[Prediction of G-protein-coupled receptor classes with pseudo amino acid composition]

G-protein-coupled receptors (GPCRs), the largest family of cell surface receptors, play an important role in the production of therapeutic drugs. The functions of GPCRs are closely related to their classification and subclassification. It is difficult to obtain the spatial structure of GPCRs sequence by experimental approaches. It is highly desired to develop powerful tools and effective algorithms for classifying the family of GPCRs. In this study, based on the concept of pseudo amino acid composition (PseAA) originally introduced by Chou, approximate entropy (ApEn) of protein sequence as an additional characteristic is used to construct PseAA. A 21-D (dimensional) PseAA is formulated to represent the sample of a protein. Fuzzy K nearest neighbors (FKNN) classifier is adopted as prediction engine. The datasets in low homology are used to validate the performance of the proposed method. Compared with prior works, the successful rates achieved of our research are the highest. The test results indicate that the novel approach can play the role of a compliment to many of the existing methods, which promises to be a useful tool for GPCRs function prediction.