Table_1_DeepKhib: A Deep-Learning Framework for Lysine 2-Hydroxyisobutyrylation Sites Prediction.DOCX

As a novel type of post-translational modification, lysine 2-Hydroxyisobutyrylation (Khib) plays an important role in gene transcription and signal transduction. In order to understand its regulatory mechanism, the essential step is the recognition of Khib sites. Thousands of Khib sites have been experimentally verified across five different species. However, there are only a couple traditional machine-learning algorithms developed to predict Khib sites for limited species, lacking a general prediction algorithm. We constructed a deep-learning algorithm based on convolutional neural network with the one-hot encoding approach, dubbed CNNOH. It performs favorably to the traditional machine-learning models and other deep-learning models across different species, in terms of cross-validation and independent test. The area under the ROC curve (AUC) values for CNNOH ranged from 0.82 to 0.87 for different organisms, which is superior to the currently available Khib predictors. Moreover, we developed the general model based on the integrated data from multiple species and it showed great universality and effectiveness with the AUC values in the range of 0.79–0.87. Accordingly, we constructed the on-line prediction tool dubbed DeepKhib for easily identifying Khib sites, which includes both species-specific and general models. DeepKhib is available at http://www.bioinfogo.org/DeepKhib.

赖氨酸2-羟基异丁酰化（lysine 2-Hydroxyisobutyrylation，Khib）作为一种新型翻译后修饰，在基因转录与信号转导过程中发挥重要作用。为阐明其调控机制，核心步骤在于识别Khib位点。目前已有数千个Khib位点在5个不同物种中得到实验验证，但当前仅针对有限物种开发了少量传统机器学习算法用于预测Khib位点，尚缺乏通用的预测算法。我们构建了一种基于卷积神经网络（convolutional neural network）结合独热编码（one-hot encoding）的深度学习算法，命名为CNNOH。在跨物种的交叉验证与独立测试中，该算法的表现优于传统机器学习模型与其他深度学习模型。针对不同物种，CNNOH的ROC曲线下面积（area under the ROC curve，AUC）取值范围为0.82至0.87，优于当前已有的Khib预测工具。此外，我们基于多物种整合数据构建了通用模型，该模型展现出优异的通用性与有效性，AUC值介于0.79~0.87。据此，我们开发了在线预测工具DeepKhib，可便捷识别Khib位点，涵盖物种特异性模型与通用模型两类。DeepKhib的访问地址为http://www.bioinfogo.org/DeepKhib。