Exploring the interactions of antihistamine with retinoic acid receptor beta (RARB) by molecular dynamics simulations and genome-wide meta-analysis

Abstract

Kaposi sarcoma (KS) is one of the most common AIDS-related malignant neoplasms, which can leave lesions on the skin among HIV patients. These lesions can be treated with 9-cis-retinoic acid (9-cis-RA), an endogenous ligand of retinoic acid receptors that has been FDA-approved for treatment of KS. However, topical application of 9-cis-RA can induce several unpleasant side effects, like headache, hyperlipidemia, and nausea. Hence, alternative therapeutics with less side effects are desirable. There are case reports associating over-the-counter antihistamine usage with regression of KS. Antihistamines competitively bind to H1 receptor and block the action of histamine, best known for being released in response to allergens. Furthermore, there are already dozens of antihistamines that are FDA-approved with less side effects than 9-cis-RA. This led our team to conduct a series of in-silico assays to determine whether antihistamines can activate retinoic acid receptors. First, we utilized high-throughput virtual screening and molecular dynamics simulations to model high-affinity interactions between antihistamines and retinoic acid receptor beta (RARβ). We then performed systems genetics analysis to identify a genetic association between H1 receptor itself and molecular pathways involved in KS. Together, these findings advocate for exploration of antihistamines against KS, starting with our two promising hit compounds, bepotastine and hydroxyzine, for experimental validation study in the future.

Keywords: Hit compound generation; Molecular dynamics simulation; Skin cancer; Systems genetics.

Figure 1.

Chemical moieties shared between 9-cis-retinoic acid (9-cis-RA) and several antihistamines (loratadine, fexofenadine, cetirizine, olopatadine, levocabastine, and hydroxyzine). Green faded boxes highlight hydrocarbon rings capable of van der Waals interactions, and orange faded boxes indicate carboxyl functional groups capable of forming hydrogen bonds. The molecular weights are varied but all within or close to 500 g/mol in accordance with Lipinski’s rule of five: 9-cis-RA (300.4 g/mol), Loratadine (382.9 g/mol), fexofenadine (501.7 g/mol), cetirizine (388.9 g/mol), olopatadine (337.4 g/mol), levocabastine (420.5 g/mol), and hydroxyzine (374.9 g/mol).

Figure 2.

Crystal structures of ligand binding domain (LBD) of retinoic acid receptors that were imported from the RCSB protein data bank for high-throughput virtual screening and molecular dynamics. All structures were prepared using Protein Preparation Wizard, Energy Minimization, and Loop Refinement tools in Maestro. RARα (PDB: 3KMR), RARβ (4JYI), RARγ (1FCY), RXRα (7A77), RXRβ (7A78), and RXRγ (7A79) had resolutions of 1.80, 1.90, 1.30, 1.50, 1.72, and 2.05 Å, respectively.

Figure 3.

Molecular docking of 9-cis-retinoic acid (9-cis-RA) to retinoic acid receptors. Docking scores indicate moderately strong binding, which is expected because 9-cis-RA is a pan-agonist of all 6 retinoic acid receptor types. Also note the presence of hydrogen bonding between the carboxylic acid group of 9-cis-RA and arginine or serine residue of retinoic acid receptors. This interaction is cited in multiple sources as one of the key binding interactions to promote high-affinity binding to retinoic acid receptors (16, 76-79).

Figure 4.

Predicted binding mode of top 4 scoring antihistamines with pharmacophore features of RARβ agonists: (a) Loratadine (b) Bepotastine (c) Dimetotiazine (d) Hydroxyzine. Yellow dotted lines represent hydrogen bonding or water bridges, whereas blue dotted lines represent π-π stacking interaction. Note the presence of hydrogen bond acceptors near ARG269 and SER280, which can be also found in the binding mode of 9-cis-RA with RARβ. However, dimetotiazine was the only molecule that was found outside the canonical binding site. In fact, neither ARG269 nor SER280 was found near dimetotiazine. Regardless, all the antihistamines displayed π-π stacking exchange with PHE295 via their aromatic rings, which is absent in 9-cis-RA because its chemical structure does not house an aromatic ring.

Figure 5.

Molecular Dynamics Root Mean Square Distance (RMSD) and Root Mean Square Fluctuation (RMSF) analyses of RARβ interacting with 9-cis-RA and antihistamines (N = 3). Note that standard deviation is not reported because each replicate produced identical results, which is not unusual in assays dictated by mathematics and programming. Green lines on RMSF plot indicate position of binding residues. RMSD is calculated with average change in displacement of a selection of atoms for a particular frame with respect to a reference frame. Except for dimetotiazine, the ligand RMSD equilibrated to roughly 1.5 Å towards the end of the simulation, which indicates strong binding between our protein of interest and the ligand. RMSF is useful for characterizing local changes along the protein chain, where peaks indicate areas of protein that fluctuates the most during simulation. Notice the similarity in the RMSF profile between RARβ-9-cis-RA and RARβ-antihistamine complexes. This suggests the structural changes in LBD of RARβ are nearly identical between 9-cis-RA and antihistamines.

Figure 6.

Interaction fraction and ligand interaction analyses of RARβ-9-cis-RA and RARβ-antihistamine complexes (N = 3). Note that standard deviation is not reported because each replicate produced identical results, which is not unusual in modeling studies dictated by mathematics and programming. Overall, the interaction fraction charts of bepotastine and hydroxyzine resembled that of 9-cis-RA. SER280 was the top binding force contributor for 9-cis-RA, bepotastine, and hydroxyzine with interaction fraction values greater than 1.0. Ligand interaction diagram analysis revealed extensive hydrogen bond networks involving SER280 and carboxylic acid groups. On the other hand, loratadine and dimetotiazine mainly displayed hydrophobic forces with interaction fraction values less than 1.0. In particular, dimetotiazine was the only molecule that was seen interacting with residues of helix 12, specifically ILE403, MET406, and MET407, via hydrophobic force and hydrogen bonding.

Figure 7.

Structural comparison of RARβ-9-cis-RA and RARβ-antihistamine complexes. The α-helices and β-sheets are overlaid nearly perfectly between RARβ-9-cis-RA and RARβ-antihistamine complexes. However, structural comparison of helix 12 with respect to loratadine and dimetotiazine revealed 2.3 Å shift away from their three-membered aromatic rings, suggesting steric hindrance. Meanwhile, structural comparison of helix 12 with respect to bepotastine and hydroxyzine revealed a minor angle change to bring MET407 closer to their benzene rings. Together, there was no steric clash with helix 12 observed in RARβ-bepotastine and RARβ-hydroxyzine.

Figure 8.

Visual representation of 3D protein-protein interaction network analyses of RARβ in mice (containing 512 nodes, 818 edges, and 293 seed genes) and HRH1 in mice (containing 397 nodes, 588 edges, and 233 seed genes). Each spherical node represents a protein. The nodes larger in size represent greater number of edges, and red color represents nodes that are important in the network.