Computational analysis uncovers the deleterious SNPs along with the mutational spectrum of p53 gene and its differential expression pattern in pan-cancer

Abstract

Abstract Background A variety of accessible data, including those of single-nucleotide polymorphisms (SNPs) on the human p53 gene, are made widely available on a global scale. Owing to this, our investigation aimed to deal with the detrimental SNPs in the p53 gene by executing various valid computational tools, including—Filter, SIFT, PredictSNP, Fathmm, UTRScan, ConSurf, SWISS-MODEL, Amber 16 package, Tm-Adjust, I-Mutant, Task Seek, GEPIA2 after practical and basic appraisal, dissolvable openness, atomic progression, analyzing the energy minimization and assessing the gene expression pattern. Results Out of the total 581 p53 SNPs, 420 SNPs were found to be missense or non-synonymous, 435 SNPs were in the three prime UTR, and 112 SNPs were in the five prime UTR from which 16 non-synonymous SNPs (nsSNPs) were predicted to be non-tolerable while PredictSNP package predicted 14. Concentrating on six bioinformatics tools of various dimensions, a combined output was generated, where 14 nsSNPs could exert a deleterious effect. We found 5 missense SNPs in the DNA binding domain's three crucial amino acid positions, using diverse SNP analyzing tools. The underlying discoveries were fortified by microsecond molecular dynamics (MD) simulations, TM-align, I-Mutant, and Project HOPE. The ExPASy-PROSITE tools characterized whether the mutations were located in the functional part of the protein or not. This study provides a decisive outcome, concluding the accessible SNPs' information by recognizing the five unfavorable nsSNPs—rs28934573 (S241F), rs11540652 (R248Q), rs121913342 (R248W), rs121913343 (R273C), and rs28934576 (R273H). By utilizing Heatmapper and GEPIA2, several visualization plots, including heat maps, box plots, and survival plots, were produced. Conclusions These plots disclosed differential expression patterns of the p53 gene in humans. The investigation focused on recognizing the detrimental nsSNPs, which augmented the danger posed by various oncogenesis in patients of different populations, including within the genome-wide studies (GWS).