基于序列的G蛋白偶联受体-药物相互作用预测研究

Research on Sequence-based Predictor for GPCR-Drug Interaction Prediction

准确预测G蛋白质偶联受体(GPCR)是否与药物(Drug)相互作用是新药开发的关键步骤之一。从时间和费用方面来说,通过生物实验的方法来确定GPCR-Drug是否相互作用的代价是昂贵的。因此,直接从蛋白质序列出发预测GPCR-Drug的相互作用具有重要的意义。提出了一种基于序列的GPCR-Drug相互作用预测方法:从蛋白质序列抽取进化信息特征;对药物抽取指纹特征;基于上述两种特征,使用基于证据理论的K近邻算法进行分类预测。在标准数据集上的实验结果表明了所述方法的有效性。

Accurately identifying whether a G-protein-coupled receptor（GPCR） will interact with a drug is a crucial step in drug discovery. However, experimentally determining the interactions between GPCR and drug is time-consuming and expensive. Hence,developing automated prediction methods for GPCT-Drug interaction prediction solely from protein sequence is in urgent need. In this study, a new sequence-based method for GPCT-Drug interaction prediction was pro- posed. Evolutionary information feature from protein sequence and footprint feature from drug were combined to form discriminative feature. And the optimized evidence-theoretic K-nearest neighbor（OET-KNN） prediction algorithm was taken as classifier. Experimental results demonstrate that the proposed method achieves good performance and can act as complementary predictor to existing methods.