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AI-powered discovery of a novel p53-Y220C reactivator
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 conformati...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10430779/ https://www.ncbi.nlm.nih.gov/pubmed/37593097 http://dx.doi.org/10.3389/fonc.2023.1229696 |
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author | Zhou, Shan Chai, Dafei Wang, Xu Neeli, Praveen Yu, Xinfang Davtyan, Aram Young, Ken Li, Yong |
author_facet | Zhou, Shan Chai, Dafei Wang, Xu Neeli, Praveen Yu, Xinfang Davtyan, Aram Young, Ken Li, Yong |
author_sort | Zhou, Shan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10430779 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104307792023-08-17 AI-powered discovery of a novel p53-Y220C reactivator Zhou, Shan Chai, Dafei Wang, Xu Neeli, Praveen Yu, Xinfang Davtyan, Aram Young, Ken Li, Yong Front Oncol Oncology 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. Frontiers Media S.A. 2023-08-01 /pmc/articles/PMC10430779/ /pubmed/37593097 http://dx.doi.org/10.3389/fonc.2023.1229696 Text en Copyright © 2023 Zhou, Chai, Wang, Neeli, Yu, Davtyan, Young and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Zhou, Shan Chai, Dafei Wang, Xu Neeli, Praveen Yu, Xinfang Davtyan, Aram Young, Ken Li, Yong AI-powered discovery of a novel p53-Y220C reactivator |
title | AI-powered discovery of a novel p53-Y220C reactivator |
title_full | AI-powered discovery of a novel p53-Y220C reactivator |
title_fullStr | AI-powered discovery of a novel p53-Y220C reactivator |
title_full_unstemmed | AI-powered discovery of a novel p53-Y220C reactivator |
title_short | AI-powered discovery of a novel p53-Y220C reactivator |
title_sort | ai-powered discovery of a novel p53-y220c reactivator |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10430779/ https://www.ncbi.nlm.nih.gov/pubmed/37593097 http://dx.doi.org/10.3389/fonc.2023.1229696 |
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