Abstract
Protein–protein interactions (PPIs) are essential for most living organisms’ process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.
Highlights
Recognition of protein–protein interactions (PPIs) is essential for elucidating the function of proteins and further understanding the various biological processes in cells
For the sake of addressing this problem and proposing an appropriate probabilistic model for predicting PPIs, we first adopt the Probabilistic Classification Vector Machine (PCVM) classifier which gives different priors over weights for training points that belong to different classes, i.e., the non-negative, left-truncated Gaussian is used for the positive class and the non-positive, right-truncated Gaussian is used for the negative class
Considering time, efficiency and economy, the use of computational methods based on protein amino acid sequences to predict PPIs has attracted the attention of researchers
Summary
Recognition of protein–protein interactions (PPIs) is essential for elucidating the function of proteins and further understanding the various biological processes in cells. A number of computational methods have been presented for the detection of PPIs based on different data types, such as protein domains, protein structure information, genomic information and phylogenetic profiles [5,6,7,8,9,10,11,12,13] These approaches cannot be achieved unless prior information of the protein is available. A novel computational approach for predicting PPIs from amino acid sequences based on a probabilistic classification vector machines model (PCVM) and a Zernike moments descriptor (PCVMZM) was proposed. We employed the Zernike moments feature representation on a Position-Specific Scoring Matrix (PSSM) to extract the evolutionary information from protein sequence, and a probabilistic classification vector machines classifier is used to infer the PPIs. In more detail, a PSSM representation is used to represent each protein. We found that our proposed method was superior to the state-of-the-art SVM, which clearly shows that the proposed approach is trustworthy in predicting PPIs [35,36,37,38,39]
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