In silico Analysis of Clinically Significant TP53 Mutations: Implications for Sustainable Cancer Diagnostics

Abstract

The TP53 tumour suppressor gene is one of the most frequently altered genes in human cancers. Several of its missense variants display distinct clinical relevance. This study aimed to characterise the structural and functional consequences of three well-documented TP53 mutations (R175H, R248Q, and R273H). These mutations remain underexplored in terms of computational evaluation. Variant pathogenicity wasassessed using SIFT and PolyPhen-2, while protein stability changes were predicted with I-Mutant 3.0. Homology models were generated through Swiss-Model, and structural perturbations were analysed and visualised using PyMOL. All three variants were predicted to be deleterious (SIFT ≤ 0.01; PolyPhen-2 ≥ 0.999) and destabilising, with ΔΔG values of −2.11 kcal/mol (R175H), −1.45 kcal/mol (R248Q), and −1.22 kcal/mol (R273H). Structural modelling revealed notable disruptions in the DNA-binding region, with R175H causing the most pronounced conformational alteration. The integrative in silico pipeline effectively revealed potential pathogenic mechanisms of these TP53 variants. This underscores the role of computational approaches in sustainable cancer diagnostics while reducing reliance on resource-intensiveexperimental studies.