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

We also examined the contribution of the features to the prediction performance of the SVM model. Table 2 shows the results of using different combinations of features in 10fold cross validation of the SVM model with the 1 : 1 dataset of Table 1 . Among the single features, F3, which is the local composition of amino acids, was the best in all performance measures. With F3 alone, the SVM model achieved an accuracy above 92% and an MCC above 0.86, indicating that F3 is a very powerful feature in predicting virus-host PPIs. e best performance of the SVM model was observed when F1 and F3 were used. We also examined this work with different combinations of features. We used double amino acid repeats (DARs) for F1 and F2 instead of single amino acid repeats (SARs), but here for F2, we used a window size of 10 residues not 6 residues because we are working with DAR, so a window size of 10 residues is the biggest available window size that obtain a different value for every double amino acid repeat in it, but a window size of 6 residues does the same thing for the single amino acid repeat.