Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier
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Zheng-Wei Li1,*, Zhu-Hong You2,*, Xing Chen3, Li-Ping Li2, De-Shuang Huang4, Gui-Ying Yan5, Ru Nie1, Yu-An Huang6
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
2Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
3School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
4School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
5Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
6College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
*These authors have contributed equally to this work and are joint First Authors
Zhu-Hong You, email: [email protected]
Xing Chen, email: [email protected]
Keywords: disease, position-specific scoring matrix, Weber Local Descriptor, cancer, protein-protein interactions
Received: November 30, 2016 Accepted: January 11, 2017 Published: February 21, 2017
Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.
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