Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 18, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/ijms18112373
Keywords
protein-protein interactions; amino acid sequences; local conjoint triad descriptor; deep neural networks
Funding
- Natural Science Foundation of China [61402378, 61562054]
- Natural Science Foundation of Chongqing Science and Technology Commission [cstc2016jcyjA0351]
- Fundamental Research Funds for the Central Universities of China [2362015XK07, XDJK2016B009]
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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.
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