DeepPSP: A Global–Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites,Journal of Proteome Research

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DeepPSP: A Global–Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites
Journal of Proteome Research ( IF 3.8 ) Pub Date : 2020-11-26 , DOI: 10.1021/acs.jproteome.0c00431
Lei Guo ₁ , Yongpei Wang ₁ , Xiangnan Xu ₂ , Kian-Kai Cheng ₃ , Yichi Long ₁ , Jingjing Xu ₁ , Sanshu Li ₄ , Jiyang Dong ₁

Affiliation

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.

中文翻译：

DeepPSP：基于全球本地信息的深度神经网络，用于预测蛋白质磷酸化位点

磷酸化位点的鉴定是蛋白质功能研究和药物设计的重要步骤。近年来，由于其低成本和高速度，计算方法在磷酸化位点识别中的应用越来越多。当前大多数可用的方法集中于使用潜在的磷酸化位点周围的局部信息进行预测，而不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的预测也可能至关重要。在本文中，提出了一种新的深度神经网络模型，称为DeepPSP，用于预测蛋白质的磷酸化位点。在DeepPSP模型中，引入了两个并行模块以从蛋白质序列中提取局部和全局特征。每个模块中引入了两个挤压和激励块和一个双向长短期记忆块，以捕获序列的有效表示。进行了比较研究以评估DeepPSP的性能，并使用公共数据集进行了其他四种预测方法。发现DeepPSP的F1得分，接收者工作特征曲线下的面积（AUROC）和精确召回曲线下的面积（AUPRC） S / T常规站点预测分别为0.4819、0.82和0.50，Y常规站点预测分别为0.4206、0.73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％，S / T一般站点预测分别为20.6％，5.8％和18.2％。在测试的方法中，还发现开发的DeepPSP方法对于不同的激酶特异性位点预测（包括CDK，丝裂原激活的蛋白激酶，CAMK，AGC和CMGC）产生最佳结果。综上所述，开发的DeepPSP方法可以通过包含全局信息来提供更准确的磷酸化位点预测。它可以用作具有更好性能和可解释性的蛋白质磷酸化位点预测的替代模型。CAMK，AGC和CMGC。综上所述，开发的DeepPSP方法可以通过包含全局信息来提供更准确的磷酸化位点预测。它可以用作具有更好性能和可解释性的蛋白质磷酸化位点预测的替代模型。CAMK，AGC和CMGC。综上所述，开发的DeepPSP方法可以通过包含全局信息来提供更准确的磷酸化位点预测。它可以用作具有更好性能和可解释性的蛋白质磷酸化位点预测的替代模型。

更新日期：2021-01-01

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