DOI: 10.1073/pnas.2535979123 ISSN: 0027-8424

AI-driven PROTAC design overcomes oncogenic resilience by eliminating the CLIP1–LTK fusion protein

Shicheng Chen, Haiting Duan, Sheng Zhong, Jingxuan Ge, Huifeng Zhao, Yuanyi Ye, Huiyong Sun, Dan Li, Yu Kang, Xiaowu Dong, Jinxin Che, Tingjun Hou, Peichen Pan

The discovery of CAP-Gly domain-containing linker protein 1(CLIP1)–Leukocyte tyrosine kinase (LTK) as an oncogenic fusion reveals a unique dependency not only on LTK kinase activity but also on CLIP1-mediated multimerization, a noncatalytic function that drives oncogenic signaling. While this fusion is currently targeted with anaplastic lymphoma kinase inhibitors, their exclusive focus on kinase inhibition leaves the scaffolding function intact, necessitating a complete protein clearance strategy. Here, we report the AI-guided development of a first-in-class proteolysis-targeting chimera (PROTAC) designed to selectively degrade the CLIP1–LTK fusion protein. By integrating deep learning models for ternary complex prediction with structure-based molecular optimization, we designed DCL05, an orally bioavailable degrader of CLIP1–LTK fusion protein, achieving picomolar degradation potency (DC 50 = 40 pM) and robust antitumor activity. DCL05 consistently outperformed existing kinase inhibitors across a broad spectrum of LTK resistance-associated mutations, both in vitro and in vivo. Collectively, our study explores resistance-associated contexts of LTK and establishes a structure-guided PROTAC development pipeline, providing a promising therapeutic strategy for overcoming acquired resistance in kinase-driven cancers.

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