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Molecules 2017, 22(8), 1366; doi:10.3390/molecules22081366

Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM

1
Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Department of Computing, Hong Kong Polytechnic University, Hong Kong, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 24 July 2017 / Accepted: 15 August 2017 / Published: 18 August 2017
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
View Full-Text   |   Download PDF [978 KB, uploaded 18 August 2017]   |  

Abstract

Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, computational methods have attracted much attention because of their good performance in detecting PPIs. In our work, a novel computational method named as PCVM-LM is proposed which combines the probabilistic classification vector machine (PCVM) model and Legendre moments (LMs) to predict PPIs from amino acid sequences. The improvement mainly comes from using the LMs to extract discriminatory information embedded in the position-specific scoring matrix (PSSM) combined with the PCVM classifier to implement prediction. The proposed method was evaluated on Yeast and Helicobacter pylori datasets with five-fold cross-validation experiments. The experimental results show that the proposed method achieves high average accuracies of 96.37% and 93.48%, respectively, which are much better than other well-known methods. To further evaluate the proposed method, we also compared the proposed method with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the same datasets. The comparison results clearly show that our method is better than the SVM-based method and other existing methods. The promising experimental results show the reliability and effectiveness of the proposed method, which can be a useful decision support tool for protein research. View Full-Text
Keywords: protein-protein interactions; Legendre moments; position specific scoring matrix; probabilistic classification vector machine protein-protein interactions; Legendre moments; position specific scoring matrix; probabilistic classification vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, Y.-B.; You, Z.-H.; Li, L.-P.; Huang, Y.-A.; Yi, H.-C. Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM. Molecules 2017, 22, 1366.

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