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

For feature F3, we tried several different partitions of a protein sequence in several datasets. Table 4 shows the performance of our SVM model in three different datasets of virus-host PPIs. All the results shown in Table 4 were obtained by using SAR for features F1 and F2, but with different partitions for feature F3. On average, partitioning a protein sequence into 5 segments showed the best performance in all performance measures except sensitivity. In addition to the performance gain, partitioning a protein sequence into 5 segments is more advantageous than 7 or 9 segments with respect to the size of a feature vector that represents the sequence. When we partition a protein sequence into 5 segments, every pair of virus and host proteins is encoded in a feature vector with 280 elements (20 elements for F1, 20 elements for F2, and 20 × 5 � 100 elements for F3 for each of the virus and host proteins). If we partition a protein sequence into 7 or 9 partitions, a feature vector will require 360 elements (20 elements for F1, 20 elements for F2, and 20 × 7 � 140 elements for F3 for each of the virus and host proteins) or 440 elements (20 elements for F1, 20 elements for F2, and 20 × 9 � 180 elements for F3 for each of the virus and host proteins). However, the larger feature vectors did not result in performance improvement in predicting virus-host PPIs.