AI驱动的从头设计靶向突变型p53肽-MHC-I复合物的微型蛋白用于癌症免疫治疗

Genetic alterations in the tumor suppressor p53 occur in nearly every type of human cancer, with mutation rates ranging from 10% to nearly 100%, averaging about 50% in cancer patients. Over 80% of these mutations are missense mutations in the p53 DNA-binding domain (DBD), with the p53-R175H mutation being the most common "hot spot," occurring in approximately 5% of p53-mutated cases. Clinically, p53-R175H mutation in cancer patients are significantly associated with decreased overall survival compared to cancer patients with wild-type p53. Biologically, missense mutations in the p53 gene, particularly "hot-spot" mutations, including p53-R175H, result in the high expression of mutant p53 protein in cancer cells. These features suggest that expression of mutant p53-R175H protein could serve as an attractive therapeutic target. However, targeting the intracellular oncoprotein mutant p53-R175H has proven extremely challenging and remains a major unmet need in cancer therapy. Interestingly, the mutant p53-R175H protein can be processed by the proteasome into a 9-mer peptide, presented on the cancer cell surface by human leukocyte antigen (HLA)-A*02:01, a common major histocompatibility complex (MHC) class I allele in the U.S. population (40%), providing a “unique” target for immunotherapy by harnessing immune cells-such as T cells-to eliminate cancer cells carrying mutant p53R-175H. Traditional efforts to develop T cell receptors (TCRs) or monoclonal antibodies (mAbs) against the peptide-MHC-I complex have largely failed due to low affinity, limited specificity, and multi-year development timelines. Recent advances demonstrate that artificial intelligence (AI)-designed miniproteins (less than 150 amino acids) targeting peptide-MHC class I complexes can be engineered into chimeric antigen receptor (CAR)-T cells, enabling robust T-cell activation and potent recognition and elimination of tumor cells. To accelerate the discovery of miniprotein-mediated therapeutics, we have developed and integrated multiple AI-driven platforms for de novo design of p53-R175H peptide-MHC-I-complex binding miniproteins, including diverse backbone generation, protein sequence optimization, and enhanced high-throughput computational screening of peptide-MHC-I-miniprotein complexes. Top candidates have undergone, or are currently undergoing, experimental validation as engineered T/NK cell engagers and CAR-T/NK cells to kill cancer cells. In summary, our integrated AI-driven framework enables rapid and personalized cancer immunotherapy by transforming previously “undruggable” intracellular oncoproteins-including, but not limited to, mutant p53-into actionable immunotherapeutic targets.