Luo Fenglin, Wang Minghui, Liu Yu, Zhao Xing-Ming, Li Ao
School of Information Science and Technology.
Centers for Biomedical Engineering, University of Science and Technology of China, Hefei AH, China.
Bioinformatics. 2019 Aug 15;35(16):2766-2773. doi: 10.1093/bioinformatics/bty1051.
Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites.
In this study we present DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation. Unlike multi-layer convolutional neural networks, DeepPhos consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylation prediction by intra block concatenation layers and inter block concatenation layers. DeepPhos can also be used for kinase-specific prediction varying from group, family, subfamily and individual kinase level. The experimental results demonstrated that DeepPhos outperforms competitive predictors in general and kinase-specific phosphorylation site prediction.
The source code of DeepPhos is publicly deposited at https://github.com/USTCHIlab/DeepPhos.
Supplementary data are available at Bioinformatics online.
磷酸化是研究最为深入的翻译后修饰，对多种生物学过程至关重要。近来，人们为开发磷酸化位点预测的计算预测器付出诸多努力，但其中大多数基于特征选择和判别分类。因此，开发一种能够自动揭示蛋白质磷酸化位点复杂模式的新型高精度预测器很有必要。
在本研究中，我们提出了DeepPhos，一种用于预测蛋白质磷酸化的新型深度学习架构。与多层卷积神经网络不同，DeepPhos由密集连接的卷积神经元网络块组成，这些块可以捕获序列的多种表示，通过块内连接层和块间连接层进行最终的磷酸化预测。DeepPhos还可用于从组、家族、亚家族和个体激酶水平进行激酶特异性预测。实验结果表明，DeepPhos在总体和激酶特异性磷酸化位点预测方面均优于同类预测器。
DeepPhos的源代码已公开存于https://github.com/USTCHIlab/DeepPhos。
补充数据可在《生物信息学》在线获取。
