Prediction of the functional consequences of single amino acid substitution in human cytochrome P450

Non-synonymous single nucleotide polymorphisms (nsSNPs) which cause single amino acid substitution in cytochrome P450 have been regarded as a considerable factor in determining the capability of drug metabolism. The examination of functional consequences of nsSNPs is of great importance in evaluating different drug metabolism capabilities among individuals and developing the personalised medicine. In this study, we use a support vector machine combined with genetic algorithm to construct the predictive model, whereby we can predict the functional consequences of nsSNPs of nine major human cytochrome P450s. In addition, we develop a strategy to calculate two kinds of protein descriptors using the amino acid indices. In contrast to the amino acid indices, the protein descriptors can fully consider the change caused by the substitution of single amino acid. After multi-round feature selection, five protein descriptors and eight substrate descriptors are selected to construct the final model. The validation results exhibit that our model can achieve high predictive accuracy (Acc) of 88.1%, sensitivity of 93.1% and specificity of 72.7%. Overall, the main improvement of our model over other existing models is the relatively high Acc and the consideration of the properties of protein and substrate in the prediction.