Structure-based virtual screening of Trachyspermum ammi metabolites targeting acetylcholinesterase for Alzheimer's disease treatment

Abstract

In recent decades, Alzheimer's disease (AD) has garnered significant attention due to its rapid global prevalence. The cholinergic hypothesis posits that the degradation of acetylcholine by acetylcholinesterase (AChE) contributes to AD development. Despite existing anti-AChE drugs, their adverse side effects necessitate new agents. This study analyzed 150 bioactive phytochemicals from Trachyspermum ammi using structure-based drug design and various in-silico tools to identify potent anti-AChE compounds. Compounds were screened for drug-likeness (QEDw ≥50%) and bioavailability (≥55%) and underwent toxicity profiling via the ProTox-II server. Selected compounds were prepared for molecular docking with the human AChE protein as the receptor. Viridifloral, 2-Methyl-3-glucosyloxy-5-isopropyl phenol, Alpha-Curcumene, and Sterol emerged as top candidates with high AChE affinity. These results were validated by molecular dynamics simulations, confirming stable interactions. The hit compounds were further evaluated for drug-likeness using Lipinski's rule and ADMET properties, confirming favorable pharmacokinetic profiles. DFT optimization analyzed frontier molecular orbitals and electrostatic potential, demonstrating favorable chemical reactivity and stability. This study suggests that these identified compounds could be novel nature-derived AChE inhibitors, potentially contributing to AD treatment. However, further in-vitro and in-vivo studies are necessary to confirm their efficacy in biological systems. Future research will focus on developing these compounds into safe and effective drugs to combat Alzheimer's disease.

Fig 1. Stepwise methods of the overall study.

Fig 2. DFT optimized structures of the hit compounds.

Fig 3

Docked conformer of Viridiflorol and Alpha-Curcumene interacting in the same binding pocket of receptor protein acetylcholinesterase: (A) Zoomed pose, (B) 3D depiction of non-bonding interaction with the key amino acid residues, (C) Hydrogen bond surface area.

Fig 4

(A) RMSD analysis of acetylcholinesterase complexes over a 200 ns MD simulation. The RMSD values are shown for curcumene (black), galantamine (red), sterol (green), and viridifloral (blue). (B) RMSD values of ligands within the selected complexes. (C) RMSF values for the four compounds complexed with acetylcholinesterase during the 200 ns MD simulation. (D) Rg graph for the complexes of curcumene, galantamine, sterol, and viridifloral during the 200 ns MD simulation, shown in black, red, green, and blue respectively.

Fig 5

(A) Number of hydrogen bonds contributing to the stability of the Galantamine, Sterol, and Viridifloral complexes over 100 ns. (B) Projection of Cα atoms in the essential subspace along the first two eigenvectors for the ligands Curcumene (black), Galantamine (red), Sterol (green), and Viridifloral (blue).

Fig 6. Free energy landscapes of four crystal structures during 200 ns MD simulation.

2D and 3D graphs projected on the first two principal components (PC1 + PC2). Blue spots indicate the energy minima. Gibbs free energy landscape obtained during 200 ns dynamics of (A) Curcumene (B) Galantamine, (C) Sterol and (D) Viridifloral.

Fig 7

DCCM of the Cα atoms plots for 200 ns of (A) curcumene, (B) galantamine, (C) sterol, and (D) viridifloral. Positive (red) and negative (blue) correlations are determined by analyzing the relative movements of the residues. A positive correlation indicates that the residues move in the same direction, while a negative correlation indicates that they move in opposite directions. Uncorrelated movement, represented by the color white, refers to residual movement that does not have an impact on the movement of others.

Fig 8

Binding free energy plot of (A) curcumene, (B) galantamine, (C) sterol, and (D) viridifloral.