• Period: July 2026 ~ Present

• Education

Mar 2022 ~ Present B.S., Convergent Bioscience and Informatics, Chungnam National University

Sep 2024 ~ Present B.S., Bio-AI Interdisciplinary Major (Double Major), Chungnam National University

• Project

(1) Deep Learning-Driven Design of a Cyclic Peptide Inhibitor Targeting the FOXO4–p53 Interaction

• Summary

My research focuses on the AI-driven design of cyclic peptide inhibitors targeting the FOXO4–p53 interaction, a central survival axis of senescent cells. FOXO4 is upregulated during senescence and retains p53 in the nucleus, preventing p53 from initiating apoptosis; blocking this interaction selectively eliminates senescent cells and has shown benefit across multiple age-related disease models. A major obstacle to rational design is that this interface forms between two intrinsically disordered regions and remains dynamic even in the bound state. To address this, I use AI-based structure prediction to describe the complex as a structural ensemble rather than a single fixed structure, and to identify the residues that consistently mediate binding. These residues then guide the design of cyclic peptides that pre-organize the binding conformation, and the resulting candidates will be validated both computationally and experimentally, with the goal of developing a next-generation senolytic peptide.

• Contact: jaemin5257@gmail.com