Prediction of the functional impact of missense SNPs in the human CYP2D6 gene using in silico analysis methods

In this thesis study, missense single nucleotide polymorphisms (SNPs) in the CYP2D6 gene, which encodes an important enzyme involved in drug metabolism in humans, were analyzed using in silico methods. The aim of the study was to determine potentially deleterious variants by evaluating their structural and functional effects with the help of predictive tools. Within the scope of the study, various variants of the CYP2D6 gene were identified from bioinformatics databases. The potential impact of these variants was evaluated using prediction tools such as SIFT, PolyPhen-2, MutPred2, PANTHER, and FATHMM. A total of 54 SNPs corresponding to 167 missense variants were examined. For functional analysis, tools such as MUpro, I-Mutant 2.0, and CUPSAT were used to assess the impact of the identified deleterious variants on protein stability. Additionally, ConSurf was used for evolutionary conservation analysis, STRING for protein–protein interactions, and GeneMANIA for gene–gene interaction analysis. The HOPE platform was utilized for 3D structural evaluations. As a result of multiple analyses, the variants R296S, R296C, R441C, and R365G were identified as deleterious by five different prediction tools and were selected for further structural evaluation. The structural and functional impacts of these critical mutations were analyzed in detail, and their potential pharmacogenetic importance was emphasized. The findings of this study are expected to contribute to pharmacogenetic research and personalized medicine practices by enabling better interpretation of individual drug responses.