Abstract
Protein–Protein Interactions (PPI) is not only the critical component of various biological processes in cells, but also the key to understand the mechanisms leading to healthy and diseased states in organisms. However, it is time-consuming and cost-intensive to identify the interactions among proteins using biological experiments. Hence, how to develop a more efficient computational method rapidly became an attractive topic in the post-genomic era. In this paper, we propose a novel method for inference of protein-protein interactions from protein amino acids sequences only. Specifically, protein amino acids sequence is firstly transformed into Position-Specific Scoring Matrix (PSSM) generated by multiple sequences alignments; then the Pseudo PSSM is used to extract feature descriptors. Finally, ensemble Rotation Forest (RF) learning system is trained to predict and recognize PPIs based solely on protein sequence feature. When performed the proposed method on the three benchmark data sets (Yeast, H. pylori, and independent dataset) for predicting PPIs, our method can achieve good average accuracies of 98.38%, 89.75%, and 96.25%, respectively. In order to further evaluate the prediction performance, we also compare the proposed method with other methods using same benchmark data sets. The experiment results demonstrate that the proposed method consistently outperforms other state-of-the-art method. Therefore, our method is effective and robust and can be taken as a useful tool in exploring and discovering new relationships between proteins. A web server is made publicly available at the URL http://202.119.201.126:8888/PsePSSM/ for academic use.
Highlights
Protein–Protein Interactions (PPIs) play an important role in almost every cellular process [1, 2]
We propose a novel method for inference of protein-protein interactions from protein amino acids sequences only
Protein amino acids sequence is firstly transformed into PositionSpecific Scoring Matrix (PSSM) generated by multiple sequences alignments; the Pseudo PSSM is used to extract feature descriptors
Summary
Protein–Protein Interactions (PPIs) play an important role in almost every cellular process [1, 2]. In order to understand the mechanisms of all kinds of biochemical activities, a variety of biological experimental methods have been designed to detect the interactions between proteins, for example, two-hybrid systems [4, 5], mass spectrometry [6, 7], immunoprecipitation [8], protein chip technology [9], etc It is time-consuming, cost-intensive and small-scale to identify the interactions among proteins using biological experiments only. Martin et al developed a computational model to identify the interactions among proteins by using the signature descriptor [30] This model achieved an accuracy of 70% and 80% when testing on the H. pylori and Yeast data sets by 10-fold cross-validation. Comparison results show that the proposed method consistently outperforms other state-of-the-art methods
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