Prediction of Rescue Mutants to Restore Functional Activity of Tumor Protein TP53 through Data Mining Techniques

Abstract

Several oncogenic malignancies show evidence of carrying mutations in the TP53 gene causing defects in the genome maintenance mechanisms that tend to instigate cancer. Early and precise detection of genetic mutations is a demanding task in the field of bioinformatics and molecular biology, while the accurate identification of rescue mutations presents great therapeutic remedies. In this research investigation, our aim is to identify potential P53 cancer –causing mutants and predict possible rescue mutations at secondary –site DNA binding domains. We highlight the impact of data mining techniques on predicting the active and inactive P53 mutant status based on the amino-acid substitutions at the DNA-binding sites. A collection of 16772 cancer cases related to P53 mutations, have been explored to detect and categorize the P53 cancer mutants and their rescue mutants in silico through Data Mining techniques. We have identified fifty-four P53 cancer mutants and report new rescue mutants for thirteen existing hot spot P53 cancer mutants. Identification of new rescue mutants is expected to offer remarkable advancement in the field of cancer therapy targeting drugs to specifically restore normal P53 transcriptional activity.