A comprehensive in silico investigation into the deleterious nonsynonymous single nucleotide polymorphisms of the human transcription factor EB (TFEB) gene and their predicted association with cancer

Transcription factor EB (TFEB) is an essential protein that is connected to a number of diseases, such as lysosomal storage disorders and cancer. Patients with glioblastoma multiforme and uterine endometrioid carcinoma have already been identified to have nonsynonymous mutations in this gene. These mutations may have an impact on TFEB protein's functions and structure. Therefore, to find possible biomarkers for different disease treatments, the most harmful single nucleotide polymorphisms (SNPs) of the TFEBprotein have been identified in this study. The goal is to create a systematic dataset of the SNPs related to the TFEBgene, which could be useful in the diagnosis and management of many disorders linked to the target gene. The SNPs of the TFEBprotein were analyzed via a wide range of bioinformatics techniques, including both sequence and structure-based methodologies. Nonsynonymous SNV research can be advanced through the application of various machine learning methods that have been developed as a result of recent advancements in computational platforms. Among 449 nsSNPs, a total of 6 nsSNPs have been found to be harmful, destabilizing, and disease-causing. Each nsSNP interferes with its function. All of the mutant proteins interact with the DNA molecule during docking more effectively than the alphafold (wild type) protein does. A strong correlation between cancer and mutations in R315 has been identified. The detrimental effects of nsSNPs and noncoding SNPs on the structure and activities of proteins will help researchers understand the crucial role that mutations play in the molecular pathways involved in a variety of disorders. The discovery of possible targets for the diagnosis of diseases and treatment interventions will eventually result from this. Moreover, this thorough analysis can facilitate the exploration of potential disease-causing SNPs in the TFEB gene and assist in identifying effective drugs or pharmacological targets. Consequently, further experimental mutational research, genome-wide association studies, and clinical-based studies are essential to validate these findings.