Prediction of hepatocellular carcinoma associated biomarkers in TP53 gene; A comprehensive in silico analysis

TP53 mutation is one of the most frequently altered genetic variants leading to hepatocellular carcinoma (HCC). Single nucleotide polymorphisms (SNPs) in this gene play a crucial role in predicting the risk of HCC development. This study aimed to analyze how SNPs in the human TP53 gene affect protein stability, structure, localization, and other physicochemical properties. The coding sequence (CDS) and SNP data of TP53 were retrieved from the Ensembl database. CDS were translated using the ExPASy Translate tool. Mutated protein sequences were analyzed using ProtParam, I-MUTANT, CELLO2, SOPMA, SWISS-MODEL, SAVES v6.0, and the HDOCK server to evaluate physicochemical properties, stability, subcellular localization, secondary and tertiary structures, structure validation, and docking behavior. Among 30 SNPs analyzed, three SNPs (rs1555526997 in exon 15, rs1567556784 in exon 59, and rs869054324 in exon 66) showed complete alterations in physicochemical properties, subcellular localization, 2D and 3D structures, and ERRAT and PROCHECK values. The study identified 21 missense, 3 frameshift, and 6 stop-gained mutations. Of the missense mutations, seven SNPs showed a decreasing effect on protein stability. This study demonstrates the effectiveness of in silico approaches in predicting HCC associated biomarkers and assessing the functional impact of TP53 mutations.