Role of Structure Quality in the P53 Protein Comprehensive Mutagenesis Analysis



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Mutations in tumor protein p53 play an important role in many human cancers, they are observed in about 50% of all tumors. p53 mutants cause various changes in cellular behavior and function. Accurate computational prediction of the mutant roles can help in clinical decision making for the affected patients. Structure-based prediction of thermodynamic stability, activity, and disease potential of protein mutants are frequently used in the analysis of structure-function relationships in proteins. One of the major challenges of this approach is caused by ambiguities in the structures of investigated proteins, e.g., when multiple structures are available or when the structures are partial. The effects of such ambiguities are investigated using the example of p53 protein. Structure based machine learning models were applied to predict changes in thermodynamic stability of p53 mutants using several different experimental and modeled protein structures. The results show that while structural ambiguities may affect the performance of structure-based function prediction models, the overall function predictions may be correct even when the structure quality is low. This finding has significant implications for using function prediction in the clinically relevant areas, where high quality structures of proteins of interest are frequently unavailable.



MUTAGENESIS, Structure, Tp53, X-ray crystallography