Comprehensive In Silico Functional Characterization of TP53 Variants Using an Automated Web-Based Annotation Platform

Abstract Background TP53 is one of the most frequently mutated tumor suppressor genes in human cancers, with mutations occurring in approximately 50% of all malignancies and exceeding 90% in certain subtypes such as high-grade serous ovarian carcinoma. Accurate interpretation of TP53 variants remains essential for both research and clinical diagnostics; however, manual mutation interpretation is time-consuming and lacks standardized domain-specific functional context. Objective This study describes the development and validation of a web-based computational platform for automated identification and functional annotation of TP53 mutations with integrated domain mapping capabilities. Methods The platform integrates HGVS nomenclature conversion, codon-level analysis, and protein domain mapping using the canonical TP53 transcript (NM_000546.6). Global sequence alignment was implemented using the Needleman–Wunsch algorithm with affine gap penalties (gap opening = − 10, gap extension = − 1). The system was developed using Python 3.9 (back-end logic), React.js (front-end interface), and Node.js (server framework), with deployment on Vercel. Domain annotations were derived from UniProt (P04637) and established structural studies. Validation was conducted using a curated dataset of 500 clinically documented TP53 variants from COSMIC (350 somatic mutations) and ClinVar (150 germline variants). Results The platform achieved 98.4% overall accuracy in mutation classification (95% CI: 96.8–99.3%) across SNPs, nonsense, missense, and frameshift variants. Cross-validation against reported pathogenic TP53 variants from ClinVar demonstrated a concordance rate of 97.3% (Cohen’s κ = 0.961, p < 0.001). Domain mapping successfully assigned 94.8% of mutations to specific functional regions, with the DNA-binding domain accounting for 68% of all variants. Comparison with existing tools revealed that the platform uniquely provides integrated domain-level annotation in a single web-based interface. Conclusion This web-based platform facilitates rapid, automated TP53 mutation interpretation with domain-specific functional context. The tool addresses a significant gap in accessible, domain-contextualized variant annotation and holds utility for researchers, clinicians, and educators in cancer genomics.