A p− adic approach to the TSPO gene

TSPO protein is known to be involved in various cellular functions and dysregulations of TSPO expression has been found to be associated with pathologies of different human diseases, including cardiovascular disease, cancer, neuroinflammatory, neurodegenerative, neoplastic disorders. However, there are limited studies in the literature on the effects of sequence variations in the TSPO gene on the function of the protein and their relationship with human diseases. Evaluating the pathogenicity of genetic variants is crucial in terms of prioritizing the functional importance and clinical use. Therefore, various in-silico prediction tools have been developed that combine different algorithms to predict the effects of sequence variations on protein functions or gene regulation. In this study, the $p$-adic distance approach in modeling the genetic code, proposed and developed by Dragovich and Dragovich, was discussed in order to obtain an alternative to the existing in-silico prediction tools. Dragovichs’ approach is expressed as follows: A 5-adic space of codons is constructed and 5-adic and 2-adic distances between codons are taken into account. As a result, two codons with the smallest value of 5-adic and 2-adic distances are obtained, encoded for the same amino acid and stop signal. This model describes well the degeneration of the genetic code. This study combined the data obtained from in-silico prediction tools and used a bioinformatics approach to determine the functional relevance of coding SNPs in the TSPO. Overall, we evaluate the potential utility of Dragovichs’ approach by comparing it with other existing prediction tools for variant classification and prioritization.