| dc.creator |
Al-Mutairi, Aamal A. |
|
| dc.creator |
MUHAMMED, Muhammed Tılahun |
|
| dc.creator |
Hameed, Huma |
|
| dc.creator |
Zaki, Magdi E. A. |
|
| dc.creator |
Javed, Sadia |
|
| dc.creator |
Irfan, Ali |
|
| dc.creator |
Zahoor, Ameer Fawad |
|
| dc.creator |
Al-Hussain, Sami A. |
|
| dc.creator |
Boulaamane, Yassir |
|
| dc.creator |
Maurady, Amal |
|
| dc.creator |
Shahzadi, Irum |
|
| dc.creator |
Ahmad, Sajjad |
|
| dc.date |
2024-01-01T00:00:00Z |
|
| dc.date.accessioned |
2025-02-25T10:21:38Z |
|
| dc.date.available |
2025-02-25T10:21:38Z |
|
| dc.identifier |
4d6fcc49-ce35-4954-b374-68cdbcfe832f |
|
| dc.identifier |
10.3389/fchem.2024.1449165 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/4d6fcc49-ce35-4954-b374-68cdbcfe832f/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/99645 |
|
| dc.description |
Monoamine oxidase B (MAO-B) plays a pivotal role in the deamination process of monoamines, encompassing crucial neurotransmitters like dopamine and norepinephrine. The heightened interest in MAO-B inhibitors emerged after the revelation that this enzyme could potentially catalyze the formation of neurotoxic compounds from endogenous and exogenous sources. Computational screening methodologies serve as valuable tools in the quest for novel inhibitors, enhancing the efficiency of this pursuit. In this study, 43 acefylline derivatives were docked against the MAO-B enzyme for their chemotherapeutic potential and binding affinities that yielded GOLD fitness scores ranging from 33.21 to 75.22. Among them, five acefylline derivatives, namely, MAO-B14, MAO-B15, MAO-B16, MAO-B20, and MAO-B21, displayed binding affinities comparable to the both standards istradefylline and safinamide. These derivatives exhibited hydrogen-bonding interactions with key amino acids Phe167 and Ile197/198, suggesting their strong potential as MAO-B inhibitors. Finally, molecular dynamics (MD) simulations were conducted to evaluate the stability of the examined acefylline derivatives over time. The simulations demonstrated that among the examined acefylline derivatives and standards, MAO-B21 stands out as the most stable candidate. Density functional theory (DFT) studies were also performed to optimize the geometries of the ligands, and molecular docking was conducted to predict the orientations of the ligands within the binding cavity of the protein and evaluate their molecular interactions. These results were also validated by simulation-based binding free energies via the molecular mechanics energies combined with generalized Born and surface area solvation (MM-GBSA) method. However, it is necessary to conduct in vitro and in vivo experiments to confirm and validate these findings in future studies. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Computational exploration of acefylline derivatives as MAO-B inhibitors for Parkinson’s disease: insights from molecular docking, DFT, ADMET, and molecular dynamics approaches |
|
| dc.type |
info:eu-repo/semantics/article |
|