The Community for Technology Leaders
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
ISBN: 978-1-5090-3051-4
pp: 108-113
Fei He , School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, China
Lingling Bao , School of Information Science and Technology, Northeast Normal University, Changchun, China
Rui Wang , School of Information Science and Technology, Northeast Normal University, Changchun, China
Jiagen Li , School of Information Science and Technology, Northeast Normal University, Changchun, China
Dong Xu , Department of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences, Center, University of Missouri, Columbia, MO 65211, USA
Xiaowei Zhao , School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, China
ABSTRACT
In eukaryotes, protein ubiquitylation is an important type of post-translation modification, in which the ubiquitin conjugates to a substrate protein. To have a better insight of the mechanisms underlying ubiquitylation, a key step is to identify protein ubiquitylation sites. Many existing computational methods are based on feature engineering, which may lead to biased and incomplete features. Deep learning provides multiple-layer networks and non-linear mapping operations to detect potential complex patterns in a data-driven way, especially for large-scale data. It provides a promising new method to predict ubiquitylation sites. In this paper, we proposed a multimodal deep architecture for protein ubiquitylation sites prediction. First, we designed different multiple layers to extract hidden informative patterns from three modalities, namely protein fragments, physico-chemical properties, and sequence profiles. Then, the deep representations corresponding to three modalities were merged to implement the classification. On the available largest scale protein ubiquitylation site database PLMD, the performance of our proposed method was measured with 66.7% sensitivity, 66.4% specificity, 66.43% accuracy, and 0.221 MCC value. A range of comparative experiments also showed that our proposed architecture outperformed several popular protein ubiquitylation site prediction tools. Our source code is freely available at https://github.com/jiagenlee/deepUbiquitylation.
INDEX TERMS
Proteins, Amino acids, Training, Computer architecture, Protein engineering, Feature extraction, Tools
CITATION

F. He, L. Bao, R. Wang, J. Li, D. Xu and X. Zhao, "A multimodal deep architecture for large-scale protein ubiquitylation site prediction," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 108-113.
doi:10.1109/BIBM.2017.8217634
280 ms
(Ver 3.3 (11022016))