The p53 gene is a tumor suppressor, meaning that its activation prevents tumor development by initiating apoptosis (growth arrest). Mutations in p53 are present in almost all tumor types, contributing to the intricate network of molecular processes that lead to tumor development. About 50% of all human cancers are caused by p53 mutations (Baugh et al. 2018).  PK11000 is an alkylating agent that maintains the DNA-binding domain of mutant p53 by covalent cysteine modification (Bauer et al. 2016).  The Y220C mutant, one of the most frequent p53 mutations, inactivates p53 by disrupting its overall structural integrity. While there is no effective treatment for the Y220C mutant, the novel drug PK11000 has been demonstrated to restore the mutant's structural stability without affecting its binding capacity. PK11000 works by binding to cysteine residues in p53 covalently. Since cysteine residues are found in many proteins, a medication that covalently binds to all cysteines would not be acceptable for human trials owing to the risk of unexpected side effects. Still, because it has been demonstrated to restore protein function in mice, it may be used as a model system to investigate how a medication could potentially mimic wildtype p53 protein dynamics. I will be presenting Molecular Dynamics (MD) simulations that help us to further understand PK11000. I also will be presenting results from the CPPTRAJ program designed to analyze MD simulation data, and processes MD trajectory formats (Roe et al. 2018). The implications from the obtained structural substates will be introduced. Furthermore, best-fit results will be discussed from 2DRMS calculations.

Machine learning of 2DRMS: Conformational analysis of p53

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.