Benchmarking analysis of deleterious SNP prediction tools on CYP2D6 enzyme

The cytochrome P450 family is composed of hemeproteins involved in the metabolic transformation of endogenous and exogenous substances. The CYP2D6 enzyme is responsible for the metabolism of ~25% of clinically used drugs and is mainly expressed in the liver. The CYP2D6 gene is known to have a large number of single nucleotide polymorphisms (SNPs). Nevertheless, these variations could modify the CYP2D6 enzyme's function, resulting in poor metabolizing or ultra-extensive metabolizing phenotypes, when metabolism is slower or accelerated, respectively. Currently, there are several computational tools for predicting functional changes caused by genetic variations. Here, we evaluated the predictive power of 20 web servers using a data set of 37 CYP2D6 missense SNPs (2 neutral and 35 deleterious) previously reported in literature with enzymatic assays with the purified protein. The results suggest that the most appropriate tools for CYP2D6 SNP prediction are SDM and PoPMuSiC, which could aid in the classification of novel missense SNPs in this gene, providing the identification of mutations potentially associated with drug metabolism and pointing new directions for precise medicine.