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

It is interesting to note that proteins of new hosts have a higher average sequence similarity to those in training datasets than proteins of new viruses, but the SVM model showed a lower performance for new hosts. is can be explained by the number of partner proteins of the target proteins shared by training and test datasets. As shown in Table 8 , the number of common proteins between the test datasets for new viruses (TS1-TS5) and their training dataset TR1 is larger than the number of common proteins between the test datasets for new hosts (TS6-TS10) and their training dataset TR2. us, the SVM model showed a better performance for new viruses than for new hosts. ese results corroborate the known problem with pairinput methods, which was first reported by Park and Marcotte [21] . According to their study [21] , prediction methods that operate on pairs of objects such as PPIs perform much better for test pairs that share components with a training set than for those that do not. us, our prediction model showed a better performance in testing for new viruses which share more partner proteins (i.e., host proteins) with training datasets than in testing for new hosts which share fewer partner proteins (i.e., virus proteins) with training datasets.