G-protein Coupled Receptor Subfamilies Prediction Based on Nearest Neighbor Approach

Hydrophobicity has been considered as the potential measurement for the prediction of G-Proteins coupled receptor subfamilies. In the present work, using Hydrophobicity measure, we make use of fast Fourier transform to better analyze the sequence information. In our experiments, we have observed that sequence pattern based information could easily be exploited in the frequency domain using proximity rather than increasing margin of separation between the classes. Based on this information, a simple nearest neighbor (NN) method is then used to classify the 17 subfamilies. The proposed proximity based approach has outperformed the one against all implementation of support vector machine (SVM) [Y. Z. Guo, et al, Acta Biochimica et Biophysica Sinica, 37(2005) 759]. Our simple proximity based approach has superior performance in terms of all three measures on both Jackknife and independent data set. For B, C, D and F subfamilies, the Mathew´s correlation coefficient and overall accuracy using jackknife test are 0.96 and 96.03%, while, using independent data set are 0.91 and 91.6% respectively. The results validate the idea of exploiting sequence pattern based information in the frequency domain using proximity in terms of Euclidian distance. Another side advantage is that instead of training and saving 17 SVM models, we need a single NN classifier.