Prediction of G-Protein Coupled Receptors and Their Type
G蛋白偶联受体及其类型的预测

G-protein coupled receptor is a very important signal molecule receptor and its dysfunction may lead to the emergence of many diseases. According to the previous studies, a method combining the feature analysis methods of sequences with support vector machine (SVM) technology was proposed for identifying GPCRs and their type by analyzing the characteristics of sequence differences. Especially, codon use frequencies of mRNA genes translating into GPCR proteins were first selected as the sequence feature, in respect that it is the inherently the fusion of both codon usage bias and amino acid composition signals. The results showed that the optimal SVM classifiers for predicting GPCR sequences and their type were designed by choosing the hybrid feature by combining codon use frequencies of mRNA genes and double amino acid use frequencies and using the RBF kernel as kernel function after considering the performance of all types of SVM classifiers.