DeepPSP: A Global-Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites
DeepPSP: A Global-Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites
复制标题
DeepPSP：基于全局-局部信息的深度神经网络，用于预测蛋白质磷酸化位点
DOI：
10.1021/acs.jproteome.0c00431
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发表时间：
2021-01-01
影响因子：
4.4
通讯作者：
Dong, Jiyang
中科院分区：
文献类型：
--
作者：
Guo, Lei;Wang, Yongpei;Dong, Jiyang
蛋白质磷酸化位点的鉴定是蛋白质功能研究和药物设计的重要环节。近年来，由于计算方法具有成本低、速度快等优点，在磷酸化位点识别中的应用越来越多。现有的预测方法大多集中于利用潜在磷酸化位点周围的局部信息进行预测，而没有考虑蛋白质序列的全局信息。在这里，我们证明了蛋白质序列的全局信息对于磷酸化位点预测也是至关重要的。本文提出了一种新的深度神经网络模型DeepPSP，用于蛋白质磷酸化位点的预测。在DeepPSP模型中，引入了两个并行模块来从蛋白质序列中提取局部和全局特征。两个挤压和激励块和一个双向长短时记忆块被引入到每个模块中，以捕捉有效的表示的序列。使用公共数据集进行了比较研究，以评估DeepPSP和其他四种预测方法的性能。DeepPSP的F1得分、受试者操作特征曲线下面积（AUROC）和精确召回曲线下面积（AUPRC）分别为0.4819、0.82和0.50，对于S/T一般站点预测和0.4206、0.73、0.50，对于S/T一般站点预测，分别为0.4819、0.82和0.50。和0.39。与MusiteDeep方法相比，DeepPSP的F1分数、AUROC和AUPRC分别增加了8.6%、2.5%和8.7%，用于S/T一般位点预测，分别增加了20.6%、5.8%和18.2%，用于Y一般位点预测。在测试的方法中，开发的DeepPSP方法也被发现对不同的激酶特异性位点预测产生最佳结果，包括CDK，促分裂原活化蛋白激酶，CAMK，AGC和CMGC。总之，开发的DeepPSP方法可以通过包括全局信息来提供更准确的磷酸化位点预测。它可以作为一个替代模型，具有更好的性能和解释性的蛋白质磷酸化位点预测。
Identification of phosphorylation sites is an important step in the function study and drug design of proteins. In recent years, there have been increasing applications of the computational method in the identification of phosphorylation sites because of its low cost and high speed. Most of the currently available methods focus on using local information around potential phosphorylation sites for prediction and do not take the global information of the protein sequence into consideration. Here, we demonstrated that the global information of protein sequences may be also critical for phosphorylation site prediction. In this paper, a new deep neural network model, called DeepPSP, was proposed for the prediction of protein phosphorylation sites. In the DeepPSP model, two parallel modules were introduced to extract both local and global features from protein sequences. Two squeeze-and-excitation blocks and one bidirectional long short-term memory block were introduced into each module to capture effective representations of the sequences. Comparative studies were carried out to evaluate the performance of DeepPSP, and four other prediction methods using public data sets The F1-score, area under receiver operating characteristic curves (AUROC), and area under precision-recall curves (AUPRC) of DeepPSP were found to be 0.4819, 0.82, and 0.50, respectively, for S/T general site prediction and 0.4206, 0.73, and 0.39, respectively, for Y general site prediction. Compared with the MusiteDeep method, the F1-score, AUROC, and AUPRC of DeepPSP were found to increase by 8.6, 2.5, and 8.7%, respectively, for S/T general site prediction and by 20.6, 5.8, and 18.2%, respectively, for Y general site prediction. Among the tested methods, the developed DeepPSP method was also found to produce best results for different kinase-specific site predictions including CDK, mitogen-activated protein kinase, CAMK, AGC, and CMGC. Taken together, the developed DeepPSP method may offer a more accurate phosphorylation site prediction by including global information. It may serve as an alternative model with better performance and interpretability for protein phosphorylation site prediction.