in atherosclerosis.

Molecular docking approach targeting LXR-α

The Protein Data Bank (PDB) ID 1UHL is used for the LXRα active site. The grid box size was set to 55X55X55 Å and its coordinates were set to X = 44.53, Y = -2.4883, Z = 21.7037. The docked structure overlapped with the co-crystallized structure to a large extent, and the docking method adequately simulated the ligand-protein interaction, as evidenced by the calculated root-mean-square deviation (RMSD) value of 1.931 Å, which fit within the accepted literature values (< 2 Å). The best conformation showed an affinity for T0901317, BMS-779788, BMS-852927, SR9243, GSK3987, AZ876, GW3965, 24(S)-hydroxycholesterol, LXR-623, 22(R)-hydroxycholesterol, and Oxycholesterol (Fig. 3 and Table 1). It has been shown that T0901317 has the highest binding affinity for LXRα of all the ligands studied, signifying that T0901317 has the highest potential for a strong interaction with LXR-α, which may lead to its inhibition and subsequent expression of its therapeutic effects. Figure 3a–c depicts the in-depth 2D and 3D interactions of T0901317 with different amino acids of LXR-α. Binding affinity for other molecules has been shown in Table 1. BMS-779788 interacts with PHE229, LEU260, VAL263, SER264, and ASP318, and Oxycholesterol, having the lowest docking score (-6.5 kcal/mol), interacts with PHE229, LEU260, VAL263, SER264, ASP318, and PHE326.

Table 1

Binding affinity of ligands within the binding pocket and interacting amino acid of LXR-α.
Ligand	Binding Affinity (Kcal/mol)	rmsd/ub	rmsd/lb	Interacting amino acid
T0901317	-10.2	2.153	1.402	GLU308, VAL311, GLN313, SER383, ARG387, ILE389, LYS435
AZ876	-8.5	5.52	2.891	ILE370, ARG373, ALA374, TRP376, LYS452, LEU504, PHE508
BMS-779788	-8.3	1.746	1.312	GLU308, GLU312, HIS386, ARG387, ILE389, LEU396, LYS435
BMS-852927	-8.2	30.743	26.208	ILE370, ARG373, ALA374, LYS452, ARG497, LEU501, LEU504, PHE503, ALA528
GSK3987	-7.4	8.99	3.51	ILE370, ARG373, ALA374, GLU378, LEU501, LEU504, ALA528
LXR-623	-7.3	4.489	2.429	ARG373, ALA374, ASN377, GLU378, LYS452, ARG497, LEU501, LEU504, MET525
SR9243	-7.2	10.006	3.63	GLU312, HIS386, ARG387, ILE389, LEU396, LYS435, LEU438
GW3965	-7.0	4.685	2.761	ARG373, ALA374, ASP450, ARG497, LEU504
24S-hydroxycholesterol	-6.9	5.959	4.397	ILE445, TYR468, LEU490, LEU493, PRO494
Oxycholesterol	-6.5	2.06	1.466	ARG387, ILE389, ARG443
*rmsd/lb (root mean square deviation/upper bound); *rmsd/lb(root mean square deviation/lower bound)

GSK3987 has a binding affinity of − 7.4 kcal/mol and binds to the following amino acids: SER366, VAL386, GLU387, HIS390, PHE404, LEU408, LEU411, and ARG415. AZ876 has an affinity of − 8.5 kcal/mol towards LXR-α and interacts with the following amino acids: THR292, ARG369, PRO370, ASN371, ARG415, TRP443, and ASP444. GW3965 showed an affinity of − 7.0 kcal/mol and interacted with the following amino acids: ARG226, PHE229, VAL263, ARG305, and TYR306. While LXR-623 showed the affinity for LXR-α (− 7.3 kcal/mol), binds to the following amino acids: ARG373, ALA374, ASN377, GLU378, LYS452, ARG497, LEU501, LEU504, MET525. Further research is necessary to confirm the hypothesis that the increased number of interactions between the amino acids of LXR-α and the tested compounds is linked to higher binding affinities and greater potential for LXR-α activation.

Molecular docking approach targeting LXR-β

Similar to the protocol for LXR-α, the native PDB protein-ligand with ID 5JY3 was redocked, and the RMSD was calculated. In the case of LXR-β, the protein was co-crystallized with AZ876 (Fig. 4). The calculated RMSD value for docked versus co-crystallized AZ876 is 1.271 Å. The size of the grid box is unchanged (55X55X55 Å) and the coordinates are X = 31.0537, Y = 19.3507, and Z = 13.216. Figure 4a–c depicts the in-depth 2D and 3D interactions of AZ876 with different amino acids of LXR-β. Binding affinity for other molecules has been shown in Table 2.

Table 2

Binding affinity of ligands within the binding pocket and interacting amino acid of LXR-β.
Ligand	Binding Affinity (Kcal/mol)	rmsd/ub	rmsd/lb	Interacting amino acid
AZ876	-10.8	1.625	1.734	PHE268, PHE271, LEU274, ALA275, MET312, PHE 329, LEU345, ILE353, HIS435
SR9243	-10.3	2.122	1.466	PHE268, SER274, ALA275, ILE309, MET312, LEU313, PHE329, LEU330, LEU345, PHE349, ILE350, ILE353, HIS435
22R-hydroxycholesterol	-9.8	1.731	1.857	LEU274, ALA275, SER278, PHE 329, LEU345, HIS435
GW3965	-9.8	2.702	1.642	PHE271, LEU274, ALA275, MET312, PHE329, PHE340, LEU345, HIS435
LXR-623	-9.8	1.387	1.766	PHE268, LEU274, ALA275, LEU345, ILE353, HIS435
T0901317	-9.7	3.589	2.528	PHE272, SER274, ALA275, GLU281, MET312, THR316, ARG319, PHE329, LEU330, LEU345, ILE353
GSK3987	-9.2	3.57	2.39	ALA275, LEU345, HIS435
BMS-779788	-9.0	2.213	2.81	PHE271, ALA275, ILE309, MET312, PHE329, PHE340, LEU345, HIS435
BMS-852927	-8.9	3.09	2.356	PHE271, ALA275, LYS305, ILE309, MET312, PHE329, PHE340, LEU345, HIS435, SER436
Oxycholesterol	-8.3	1.201	1.754	PHE268, PHE271, MET312, ILE327, PHE329, PHE340, LEU345, ILE353
24S-hydroxycholesterol	-8.1	2.714	2.472	PHE268, LEU274, ALA275, MET312, PHE329, LEU345, ILE353, HIS435
rmsd/lb (root mean square deviation/upper bound); rmsd/lb(root mean square deviation/lower bound)

Subsequently, other ligands, including AZ876, BMS-779788, BMS-852927, GSK3987, GW3965, LXR-623, SR9243, 24(S)-hydroxycholesterol, 22(R)-hydroxycholesterol and Oxycholesterol, all with demonstrated affinities for LXR-β, have been docked. Protein preparation was carried out according to the data presented in the materials and methods section. The binding affinities of the docked ligands are presented in Table 2. Of the examined ligands, AZ876 has the highest affinity for LXR-β, with − 10.8 kcal/mol, and interacts with the following amino acids: ILE25, ALA28, ALA29, GLN32, TRP62, ASN63, LEU66, ILE67, PHE70, LEU83, ALA84, ILE102, CYS189, HIS192, and PHE196 (Fig. 3). BMS-779788, which has a lower affinity than BMS-852927 and interacts with GLN32, TRP62, ASN63, LEU66, ILE67, PHE70, LEU83, ALA84 and ILE102, while BMS-852927 has an affinity of − 8.9 kcal/mol and interacts with ALA28, ALA29, GLN32, TRP62, ASN63, LEU66, ILE67, PHE70, LEU83, ALA84, and ILE102 (Table 2).

Virtual screening of the most promising compounds

In order to identify new compounds targeting LXR-α and LXR-β, a search for target compounds in the ZINC online database was performed using SwissSimilarity online platform. SwissSimilarity provides multiple compound databases for screening, including authorized pharmaceuticals, widely accessible commercial compounds, and acknowledged biomolecules [32]. Based on the structure of T0901317, the compound with the highest binding affinity to LXR-α, 8 structures (ZINC000001550221, ZINC000000985503, ZINC000058101934, ZINC000095464663, ZINC000003243391, ZINC000016130131, ZINC000001042265, ZINC000031669066) were found with similarity scores ranging from 0.985 to 0.526 (Fig. 5 and Table 3). The similarity score shows the similarity between T0901317 and the newly identified compounds (Fig. 5 and Table 3). The name assigned by the ZINC database, the chemical structure in 2D and 3D format, and the protein affinity resulting from the docking process for the three most relevant (highest binding affinities to LXR-α) compounds not used in medical practice are shown in Figs. 5 and 7.

Table 3

Binding affinity of newly ligands within the binding pocket and interacting amino acid of LXR-α.
Ligand	Similarity score	Binding Affinity (Kcal/mol)	rmsd/ub	rmsd/lb	Interacting amino acid
ZINC000001550221	0.985	-10.2	2.153	1.402	ARG373, ALA374, ASN377, GLU378, LYS452, ARG497, LEU501, LEU504, MET525
ZINC000000985503	0.926	-9.8	5.52	2.891	GLU312, HIS386, ARG387, ILE389, LEU396, LYS435, LEU438
ZINC000058101934	0.837	-10.13	1.746	1.312	ARG373, ALA374, ASP450, ARG497, LEU504
ZINC000095464663	0.817	-12.3	3.743	2.208	ILE445, TYR468, LEU490, LEU493, PRO494
ZINC000003243391	0.709	-9.6	8.99	3.51	GLU378, ARG464, TYR468, PHE486, LEU490, LEU491, PRO494
ZINC000016130131	0.637	-8.2	4.489	2.429	ARG373, ALA374, ASN377, GLU378, LYS452, ARG497, LEU501, LEU504, MET525
ZINC000001042265	0.568	-7.4	1.746	1.312	ARG373, ALA374, ASP450, ARG497, LEU504
ZINC000031669066	0.526	-7.3	3.743	2.208	ILE445, TYR468, LEU490, LEU493, PRO494
rmsd/lb (root mean square deviation/upper bound); rmsd/lb(root mean square deviation/lower bound)

The compound ZINC000095464663 presented the highest binding affinity for LXR-α in the present virtual screening study, suggesting that it may be a potential candidate for developing a drug to target LXR-α. The binding affinity quantifies the interaction intensity between a ligand and its target protein. It assesses the ligand–protein complex’s stability, and a more negative value suggests a greater affinity for binding. This range encompasses the binding affinity of ZINC000095464663, ZINC000058101934, ZINC000003243391, ZINC000016130131, ZINC000001042265, and ZINC000031669066 is shown in Table 3. Out of all the ligands tested, 2D analysis of compound ZINC000095464663 has revealed that it interacts with SER383, HIS386, ARG387, ILE389, ASP390, LYS435, LEU438 and GLU308, GLU312. The amino acids involved in the interactions were vander waals, alkyl, pi-alkyl, amide-pi stacked, conventional hydrogen bond, and carbon hydrogen bond. The most frequent type of interaction among these was a conventional hydrogen bond, followed by pi-sulfur, amide-pi stacked, alkyl, and pi-alkyl interactions with GLU312, ILE389, LYS435, and LEU438. Fluorine and benzene atoms, which are widely used to enhance the properties of chemical compounds, are responsible for the majority of the interactions between the molecule and the protein. The following amino acids interact with these functional groups: ARG387, ILE389, GLU308, SER383, HIS386 and GLU312. The N-{2‐[(1R)‐2,2,2‐trifluoro‐1‐hydroxyethyl]phenyl group interacts with GLU308 and SER383, benzene group interacts with GLU312, ARG387, ILE389, and sulfonamide group interacts with HIS386 and LYS435 (Fig. 6)

Like the protocol for LXR-α, molecular docking evaluation suggested that AZ876 had the highest affinity for the amino acids of the LXR-β protein. Based on the structure of AZ876, 400 ligands were identified with similarity scores ranging from 0.94 to 0.354 (Suppl. Table 1). The name assigned by the ZINC database, the 2D and 3D structure, the similarity score, and the affinity towards LXR-β resulting from the docking process for the ten most promising compounds not used in pharmacotherapies are shown in Fig. 7. The compound ZINC000021912925 presented the highest binding affinity for LXR-β in the present virtual screening study, suggesting that it may be a potential candidate for developing a drug to target LXR-β due to a possible effective interaction with the target (Fig. 7 and Table 4). The binding affinities of compounds targeting LXR-β have been reported in the literature from in silico studies to range from − 8.1 kcal/mol to − 10.8 kcal/mol. In the present analysis, the binding affinity of all the top ten molecules falls within the range commonly found in the scientific literature (Fig. 8 and Table 4). The compound ZINC000021912925 has the highest affinity for LXR-β and binds to PHE271, MET312, PHE329, LEU345, and HIS435 amino acids (Fig. 7). Although the N-(2,3‑dimethylphenyl) group interacts with the amino acids PHE271, LEU345, and HIS435; the 2,3‐dihydro‑1lambda6,2‐thiazol group in the molecule contributes only one interaction with the protein (Fig. 8).

Table 4

Binding affinity of newly identified top ten ligands within the binding pocket and interacting amino acid of LXR-β.
Ligand	Similarity score	Binding Affinity (Kcal/mol)	rmsd/ub	rmsd/lb	Interacting amino acid
ZINC000021912925	0.816	-11.7	2.122	1.466	PHE272, SER274, ALA275, GLU281, MET312, THR316, ARG319, PHE329, LEU330, LEU345, ILE353
ZINC000021913093	0.795	-10.8	1.625	1.734	PHE268, LEU274, ALA275, LEU345, ILE353, HIS435
ZINC000021913098	0.787	10.03	1.325	1.724	LEU274, ALA275, LEU345, ILE353, HIS435
ZINC000021913127	0.600	-9.8	1.731	1.857	ALA275, LEU345, HIS435
ZINC000095446598	0.496	-9.8	2.702	1.642	PHE271, ALA275, ILE309, MET312, PHE329, PHE340, LEU345, HIS435
ZINC000036398658	0.485	-9.5	1.625	1.734	PHE268, LEU274, ALA275, LEU345, ILE353, HIS435
ZINC000037207255	0.48	-9.2	2.531	1.257	PHE272, LEU274, ALA275, LEU345, ILE353, HIS435
ZINC000040555976	0.47	-8.7	1.724	1.625	PHE268, LEU274, ALA275, LEU345, ILE353, HIS435
ZINC000014043132	0.447	-9.1	1.857	1.325	GLU281, MET312, THR316, ARG319, PHE329, LEU330, LEU345, ILE353
ZINC000019867701	0.443	-8.9	1.642	1.731	SER274, ALA275, GLU281, MET312, THR316, ARG319, PHE329
rmsd/lb (root mean square deviation/upper bound); rmsd/lb(root mean square deviation/lower bound)

In silico evaluation of the chemico-pharmacokinetic profile of the known and the newly identified compounds

To support potential candidate in drug discovery process, it is necessary to compute physicochemical descriptors and predict ADME parameters, pharmacokinetic properties, druglike nature, and medicinal chemistry of one or more small molecules. We assessed the chemico-pharmacokinetic profile of both known and newly identified compounds in silico, using the SwissADME. The results of chemico-pharmacokinetic parameters obtained after processing the data introduced into the platform is shown in Table 5 and Suppl. Tables 2, 3 and 4. Important features tested include physicochemical — Molecular Weight, Rotatable bonds, H-bond acceptors, H-bond donors, MR, TPSA; lipophilicity — iLOGP, XLOGP3, WLOGP, MLOGP, Silicos-IT Log P, Consensus Log P; water solubility — ESOL Log S, ESOL Solubility (mg/ml), ESOL Solubility (mol/l), ESOL Class, Ali Log S, Ali Solubility (mg/ml), Ali Solubility (mol/l), Ali Class, Silicos-IT LogSw, Silicos-IT Solubility (mg/ml), Silicos-IT Solubility (mol/l), Silicos-IT class; pharmacokinetics — GI absorption, BBB permeant, Pgp substrate, CYP1A2 inhibition, CYP2C19 inhibition, CYP2C9 inhibition, CYP2D6 inhibition, CYP3A4 inhibition, log Kp (cm/s); druglikeness — Lipinski violations, Ghose violations, Veber violations, Egan violations, Muegge violations, bioavailability score; medicinal chemistry — PAINS alerts, Brenk, alerts, Leadlikeness, violations, synthetic accessibility (Table 5 and Suppl. Tables 2, 3 and 4)

Table 5

Chemico-Pharmacokinetics features of T0901317, AZ876 and the newly identified molecule, determined in SwissADME
Features	T0901317	ZINC000095464663	AZ876	ZINC000021912925
Physicochemical Properties
MW	481.33	399.31	439.57	414.43
#Heavy atoms	31	26	31	29
#Aromatic heavy atoms	12	12	12	12
Fraction Csp3	0.29	0.2	0.38	0.15
#Rotatable bonds	8	6	5	7
#H-bond acceptors	12	9	3	6
#H-bond donors	1	2	1	1
MR	89.46	79.68	132.74	109.93
TPSA	65.99	74.78	78.1	118.23
Lipophilicity
iLOGP	2.84	1.92	3.7	2.83
XLOGP3	4.94	4.14	4.51	2.49
WLOGP	9.51	7.08	4.56	2.52
MLOGP	3.65	3.13	2.41	0.62
Silicos-IT Log P	3.94	3.26	2.81	1.73
Consensus Log P	4.98	3.91	3.6	2.04
Water Solubility
ESOL Log S	-5.69	-4.87	-5.36	-3.82
ESOL Solubility (mg/ml)	9.72E-04	5.39E-03	1.91E-03	6.24E-02
ESOL Solubility (mol/l)	2.02E-06	1.35E-05	4.33E-06	1.51E-04
ESOL Class	Moderately soluble	Moderately soluble	Moderately soluble	Soluble
Ali Log S	-6.06	-5.42	-5.87	-4.62
Ali Solubility (mg/ml)	4.16E-04	1.53E-03	5.91E-04	9.99E-03
Ali Solubility (mol/l)	8.65E-07	3.82E-06	1.34E-06	2.41E-05
Ali Class	Poorly soluble	Moderately soluble	Moderately soluble	Moderately soluble
Silicos-IT LogSw	-6.56	-6.06	-6.74	-5.64
Silicos-IT Solubility (mg/ml)	1.34E-04	3.48E-04	8.05E-05	9.43E-04
Silicos-IT Solubility (mol/l)	2.78E-07	8.71E-07	1.83E-07	2.28E-06
Silicos-IT class	Poorly soluble	Poorly soluble	Poorly soluble	Moderately soluble
Pharmacokinetics
GI absorption	Low	Low	High	High
BBB permeant	No	No	No	No
Pgp substrate	No	No	No	No
CYP1A2 inhibitor	No	No	No	No
CYP2C19 inhibitor	Yes	Yes	Yes	Yes
CYP2C9 inhibitor	Yes	Yes	Yes	Yes
CYP2D6 inhibitor	No	No	Yes	No
CYP3A4 inhibitor	Yes	Yes	Yes	Yes
log Kp (cm/s)	-5.73	-5.8	-5.78	-7.06
Druglikeness
Lipinski #violations	0	0	0	0
Ghose #violations	2	1	1	0
Veber #violations	0	0	0	0
Egan #violations	1	1	0	0
Muegge #violations	1	0	0	0
Bioavailability Score	0.55	0.55	0.55	0.55
Medicinal Chemistry
PAINS #alerts	0	0	1	0
Brenk #alerts	0	0	0	0
Leadlikeness #violations	3	2	2	1
Synthetic Accessibility	2.66	3.07	4.25	3.63

Molecular weight indicates that all four compounds, T0901317 (481.33), ZINC000095464663 (399.31), AZ876 (439.57), and ZINC000021912925 (414.43) may exhibit rapid absorption and easy elimination (Table 5). These compounds meet all of Lipinski's criteria for estimating a small drug molecule's oral bioavailability: molecular weight less than 500 g/mol, maximum number of rotatable bonds less than 10, maximum number of H-bond donors and acceptors, and LogP (octanol–water partition coefficient) less than 5 (Table 5). According to the Lipinski principles, small, lipophilic, or hydrophobic molecules with a limited number of hydrogen bond donors and acceptors are more likely to achieve good oral bioavailability because they can easily pass through the intestines' cellular membrane and enter the bloodstream [33–35]. However, GI absorption of T0901317 ZINC000095464663 is low, while for AZ876 and ZINC000021912925, it is high. All other pharmacokinetics parameters are similar for all these four molecules, only log Kp (cm/s) is a little higher for ZINC000021912925 (Table 5). Important features were found that can help with the framework's design so that the two newly discovered compounds can be used in the future.

Given their significance role in the lipids and cholesterol regulation, LXRs may have an impact on the emergence of metabolic diseases like hyperlipidemia and atherosclerosis. Our results of expression study of LXR-α and LXR-β both in normal and disease tissues, rerepeats their crucial role not only in normal physiological functions, but also regulation of numerous diseases including atherosclerosis [7, 36, 38, 39].Therefore, we looked through the literature to identify possible LXR modulators. We found AZ876, BMS-779788, BMS-852927, GSK3987, GW3965, T0901317, LXR-623, SR9243, 24(S)-hydroxycholesterol, 22(R)-hydroxycholesterol, and Oxycholesterol, all of which have been shown to have affinities for LXRs [13, 14, 20–23, 30, 36, 46]. Among these molecule T0901317 and AZ876 showed the highest affinity to LXR-α/ β as determined by molecular docking, virtual screening, and pharmacokinetic in silico strategies (Fig. 3, 4 and Table 5).

Using SwissSimilarity, SwissADME coupled Molecular docking analysis here we identified two compounds from the ZINC database with the highest affinity to the LXR (Fig. 5 and Fig. 7). This would turn drugs that are not currently used in medicine into drugs that may be used to treat cancer, atherosclerosis, and metabolic diseases. Key chemico-pharmacokinetics parameters, such as solubility, lipophilicity, permeability, and pharmacokinetics, are estimated by SwissADME using computational models and algorithms (Table 5). It provides estimates based on the molecule's chemical structure by using machine learning techniques and molecular descriptors. It is important to stress that in silico methods for evaluating drug ADME properties can be helpful in the early phases of drug development, but they should not be used in place of in vivo studies. Instead, these studies are meant to offer a quicker and less expensive way to examine the pharmacokinetic characteristics of small molecules [34].

Currently used in the investigation and creation of novel medications are computational technologies such as molecular docking, virtual screening, and pharmacokinetic studies [34, 35, 41, 45, 47, 48]. The most pertinent benefits of in silico studies include improving the capacity to develop and optimize new molecules, stressing understanding of drug-protein interactions, and predicting the binding affinity of putative active molecules to their target proteins using accurate molecular simulation models and analyses. Compared to traditional wet-lab experiments, they minimize costs and time while enabling the rapid evaluation of a large number of potential active molecule candidates [49]. The advantages produced by these techniques were also exploited in the present study, in which in silico studies on the interaction with three LXR isoforms were conducted, providing, through a comprehensive approach, two compounds with potential input for extensive laboratory studies regarding atherosclerosis for accurately determining their safety and efficacy profile [50, 51].

There are certain limitations on the kinds of assessments carried out in this study [50, 52, 53]. Molecular docking simulations have yielded valuable insights into compound-target interactions; however, experimental validation is still necessary to validate these results. The results of the experimental validation method may not always match the predictions of the simulations, and it can be costly and time-consuming. In contrast to proteins, which are highly dynamic in the biological environment and can drastically change their conformation during compound binding, molecular docking assessments concentrate on a single static interaction between the compound and its target protein. Molecular docking simulations have made many approximations, such as excluding other proteins from the cellular environment and ignoring the effects of solvents. These presumptions could lead to erroneous estimations and a decrease in the accuracy of the simulations.

These limitations enable improvements in the study design by opening research directions through the application of network pharmacology and molecular dynamics, which address the dynamic nature of proteins and possible interactions, the effect of solvents and ions, as well as simulating the presence of other cellular structures that may influence the interaction.

Based on their different patterns of expression, relationships between genes and diseases, and the enrichment of networks and pathways in both healthy and diseased tissues, LXR-α and LXR-β play divergent roles in regulating physiological cholesterol and lipids. Here, we identified the most promising ligand candidates that agonise LXR activity by conducting a thorough literature search and then performing a molecular docking analysis. In this case, we identified ten known and two new compounds to modulate LXRs with potential oral administration using SwissSimilarity and SwissADME coupled Molecular docking analysis. These findings support additional in vivo and in vitro research as well as the future development of novel LXR-targeted therapeutic agents.

Author contributions:

Sarder Arifuzzaman conceived the study, interpreted the data and wrote the manuscript. Zubair Khalid Labu, Md. Harun-Or-Rashid, Farhina Rahman Laboni, Mst. Reshma Khatun and Nargis Sultana Chowdhurywere involved in proofreading the manuscript.All of the authors read and approved the final manuscript.

Availability of data and materials

The datasets generated and/or analysed in this study are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

This work does not receive any external funds.

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No competing interests reported.

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Identification of Novel Compounds Targeting the Liver X Receptor (LXR): in-silico studies, screening, molecular docking, and chemico-pharmacokinetic analysis

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Abstract

Background

Method

Results

Conclusion

Figures

Background

Methods

mRNA and protein expression analysis of LXRs

Gene-disease association analysis

Gene network and pathway analysis

Identification and Selection of Target Proteins

Ligand Preparation

Protein Preparation

Molecular Docking Analysis

Virtual Screening

Chemico-Pharmacokinetic Profile Predictions

Results

Molecular docking approach targeting LXR-β

Virtual screening of the most promising compounds

In silico evaluation of the chemico-pharmacokinetic profile of the known and the newly identified compounds

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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