Comprehensive Characterization of the Coding and Non-coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools.

Single Nucleotide Polymorphisms (SNPs) help to understand the phenotypic variations in humans. Numerous studies have examined the association of SNPs with various complex diseases. Researchers have identified the association of SNPs of genes through Genome-wide association study (GWAS). A number of GWAS have identified a loci located in the TP63 gene to be significantly associated with the risk of urinary bladder cancer. However, there is not any study characterizing the SNPs located at the TP63 gene for their functional and structural significance. Hence, the study aimed to comprehensively characterize SNPs in the human TP63 gene for their functional and structural significance. We investigated and evaluated the genomic variations affecting the expression, structure, and function of the TP63 protein. The study systematically retrieved nsSNPs information for the TP63 gene from the dbSNP database. We screened and analyzed both nsSNPs and non-coding SNPs in TP63 protein using a wide variety of computational tools to find the risk of pathogenicity. A total of 17 nsSNPs were identified using the 13 bioinformatics tools (i.e., SIFT, CADD, PROVEAN, PolyPhen2, PANTHER, PhD-SNP, SNP&GO, I-Mutant 2.0, ClinVar, Mutpred2, ConSurf, HOPE, and Mutation 3D) along with domain analysis. These pathogenic mutations cause a decrease in protein stability according to I-Mutant2.0. HOPE predicted 17 SNPs to have significant effect on TP63 protein structure and function. 12 nsSNPs were found in highly conserved position in TP63 inferring the damaging effect on the structure and function of the protein. Swiss PDB Viewer showed loss of hydrogen bonds and increased energy due to the SNPs. Molecular docking showed the reduction of the binding affinity of proteins for DNA and loss of hydrogen bonds. Six non-coding SNPs were found in miRNA binding sites in gene showing the effect on protein regulation using PolymiRTS and five non-coding SNPs were identified in single tissue expression quantitative trait loci (eQTL) in lung tissue, heart tissue (LV), and cerebral hemisphere (Brain) according to GTEx portal. The characterization of nsSNPs and non-coding SNPs will support researchers to focus on TP63 gene loci and ascertain their association with certain diseases.