Abstract
The dopamine D2 receptor (PDB ID: 6CM4) plays a crucial role in the pharmacological action of
antipsychotic drugs, distinguishing between atypical and typical antipsychotic interactions. In this
study, we investigated the binding interaction of Haloperidol (GMJ) with the D2 receptor using
molecular docking via Auto Dock Vina. The receptor structure was processed by removing
unnecessary chains, ligands, and water molecules while adding polar hydrogens and appropriate
charges. Similarly, the ligand was optimized by assigning polar hydrogens and torsions. A docking
grid was defined at coordinates (center_x = -0.274, center_y = -0.120, center_z = 0.082) with a box
size of 60 × 60 × 60.
Docking results revealed that Haloperidol exhibited a strong binding affinity with the D2 receptor,
with the best binding mode showing energy of -11.1 kcal/mol. The top five docking poses consistently
displayed high affinity, with affinities ranging from -11.1 to -10.0 kcal/mol. Structural alignment in
PyMOL indicated a root mean square deviation (RMSD) of 0.905 Å after iterative rejection of
misaligned atoms, suggesting a reliable binding pose. Notably, the docked ligand maintained a
spatial alignment close to the original ligand, reinforcing the validity of the docking approach.
These findings provide valuable insights into the molecular interactions of Haloperidol with the D2
receptor, highlighting its strong binding potential. This study contributes to a deeper understanding
of antipsychotic drug-receptor interactions, which may aid in drug design and optimization efforts for
neuropsychiatric treatments.
Keywords: Dopamine D2 receptor, Haloperidol, Molecular docking, Auto Dock Vina, Drug-receptor
interaction, Binding affinity, Antipsychotic drugs
Supplementary weblinks
Title
Molecular Docking of 6CM4 Receptor with GMJ Ligand using AutoDock Vina & PyMOL
Description
This project focuses on the molecular docking analysis of the 6CM4 dopamine receptor with the GMJ ligand using AutoDock Vina. The docking results are analyzed and visualized using PyMOL.
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