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

Abstract

The search for novel cholinesterase inhibitors is essential for advancing treatments for neurodegenerative disorderssuch as Alzheimer’s disease (AD). In this study, we employed the Rosetta pepspec module, originallydeveloped for designing peptides targeting protein–protein interactions, to design de novo peptides targeting theperipheral aromatic site (PAS) of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). A total of ninepeptides 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 theirinhibitory potency. Peptide 11tA, designed for TcAChE, exhibited potent inhibition of hAChE (IC50 = 1.21 ±0.25 μM) and demonstrated strong antioxidant activity against DPPH radicals and lipid peroxidation, making it apromising multitherapeutic candidate for AD. Peptide 11hB, designed for hBChE, showed the highest inhibitoryactivity against hBChE, with a Ki of 12.69 ± 1.27 μM, making it the most potent natural amino acid peptidereported against hBChE. The computational protocol effectively distinguished the specific characteristics of eachenzyme target. Toxicity assessments, including hemolysis tests and A. salina lethality assays, revealed no toxiceffects at low concentrations, further supporting the potential of these peptides for peptide-based drug developmentin 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 todesign high-affinity peptide inhibitors.