Cholinesterases inhibition profiles with Ugi-derived peptidemimetics: A combined experimental and computational study
Alma Ramić1, Toni Divjak1, Lucija Hadrović1
1Department of Chemistry, Faculty of Science, University of Zagreb, Horvatovac 102a, Zagreb, Croatia.
Background And Purpose:
Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are crucial enzymes implicated in various neurological disorders, including Alzheimer's disease. Developing selective inhibitors for either enzyme is one of the key therapeutic strategies. This study aimed to synthesize and evaluate a novel series of peptidomimetics for their ability to inhibit both human AChE and BChE, with a focus on identifying compounds exhibiting joint inhibitory activity or selectivity for BChE.
Experimental Approach:
Eleven peptidomimetics were synthesized using the Ugi four-component reaction. In vitro enzyme inhibition assays were performed to determine the dissociation constants (K i) for both human AChE (hAChE) and human BChE (hBChE). Principal component analysis was employed to analyse the inhibition data and map compound selectivity. To investigate the molecular interactions between the peptidomimetics and the BChE active site, quantum-chemical docking simulations were conducted.
Key Results:
All synthesized compounds exhibited reversible, micromolar inhibition of both hAChE and hBChE. Two compounds demonstrated significant BChE selectivity, with 279- and 169-fold higher preference for BChE, respectively. Principal component analysis revealed distinct clusters correlating with preferential binding to either enzyme. Docking simulations supported these findings, highlighting key stabilizing interactions (primarily π-π stacking) between the peptidomimetics and BChE, explaining superior selective and joint inhibitory activity.
Conclusion:
This work demonstrates the successful synthesis and characterization of novel peptidomimetics with varying degrees of hAChE and hBChE inhibition, including compounds with notable BChE selectivity. The combination of experimental data and computational modelling provides valuable insights into the structural basis of enzyme inhibition and establishes a foundation for rational design of more potent and selective cholinesterase inhibitors based on the designed scaffold.
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