Predicting protein-protein interaction based on the sequence-segmented amino acid composition
基于分段氨基酸组成成分的蛋白质相互作用预测

The research on protein-protein interaction (PPI) can help us to reveal many essential problems of life processes, and also administer to prevention and diagnosis of human's diseases. It has important reference value for drug development. A dataset of protein-protein interactions was constructed firstly, and then the feature extracting method of sequence-segmented amino acid composition (SAAC) was proposed to predict protein-protein interaction in this paper. Based on the support vector machine (SVM), the prediction accuracy of 3 segments SAAC is 86.2% in 10-fold cross-validation (10CV) test, which is 2.31% higher than that of common amino acid composition (AAC) method. Using Guo's database and test method, the prediction accuracy of 3 segments SAAC is 90.11%, which is 2.75% higher than that of Guo's approach. The results show that the SAAC method can predict protein-protein interaction effectively.