Predicting Interactions between Virus and Host Proteins Using Repeat Patterns and Composition of Amino Acids

Alguwaizani, Saud; Park, Byungkyu; Zhou, Xiang; Huang, De-Shuang; Han, Kyungsook

As discussed earlier, we trained the SVM model with the training dataset TR1 consisting of PPIs of human with +ssRNA viruses except hepatitis C virus (HCV) and SARS [20] to assess the independence of the test data from the training data. As shown in Table 5 , target virus proteins in the test datasets showed a very low average sequence similarity in the range (3.12% to 5.20%) to the virus proteins in the training dataset (see Additional file 4 for the similarity of every sequence pair between the training and test datasets). Table 6 shows the results of testing the prediction model on 5 independent datasets of PPIs of new viruses. Despite such a low sequence similarity and species difference, the SVM model showed a high performance in independent testing. In particular, the SVM model showed a higher sensitivity (94.37% and 96.67%) for HCV and SARS virus, which are +ssRNA viruses. It is interesting to note that HPV-16, which is a dsDNA virus, showed the highest specificity of 94.04% and accuracy of 87.93%. Figure 4 shows the ROC curves of independent testing of the SVM model on PPIs of five new viruses.