4-Phthalimidobenzenesulfonamide Derivatives as Acetylcholinesterase and Butyrylcholinesterase Inhibitors: DFTs, 3D-QSAR, ADMET, and Molecular Dynamics Simulation.
Neurodegener Dis
; 22(3-4): 122-138, 2022.
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| MEDLINE
| ID: mdl-36288689
INTRODUCTION: Alzheimer's disease is a form of dementia which affects majority of the people. It is characterized by memory loss and other cognitive function disabilities and is one of the most challenging neurodegenerative disorders to treat because of its progressive nature. The disease affects millions of people all around the world, and the number of those affected is expanding every day. In the previous study, the 4-phthalimidobenzenesulfonamide derivatives were synthesized as AChE and BChE inhibitors, and here, we were aiming to further reporting in silico studies of these compounds for efficient drug discovery process and to find out the potential lead compounds. METHODS: In silico characterization included density functional theory (DFT) studies, 3D-QSAR, ADMET properties, molecular docking, and molecular dynamic simulations. The geometries of all derivatives were optimized using B3LYP method and 6-311G basis set. RESULTS: The findings of the current study revealed that 4-phthalimidobenzenesulfonamide derivatives exhibited a reactive electronic property which is essential for anticholinesterase activity. Moreover, optimized structures were subjected to molecular docking studies with targeted protein. The compounds 2c and 2g showed excellent binding score of -37.44 and -33.67 kJ/mol with BChE and AChE, respectively, and exhibited strong binding affinity. The potent derivatives produced stable complex with amino acid residues of active pocket of both BChE and AChE. The stability of protein-ligand complexes was determined by molecular dynamic simulation studies, and results were found in correlation with molecular docking findings. CONCLUSION: Findings of the current study suggested that these derivatives are potent inhibitors of cholinesterase enzyme.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Neurodegener Dis
Asunto de la revista:
NEUROLOGIA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Pakistán