From Amphibian Extracts to Focused Libraries: An Algorithm-Based Strategy to Uncover Potent Multi-Target Anti-Alzheimer Peptides

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder, driving the search for multitarget-directed ligands (MTDLs) able to modulate multiple pathological pathways. Since amphibians represent a rich source of bioactive peptides, skin extracts from Scinax nasicus and Dendropsophus nanus, classified by sex, developmental stage, and season, were evaluated against key AD-related mechanisms, including cholinesterase inhibition, antioxidant activity, and Fe2+ chelation. Subsequently, a rational discovery workflow was implemented, integrating extract-level chemical characterization and bioactivity screening with the sequence identification of the main components and a novel in silico algorithm-based prioritization. This strategy, formalized through the Multi-Target Therapeutic Potential Score (MTTP-Score), served as a rational filter to produce a focused synthetic library, significantly minimizing experimental time and cost. Peptides derived from D. nanus (Dn) displayed the most promising multitarget candidates. Dn-7 combined sub-micromolar dual cholinesterase inhibition (IC50 = 0.32 and 1.2 µM for AChE and BChE, respectively) with highly efficient Fe2+ chelation (EC50 = 39 µM), representing the highest MTDL activity reported for an amphibian peptide. Dn-18 emerged as a moderate butyrylcholinesterase inhibitor with strong metal chelating capacity. Overall, this study establishes a pipeline for the rational discovery of peptide-based MTDL scaffolds for AD.