Incorporating Deep Learning With Word Embedding to Identify Plant Ubiquitylation Sites - Wang, Hongfei; Wang, Zhuo; Li, Zhongyan; Lee, Tzong-Yi (Frontiers in Cell and Developmental Biology, 2020)
Protein ubiquitylation is an important post-translational modification (PTM), which involves in diverse biological processes and plays an essential role in the regulation of physiological mechanisms and diseases. The Protein Lysine Modifications Database (PLMD) has accumulated abundant ubiquitylated proteins with their substrate sites for more than 20 kinds of species. Numerous works have consequently developed a variety of ubiquitylation sites prediction tools across all species, mainly relying on the predefined sequence features and machine learning algorithms. However, the difference of ubiquitylated patterns between these species stays unclear. In this work, the sequence-based characterization of ubiquitylated substrate sites has revealed remarkable differences among plants, animals and fungus. Then an improved word embedding scheme based on the transfer learning strategy was incorporated with the multi-layer Convolutional Neural Network (CNN) for identifying protein ubiquitylation sites. For the prediction of plant ubiquitylation sites, the proposed deep-learning scheme could outperform the machine learning-based methods, with the accuracy of 75.6%, precision of 73.3%, recall of 76.7%, F-score of 0.7493 and 0.82 AUC on the independent testing set. Although the ubiquitylated specificity of substrate sites is complicated, this work has demonstrated that the application of word embedding method can enable the extraction of informative features and help the identification of ubiquitylated sites. To accelerate the investigation of protein ubiquitylation, the data sets and source code used in this study are freely available at https://github.com/wang-hong-fei/DL-plant-ubsites-prediction.