Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding.
BACKGROUND
Proteins are the important molecules which participate in virtually every aspect of cellular function within an organism in pairs. Although high-throughput technologies have generated considerable protein-protein interactions (PPIs) data for various species, the processes of experimental methods are both time-consuming and expensive. In addition, they are usually associated with high rates of both false positive and false negative results. Accordingly, a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. Therefore, it is very urgent to develop effective computational methods for prediction of PPIs solely using protein sequence information.


RESULTS
In this study, we present a novel computational model combining weighted sparse representation based classifier (WSRC) and global encoding (GE) of amino acid sequence. Two kinds of protein descriptors, composition and transition, are extracted for representing each protein sequence. On the basis of such a feature representation, novel weighted sparse representation based classifier is introduced to predict protein interaction class. When the proposed method was evaluated with the PPIs data of S. cerevisiae, Human and H. pylori, it achieved high prediction accuracies of 96.82, 97.66 and 92.83 % respectively. Extensive experiments were performed for cross-species PPIs prediction and the prediction accuracies were also very promising.


CONCLUSIONS
To further evaluate the performance of the proposed method, we then compared its performance with the method based on support vector machine (SVM). The results show that the proposed method achieved a significant improvement. Thus, the proposed method is a very efficient method to predict PPIs and may be a useful supplementary tool for future proteomics studies.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China.
Yu-An
YA
Huang
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China. zhuhongyou@cumt.edu.cn.
Zhu-Hong
ZH
You
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. xingchen@amss.ac.cn.
Xing
X
Chen
Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China.
Keith
K
Chan
Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China.
Xin
X
Luo
D001426:Bacterial Proteins / Q000737:chemistry / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D016480:Helicobacter pylori; D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D040901:Proteomics; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*; D060388:Support Vector Machine
D001426:Bacterial Proteins; D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins