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AI-powered discovery of a novel p53-Y220C reactivator

Shan Zhou, Dafei Chai, Xu Wang, Praveen Neeli, Xinfang Yu, Aram Davtyan, Ken H. Young, Yong Li

2023Frontiers in Oncology14 citationsDOIOpen Access PDF

Abstract

Introduction The p53-Y220C mutation is one of the most common mutations that play a major role in cancer progression. Methods In this study, we applied artificial intelligence (AI)-powered virtual screening to identify small-molecule compounds that specifically restore the wild-type p53 conformation from p53-Y220C. From 10 million compounds, the AI algorithm selected a chemically diverse set of 83 high-scoring hits, which were subjected to several experimental assays using cell lines with different p53 mutations. Results We identified one compound, H3, that preferentially killed cells with the p53-Y220C mutation compared to cells with other p53 mutations. H3 increased the amount of folded mutant protein with wild-type p53 conformation, restored its transcriptional functions, and caused cell cycle arrest and apoptosis. Furthermore, H3 reduced tumorigenesis in a mouse xenograft model with p53-Y220C -positive cells. Conclusion AI enabled the discovery of the H3 compound that selectively reactivates the p53-Y220C mutant and inhibits tumor development in mice.

Topics & Concepts

MutantMutationCarcinogenesisApoptosisP53 proteinCell cycle checkpointCell cultureVirtual screeningCancer researchWild typeBiologyChemistryCell cycleComputational biologyDrug discoveryCell biologyMolecular biologyCancerGeneticsBiochemistryGeneGenetics, Bioinformatics, and Biomedical ResearchCancer-related Molecular PathwaysMachine Learning in Bioinformatics
AI-powered discovery of a novel p53-Y220C reactivator | Litcius