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 tested the prediction model on independent datasets of virus-host PPIs, which were not used in training the model and have a very low sequence similarity to any protein in training datasets of the model. Despite a low sequence similarity between proteins in training datasets and target proteins in test datasets, the prediction model showed a high performance comparable to the best performance of other methods for single virus-host PPIs. In comparison of our method with others using same datasets, our method outperformed the others. Experimental results demonstrate that the repeat patterns and composition of amino acids are simple, yet powerful features for predicting virus-host PPIs. e method can be used to find potential PPIs of new viruses or hosts, for which little information is known. TR2 TS6 TR2 TS7 TR2 TS8 TR2 TS9 TR2 TS10 #PPIs 689 191 689 125 689 86 689 57 689 78 #Virus proteins 35 116 35 34 35 24 35 10 35 27 #Host proteins 522 141 522 87 522 79 522 38 522 64 #Virus proteins common to TR and TS 9 (7.8%) 1 (2.9%) 4 (16.7%) 0 (0.0%) 0 (0.0%) e numbers in parentheses represent the proportion of common proteins to proteins in test datasets.