In Methods in enzymology
Mutation of p53 is the most common genetic alteration in human cancer. The vast majority of p53 mutations found in cancer are missense mutations, with some single nucleotide point mutations leading to the accumulation of mutant p53 protein with potential gain of oncogenic function. The mechanism for stabilization and accumulation of missense mutant p53 protein in malignant cells is not fully understood. It is thought that DNAJA1 plays a crucial role as a co-chaperone protein by stabilizing mutant p53 and amplifying oncogenic potential. As such, identifying small molecule inhibitors to disrupt the protein-protein interaction between mutant p53 and DNAJA1 may lead to an effective treatment for preventing carcinogenesis. Studying protein-protein interactions and identifying potential druggable hotspots has historically been limited-protein-protein binding sites require more complex characterization than those of single proteins and the crystal structures of many proteins have not been identified. Due to these issues, identifying salient druggable targets in protein-protein interactions through bench research may take years to complete. However, in silico modeling approaches allow for rapid characterization of protein-protein interfaces and the druggable binding sites they contain. In this chapter, we first review the oncogenic potential of mutant p53 and the crucial role of DNAJA1 in stabilizing missense mutant p53. We then detail our methodology for using in silico modeling and molecular biology to identify druggable protein-protein interaction sites/pockets between mutant p53 and DNAJA1. Finally, we discuss screening for and validating the utility of a small molecule inhibitor identified through our in silico framework. Specifically, we describe GY1-22, a unique compound with activity against mutant p53 that demonstrates therapeutic potential to inhibit cancer cell growth both in vivo and in vitro.
Jacobsen Danielle, Bushara Omar, Mishra Rama K, Sun Leyu, Liao Jie, Yang Guang-Yu
Cochaperone, DNAJA1, Homology model, Interacting pocket, Machine learning, Missense mutation, Oncogenesis, Protein–protein docking, p53