Title : Inhibition of human cholinesterases by novel peptides designed using a protein–peptide specificity prediction protocol in Rosetta
Abstract:
The development of new cholinesterase inhibitors is crucial for advancing therapies for neurodegenerative diseases like Alzheimer's disease (AD). In this work, we utilized the Rosetta pepspec module, originally designed for peptide engineering in protein–protein interactions, to generate de novo peptides targeting the peripheral aromatic site (PAS) of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). A total of nine peptides were designed to interact with human AChE (hAChE), T. californica AChE (TcAChE), and human BChE (hBChE). These peptides were synthesized using Fmoc-SPPS and evaluated in vitro via Ellman’s assay to determine their inhibitory potency. Peptide 11tA, designed for TcAChE, exhibited strong inhibition of hAChE (IC50 = 1.21 ± 0.25 μM) and displayed significant antioxidant activity against DPPH radicals and lipid peroxidation, highlighting its potential as a multifunctional AD therapeutic. Peptide 11hB, optimized for hBChE, showed the strongest inhibitory effect on hBChE, with a Ki of 12.69 ± 1.27 μM, making it the most potent peptide composed of natural amino acids reported against hBChE. The computational design approach effectively captured key differences between enzyme targets. Toxicity evaluations, including hemolysis assays and A. salina lethality tests, indicated no toxicity at low concentrations, reinforcing the feasibility of these peptides for therapeutic development. This study highlights the increasing relevance of peptide-based drugs as viable alternatives to small molecules and demonstrates the successful adaptation of computational protein–protein interaction methodologies for designing high-affinity peptide inhibitors.
