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<p>Glioblastoma (GBM) is an aggressive brain tumor with a poor prognosis, frequently associated with TP53 mutations that impair the tumor suppressor function of p53. This study employed a comprehensive computational approach to investigate the structural and dynamic consequences of TP53 missense mutations in GBM. Using data from the GDC portal for variant identification and PolyPhen-2 for pathogenicity prediction, we analyzed mutation effects through conservation studies, stability predictions (DDMut), and molecular dynamics simulations (CABS-flex v2.0). Our analysis identified eleven destabilizing mutations (Y205S, I255S, I195T, Y205H, T256P, Y163C, P151H, R158H, T155P, R156G, and G266E) with predicted stability changes (ΔΔG ≤ -2.0&#xa0;kcal mol-1), most located in the DNA-binding domain. Molecular dynamics simulations revealed distinct flexibility patterns, with Y205H showing the most significant deviation from wild-type p53, while Y163C maintained near-normal flexibility. Based on these findings, we propose a dynamics-based framework for TP53 variants according to their dynamic properties. These results demonstrate that TP53 mutations in GBM primarily affect protein stability and dynamics in the DNA-binding domain, likely contributing to oncogenesis through diverse mechanisms. This structural insight into p53 dysfunction in GBM provides a foundation for developing targeted therapeutic strategies against this devastating disease.</p>

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Computational analysis of structural and dynamic impacts of TP53 missense mutations in glioblastoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月8日

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  • Noora A. Hadi,
  • Hanaa Al-Mahmoodi,
  • Ibtihal Al-Shamarti,
  • Ali Z. Al-Saffar,
  • Ghassan M. Sulaiman,
  • Hamdoon A. Mohammed,
  • Jameel R. Al-Obaidi

摘要

Glioblastoma (GBM) is an aggressive brain tumor with a poor prognosis, frequently associated with TP53 mutations that impair the tumor suppressor function of p53. This study employed a comprehensive computational approach to investigate the structural and dynamic consequences of TP53 missense mutations in GBM. Using data from the GDC portal for variant identification and PolyPhen-2 for pathogenicity prediction, we analyzed mutation effects through conservation studies, stability predictions (DDMut), and molecular dynamics simulations (CABS-flex v2.0). Our analysis identified eleven destabilizing mutations (Y205S, I255S, I195T, Y205H, T256P, Y163C, P151H, R158H, T155P, R156G, and G266E) with predicted stability changes (ΔΔG ≤ -2.0 kcal mol-1), most located in the DNA-binding domain. Molecular dynamics simulations revealed distinct flexibility patterns, with Y205H showing the most significant deviation from wild-type p53, while Y163C maintained near-normal flexibility. Based on these findings, we propose a dynamics-based framework for TP53 variants according to their dynamic properties. These results demonstrate that TP53 mutations in GBM primarily affect protein stability and dynamics in the DNA-binding domain, likely contributing to oncogenesis through diverse mechanisms. This structural insight into p53 dysfunction in GBM provides a foundation for developing targeted therapeutic strategies against this devastating disease.