Identification and Validation of YWHAZ Using AI Generated Prompts

Artificial intelligence–assisted prompting offers a transformative strategy for rapidly identifying and contextualizing biological targets. Using Swalife PromptStudio, we present a case study of YWHAZ (14-3-3ζ) which is a multifunctional signaling adaptor that integrates diverse cellular pathways, including PI3K/AKT, MAPK, apoptosis, and cell cycle regulation. Multi-omics profiling reveals consistent overexpression at transcriptomic and proteomic levels, with downstream metabolomic signatures of glycolysis, glutaminolysis, and redox balance. Pathway and network analyses position YWHAZ as a super-hub protein with high degree and betweenness centrality, linking kinase signaling, apoptosis, and metabolic modules. Genetic evidence highlights common regulatory variants associated with cancer and metabolic traits, alongside rare, deleterious LoF mutations linked to neurodevelopmental disorders, confirming essentiality. Collectively, YWHAZ emerges as a high-value but high-risk therapeutic target, best addressed through selective modulation of pathogenic interactions and biomarker-guided strategies in precision medicine. This study highlights the effectiveness of AI-driven prompt engineering as a scalable and reproducible framework for precision target prioritization.