Classifying G-protein-coupled receptors to the finest subtype level

G-protein-coupled receptors (GPCRs) constitute a remarkable protein family of receptors that are involved in a broad range of biological processes. A large number of clinically used drugs elicit their biological effect via a GPCR. Thus, developing a reliable computational method for predicting the functional roles of GPCRs would be very useful in the pharmaceutical industry. Nowadays, researchers are more interested in functional roles of GPCRs at the finest subtype level. However, with the accumulation of many new protein sequences, none of the existing methods can completely classify these GPCRs to their finest subtype level. In this paper, a pioneer work was performed trying to resolve this problem by using a hierarchical classification method. The first level determines whether a query protein is a GPCR or a non-GPCR. If it is considered as a GPCR, it will be finally classified to its finest subtype level. GPCRs are characterized by 170 sequence-derived features encapsulating both amino acid composition and physicochemical features of proteins, and support vector machines are used as the classification engine. To test the performance of the present method, a non-redundant dataset was built which are organized at seven levels and covers more functional classes of GPCRs than existing datasets. The number of protein sequences in each level is 5956, 2978, 8079, 8680, 6477, 1580 and 214, respectively. By 5-fold cross-validation test, the overall accuracy of 99.56%, 93.96%, 82.81%, 85.93%, 94.1%, 95.38% and 92.06% were observed at each level. When compared with some previous methods, the present method achieved a consistently higher overall accuracy. The results demonstrate the power and effectiveness of the proposed method to accomplish the classification of GPCRs to the finest subtype level.