A Transfer Learning-Based Approach for Lysine Propionylation Prediction - Li, Ang; Deng, Yingwei; Tan, Yan; Chen, Min (Frontiers in Physiology, 2021)
Lysine propionylation is a newly-discover post-translational modification and plays a key role in the cellular process. Although the proteomics techniques was capable of detecting propionylation, large-scale detection still was challenging. To bridge this gap, we presented a transfer learning-based method for computationally predicting propionylation sites. The recurrent neural network-based deep learning model was trained firstly by the malonylation and then fine-tuned by the propionylation. The trained model served as feature extractor where protein sequences as input were translated into numerical vectors. The support vector machine was used as the final classifier. The proposed method reached a matthews correlation coefficient of 0.6615 on the 10-fold cross validation and 0.3174 on the independent test, outperforming state of the art methods. The enrichment analysis indicated that the propionylation was associated with these GO terms (GO:0016620, GO:0051287, GO:0003735, GO:0006096, and GO:0005737) and with metabolism.