Li Ang, Deng Yingwei, Tan Yan, Chen Min
School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China.
Front Physiol. 2021 Apr 21;12:658633. doi: 10.3389/fphys.2021.658633. eCollection 2021.
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/.
赖氨酸丙酰化是一种新发现的翻译后修饰（PTM），在细胞过程中起关键作用。尽管蛋白质组学技术能够检测丙酰化，但大规模检测仍然具有挑战性。为了弥补这一差距，我们提出了一种基于迁移学习的方法来计算预测丙酰化位点。基于循环神经网络的深度学习模型首先通过丙二酰化进行训练，然后通过丙酰化进行微调。训练后的模型用作特征提取器，将蛋白质序列作为输入转换为数值向量。支持向量机用作最终分类器。所提出的方法在10折交叉验证中马修斯相关系数（MCC）达到0.6615，在独立测试中达到0.3174，优于现有方法。富集分析表明，丙酰化与这些GO术语（GO:0016620、GO:0051287、GO:0003735、GO:0006096和GO:0005737）以及代谢相关。我们开发了一个用户友好的在线工具来预测丙酰化位点，可在http://47.113.117.61/获取。
