همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Assessment of TP53 Gene Mutations by Bioinformatic tools and Their Impact on Tumor Suppressor Function

نویسندگان :

Hirad Akhlaghi¹ Mehri Khatami²

1- دانشگاه یزد 2- دانشگاه یزد

کلمات کلیدی :

TP53،Tumor Suppressor Protein،Pathogenic Mutations،Cancer-associated Variants،Molecular Modeling

چکیده :

The TP53 gene encodes a tumor suppressor protein that is essential for maintaining cellular homeostasis. Commonly known as the "guardian of the genome," TP53 plays a crucial role in regulating processes such as cell cycle arrest, apoptosis, senescence, DNA repair, and metabolic reprogramming in response to cellular stress. This protein functions as a tetrameric transcription factor, binding to specific DNA sequences to control the expression of target genes. Despite its critical role in preventing malignant transformation, TP53 is one of the most frequently mutated genes in human cancers. Mutations, particularly those occurring in its DNA-binding domain, impair its tumor-suppressive functions and are linked to various cancers, including breast cancer. These mutations often exhibit dominant-negative or loss-of-function effects, disrupting key regulatory pathways. In this study, five distinct pathogenic mutations in the TP53 gene were analyzed, with a focus on isoform D. The three-dimensional structure of TP53 was modeled using two tools: AlphaFold2, which predicts protein structure de novo without a template, and SWISS-MODEL, which generated a structure based on a template from the TP53 protein of Macaca fascicularis. The resulting structures were visualized and analyzed in PyMOL, providing a detailed view of their conformations. After modeling, the identified mutations were introduced into the TP53 sequence, and the mutated protein structures were generated using AlphaFold2. These mutated models were then aligned with the wild-type structure in PyMOL for direct comparison. Structural differences were captured as images, highlighting the impact of the mutations on protein conformation and potential functional impairment. The comparison revealed alterations in the protein structure that may affect its overall function. Using multiple Bioinformatic tools, including PolyPhen-2, SIFT, ExPASy, HOPE, and I-Mutant, we systematically evaluated how these mutations affect the protein's structure, stability, and function. Our analyses revealed that all five mutations were pathogenic, significantly disrupting TP53’s ability to bind to DNA and destabilizing its structural domains .After computational analysis, PolyPhen-2 identified all mutations as pathogenic, with scores of 0.95 or higher, indicating a high likelihood of damaging the protein's function. SIFT analysis further corroborated these findings, assigning a score of 0 to all mutations, signifying maximum pathogenicity. Using Expasy, we generated a hydrophilicity plot with Hydropath. / Kyte & Doolittle chosen as the amino acid scale , which revealed that the mutations altered the hydrophilicity scores of residues, likely resulting in significant structural changes and destabilization of the protein's domains. Additionally, both HOPE and I-Mutant analyses confirmed the pathogenic nature of these mutations. This study underscores the value of in-silico approaches in elucidating the molecular consequences of TP53 mutations. These findings contribute to the growing body of knowledge necessary for developing targeted therapeutic interventions aimed at restoring TP53 function in cancer treatment. Future research should focus on experimental validation of these results to confirm the structural and functional impacts of these mutations both in vitro and in vivo.

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