Elucidating the role of phytocompounds from Brassica oleracea var. italic (Broccoli) on hyperthyroidism: an in-silico approach

Abstract

Thyroid hormone (TH) plays a crucial role in regulating the metabolism in every cell and all organs in of the human body. TH also control the rate of calorie burning, body weight, and function of the heartbeat. Therefore, the aim of the present study is to investigate the role of phytocompounds from Brassica oleracea var. italic (Broccoli) against irregularities of TH biosynthesis (hyperthyroidism) through in silico molecular modelling. Initially, the genetic network was built with graph theoretical network analysis to find the right target to control excessive TH production. Based on the network analysis, the three-dimensional crystal structure of the mammalian enzyme lactoperoxidase (PDB id: 5ff1) was retrieved from the protein data bank (PDB), and the active site was predicted using BIOVIA Discovery studio. Sixty-three phytocompounds were selected from the IMPPAT database and other literature. Selected sixty-six phytocompounds were docked against lactoperoxidase enzyme and compared with the standard drug methimazole. Based on the docking scores and binding energies, the top three compounds, namely brassicoside (- 10.00 kcal × mol^-1), 24-methylene-25-methylcholesterol (- 9.50 kcal × mol^-1), 5-dehydroavenasterol (- 9.40 kcal × mol^-1) along with standard drug methimazole (- 4.10 kcal × mol^-1) were selected for further ADMET and molecular dynamics simulation analysis. The top-scored compounds were for their properties such as ADMET, physicochemical and drug-likeness. The molecular dynamics simulation analyses proved the stability of lactoperoxidase-ligand complexes. The intermolecular interaction assessed by the dynamic conditions paved the way to discover the bioactive compounds brassicoside, 24-methylene-25-methylcholesterol, and 5-dehydroavenasterol prevent the excessive production of thyroid hormones.

Supplementary information: The online version contains supplementary material available at 10.1007/s40203-023-00180-2.

Keywords: Broccoli; Hyperthyroidism; Lactoperoxidase; Molecular docking; Molecular dynamics simulation.

Fig. 1

The signaling pathway of Thyroid hormone synthesis

Fig. 2

Depicted the interaction between the compound brassicoside and LPO. The left side shows the 3D (a) and the right side representing the 2D complex of the LPO–brassicoside interaction (b). Depicted the interaction between the compound 24-methylene-25-methylcholesterol and LPO. The left side representing the 3D (c) and the right side representing the 2D complex of the LPO–24-methylene-25-methylcholesterol (d). Depicted the interaction between the compound 5-dehydroavenasterol and LPO. The left side represents the 3D (e) and the right side represents the 2D complex of the LPO–5-dehydroavenasterol interaction (f). Presented the interaction between the compound methimazole and LPO. The left side represents the 3D (g) and the right side represents the 2D complex of the LPO–methimazole interaction (h)

Fig. 3

Bioavailability radar plot for oral bioavailability of selected bioactive compounds. brassicoside (a), 24-methylene-25-methylcholesterol (b), 24-methylene-25-methylcholesterol (c), methimazole (d). The pink area exhibits the optimal range for each properties (Lipophilicity as XLOGP3 between − 0.7 and + 5.0; Size as molecular weight between 150 and 500 g mol⁻¹; Polarity as TPSA (topological polar surface area) between 20 and 130 Ų; Insolubility in water by log S scale not higher than 6; Insaturation as per fraction of carbons in the sp3 hybridization not less than 0.25 and Flexibility as per rotatable bonds no more than 9)

Fig. 4

The EGG-BOILED model for the selected bioactive and standard methimazole. The EGG-BOILED represents for intuitive evaluation of passive gastrointestinal absorption (HIA) white part and brain penetration (BBB) yellow part as well as substrates (PGP +) and non-substrates (PGP–) of the permeability glycoprotein (PGP) are represented by blue and red color circles, respectively, of the selected bioactive compound and mathimazole inhibitor in the WLOGP-versus-TPSA graph. The grey region is the physicochemical space of compounds predicted to exhibit high intestinal absorption

Fig. 5

RMSD study plot for 50 ns MD Simulation of LPO-APO (Black), LPO- brassicoside (Red), LPO-24-methylene-25-methylcholesterol (Green), LPO-24-methylene-25-methylcholesterol (Blue), and LPO-Methimazole (standard) (Yellow)

Fig. 6

RMSF study plots for 100 ns MD Simulation. Chain of LPO-APO (Black), LPO- brassicoside (Red), LPO-24-methylene-25-methylcholesterol (Green), LPO-24-methylene-25-methylcholesterol (Blue), and LPO-Methimazole (standard) (Yellow)

Fig. 7

Radius of gyration study plot for 100 ns MD Simulation of LPO-APO (Black), LPO- brassicoside (Red), LPO-24-methylene-25-methylcholesterol (Green), LPO-24-methylene-25-methylcholesterol (Blue), and LPO-Methimazole (standard) (Yellow)

Fig. 8

Solvent accessible surface area study plot for 100 ns MD Simulation of LPO-APO (Black), LPO- brassicoside (Red), LPO-24-methylene-25-methylcholesterol (Green), LPO-24-methylene-25-methylcholesterol (Blue), and LPO-Methimazole (standard) (Yellow)

Fig. 9

Intermolecular hydrogen bonding study plot for 50 ns MD Simulation of LPO- brassicoside (Red), LPO-24-methylene-25-methylcholesterol (Green), LPO-24-methylene-25-methylcholesterol (Blue), and LPO-Methimazole (standard) (Yellow)