Hybrid AI Framework for Structural Deviation Analysis of TP53 Mutations using Multi-Metric Learning Models

Protein structural mutations are critical for understanding drug resistance, disease mechanisms, and protein function. This paper presents a mixed computational approach that examines the structural differences between wild-type and mutant proteins. This AI-driven framework provides ultra-fine-grained predictions of how individual mutations alter protein structure and function, augmenting the precision of structural-deviation information. By integrating multi-metric performance evaluation (Root Mean Square Deviation, Local Distance Difference Test) within a cognitive AI architecture, it delivers authentic, data-powered direction for biomedical research can depend on. The approach radically speeds the pace of drug-resistance profiling, therapeutic design, and targeted protein engineering. Real-world protein mutation datasets are used for validating model's performance. It detects structural abnormalities with a high accuracy that surpasses 95%. The results illustrate how the strategy could be useful in drug development, protein engineering, biological research. Using it allows to improve prediction accuracy by better modeling long-range (global) and short-range (local) deviations in protein conformation. In addition to mapping genomic mutations to protein structure, this model goes a step further by providing an explanation of how specific mutations change protein structure and disturb biological function.