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Int. J. Mol. Sci. 2017, 18(11), 2373; doi:10.3390/ijms18112373

Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences

1
College of Computer and Information Science, Southwest University, Chongqing 400715, China
2
College of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, China
3
SMILE (Statistical Machine Intelligence & Learning) Lab and Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610000, China
*
Author to whom correspondence should be addressed.
Received: 9 October 2017 / Revised: 1 November 2017 / Accepted: 4 November 2017 / Published: 8 November 2017
(This article belongs to the Special Issue Special Protein Molecules Computational Identification)
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Abstract

Protein-protein interactions (PPIs) play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs) and a novel local conjoint triad description (LCTD) feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae, DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC) as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study. View Full-Text
Keywords: protein-protein interactions; amino acid sequences; local conjoint triad descriptor; deep neural networks protein-protein interactions; amino acid sequences; local conjoint triad descriptor; deep neural networks
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Wang, J.; Zhang, L.; Jia, L.; Ren, Y.; Yu, G. Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences. Int. J. Mol. Sci. 2017, 18, 2373.

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