Specific Rosetta-based protein-peptide prediction protocol allows the design of novel cholinesterase inhibitor peptides.

The search for novel cholinesterase inhibitors is essential for advancing treatments for neurodegenerative disorders such as Alzheimer's disease (AD). In this study, we employed the Rosetta pepspec module, originally developed for designing peptides targeting protein-protein interactions, to design de novo peptides targeting the peripheral aromatic site (PAS) of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). A total of nine peptides were designed for human AChE (hAChE), T. californica AChE (TcAChE), and human BChE (hBChE). These peptides were synthesized using Fmoc-SPPS and tested in vitro using Ellman's reaction to evaluate their inhibitory potency. Peptide 11tA, designed for TcAChE, exhibited potent inhibition of hAChE (IC = 1.21 ± 0.25 µM) and demonstrated strong antioxidant activity against DPPH radicals and lipid peroxidation, making it a promising multitherapeutic candidate for AD. Peptide 11hB, designed for hBChE, showed the highest inhibitory activity against hBChE, with a K of 12.69 ± 1.27 µM, making it the most potent natural amino acid peptide reported against hBChE. The computational protocol effectively distinguished the specific characteristics of each enzyme target. Toxicity assessments, including hemolysis tests and A. salina lethality assays, revealed no toxic effects at low concentrations, further supporting the potential of these peptides for peptide-based drug development in AD. This study underscores the growing potential of peptides as alternatives to small-molecule drugs. It demonstrates that computational protocols for protein-protein interactions can be successfully adapted to design high-affinity peptide inhibitors.