Data_Sheet_2_A Transfer Learning-Based Approach for Lysine Propionylation Prediction.CSV

Lysine propionylation is a newly discovered posttranslational modification (PTM) and plays a key role in the cellular process. Although proteomics techniques was capable of detecting propionylation, large-scale detection was still 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 (MCC) of 0.6615 on the 10-fold crossvalidation 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. We developed a user-friendly online tool for predicting propoinylation sites which is available at http://47.113.117.61/.

赖氨酸丙酰化是一类新近发现的翻译后修饰（posttranslational modification, PTM），在细胞过程中发挥关键作用。尽管蛋白质组学技术已可实现丙酰化修饰的检测，但大规模检测仍颇具挑战。为填补这一研究缺口，我们提出了一种基于迁移学习的丙酰化位点计算预测方法。该基于循环神经网络的深度学习模型首先以丙二酰化修饰数据完成预训练，随后通过丙酰化修饰数据进行微调。经训练完成的模型可作为特征提取器，将输入的蛋白质序列转化为数值向量。最终采用支持向量机作为分类器。所提方法在10折交叉验证中取得了0.6615的马修斯相关系数（Matthews correlation coefficient, MCC），在独立测试集上的马修斯相关系数为0.3174，性能优于当前顶尖方法。富集分析结果表明，丙酰化修饰与GO术语（GO:0016620、GO:0051287、GO:0003735、GO:0006096及GO:0005737）及代谢过程显著相关。我们开发了一款操作便捷的丙酰化位点预测在线工具，其访问地址为http://47.113.117.61/。