Exploring the therapeutic mechanisms of millet in obesity through molecular docking, pharmacokinetics, and dynamic simulation

Abstract

Obesity, a prevalent global health concern, is characterized by excessive fat accumulation, which confers significant nutritional and health risks, including a shortened lifespan and diminished wellbeing. Central to the regulation of energy balance and food intake is the fat mass and obesity-associated (FTO) protein, which modulates the interplay between caloric consumption and energy expenditure. Given its pivotal role in obesity regulation, the identification of effective inhibitors targeting the FTO protein is imperative for developing therapeutic interventions. Currently available anti-obesity drugs are often plagued by undesirable side effects. In contrast, natural plant-derived bioactive compounds are gaining prominence in the pharmaceutical industry due to their efficacy and lower incidence of adverse effects. Little Millet, a traditional cereal known for its rich nutritional profile and high satiety index, was investigated in this study using molecular docking and dynamics simulation approach for its potential as an anti-obesity agent. Our research demonstrates that four bioactive compounds from Little Millet exhibit superior binding energies ranging from 7.22 to 8.83 kcal/mol, compared to the standard anti-obesity drug, orlistat, which has a binding energy of 5.96 kcal/mol. These compounds fulfilled all drug-like criteria, including the Lipinski, Ghose, Veber, Egan, and Muegge rules, and exhibited favorable profiles in terms of distribution, metabolism, and prolonged half-life without toxicity. Conversely, orlistat was associated with hepatotoxicity, a reduced half-life, and multiple violations of drug-likeness parameters, undermining its efficacy. Molecular dynamics simulations and Gibbs free energy assessments revealed that the four identified compounds maintain stable interactions with key residues in the FTO protein's active site. We propose further validation through extensive In vitro, In vivo, and clinical studies to ascertain the therapeutic potential of these compounds in combating obesity.

Keywords: ADMET; binding free energy; human fat mass and obesity associated protein (FTO); little millet; molecular docking and molecular dynamics (MD) simulation; obesity.

Figure 1

Graphic/Circos representation of potential bioactive compounds in little millet decipited with their hydrogen bonds and binding energy.

Figure 2

Molecular docking of luteolin to binding site of 4IDZ (A) molecular docking complex, (B) 3D representation for amino acid residues involved in hydrogen bond donor acceptor binding of luteolin with 4IDZ, (C) 2D representation of interaction type of 18,19-Secoyohimban 19-oic acid with surrounding amino acids of 4IDZ.

Figure 3

Molecular docking of Naringenin binding site of 4IDZ (A) molecular docking complex, (B) 3D representation for amino acid residues involved in hydrogen bond donor acceptor binding of naringenin with 4IDZ, (C) 2D representation of interaction type of naringenin with surrounding amino acids of 4IDZ.

Figure 4

Molecular docking of quercetin binding site of 4IDZ (A) molecular docking complex, (B) 3D representation for amino acid residues involved in hydrogen bond donor acceptor binding of quercetin with 4IDZ, (C) 2D representation of interaction type of quercetin with surrounding amino acids of 4IDZ.

Figure 5

Molecular docking of atropine to binding site of 4IDZ (A) molecular docking Complex, (B) 3D representation for amino acid residues involved in hydrogen bond donor acceptor binding of atropine with 4IDZ, (C) 2D representation of interaction type of atropine with surrounding amino acids of 4IDZ.

Figure 6

Molecular docking of standard drug orlistat to binding site of 4IDZ (A) molecular docking complex, (B) 3D representation for amino acid residues involved in hydrogen bond donor acceptor binding of orlistat with 4IDZ, (C) 2D representation of interaction type of orlistat with surrounding amino acids of 4IDZ.

Figure 7

Bioavailibity radar prediction for compounds compounds (A) and (B) luteolin, (C) and (D) naringenin, (E) and (F) quercetin, (G) and (H) atropine, and (I) and (J) orlistat using the SwissADME.

Figure 8

Protein–ligand root-mean-square deviation (RMSD) plot of (A) luteolin, (B) Naringenin, (C) quercetin, (D) atropine, and (E) orlistat bound to the inhibitory site of 4IDZ protein.

Figure 9

Stability of the 4IDZ secondary structure over 100 ns of MD simulation when complexed with (A) luteolin, (B) naringenin, (C) quercetin, (D) atropine, and (E) standard drug orlistat. Protein secondary structure elements (SSE) like alpha-helices (orange color) and beta-strands (light blue color) were monitored during the simulation.

Figure 10

Histogram of the protein–ligand complex of through hydrogen bonds, hydrophobic bonds, ionic bonds, and water bridges exhibited by (A) luteolin, (B) naringenin, (C) quercetin, (D) atropine, and (E) standard drug orlistat to 4IDZ protein.

Figure 11

Timeline representation for 100 ns simulation run analysis of (A) luteolin, (B) naringenin, (C) quercetin, (D) atropine, and (E) standard drug orlistat to 4IDZ protein.

Figure 12

Fluctuation properties of ligands with the complexes of 4IDZ protein formed with (A) luteolin, (B) naringenin, (C) quercetin, (D) atropine, and (E) standard drug orlistat using radius of gyration (Rg), molecular surface area (MolSA), solvent accessible surface area (SASA), and polar surface area (PSA).

Figure 13

Torsional analysis of ligand-4IDZ conformations during 100 ns simulations (A) luteolin, (B) naringenin, (C) quercetin, (D) atropine, and (E) standard drug orlistat.