Systematic discovery of disease-modifying targets by prediction from knowledge graph-based AI model and experimental validation: Parkinson’s disease case

expression was elevated in neuromelanin-positive dopaminergic neurons in late-stage PD, and its knockdown increased α-synuclein aggregation, suggesting a protective role in α-synuclein homeostasis. Structural modeling of AlphaFold-Multimer further revealed a substrate-like interface between α-synuclein and the TPP1 catalytic triad, consistent with a potential proteolytic mechanism of α-synuclein clearance. Together, these findings identify TPP1 as a previously underappreciated and mechanistically plausible PD DMT target and demonstrate how static knowledge graphs can be transformed into interpretable, disease-focused target discovery systems. By integrating explainable subgraph structures with enrichment-based re-prioritization, this framework provides a generalizable strategy for therapeutic target identification across indications.