Deciphering the functional landscape of phosphosites with deep neural network
Deciphering the functional landscape of phosphosites with deep neural network
复制标题
用深度神经网络破译磷酸盐的功能景观
DOI：
10.1016/j.celrep.2023.113048
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发表时间：
2023-09-01
期刊：
影响因子：
8.8
通讯作者：
Luo，Cheng
中科院分区：
文献类型：
--
作者：
Liang，Zhongjie;Liu，Tonghai;Luo，Cheng
目前的生化方法仅鉴定了一小部分磷酸蛋白质组中最明确表征的激酶，并且磷酸位点的功能分配几乎可以忽略不计。在此，我们分析了特定激酶催化的底物偏好，并提出了一种名为 FuncPhos-SEQ 的新型集成深度神经网络模型，用于人类蛋白质组水平磷酸位点的功能分配。 FuncPhos-SEQ 使用多个卷积神经网络 (CNN) 通道整合来自蛋白质序列的磷酸位基序信息，以及使用网络嵌入和深度神经网络 (DNN) 通道整合来自蛋白质-蛋白质相互作用 (PPI) 的网络特征。这些串联的特征被共同输入到异构特征网络中，以对功能性磷酸位点进行优先级排序。结合一系列体外和细胞生化测定，我们证实 NADK-S48/50 磷酸化可以激活其酶活性。此外，ERK1/2 被发现是负责 NADK-S48/50 磷酸化的主要激酶。此外，FuncPhos-SEQ 被开发为在线服务器。
Current biochemical approaches have only identified the most well-characterized kinases for a tiny fraction of the phosphoproteome, and the functional assignments of phosphosites are almost negligible. Herein, we analyze the substrate preference catalyzed by a specific kinase and present a novel integrated deep neural network model named FuncPhos-SEQ for functional assignment of human proteome-level phosphosites. FuncPhos-SEQ incorporates phosphosite motif information from a protein sequence using multiple convolutional neural network (CNN) channels and network features from protein-protein interactions (PPIs) using network embedding and deep neural network (DNN) channels. These concatenated features are jointly fed into a heterogeneous feature network to prioritize functional phosphosites. Combined with a series ofin vitroand cellular biochemical assays, we confirm that NADK-S48/50 phosphorylation could activate its enzymatic activity. In addition, ERK1/2 are discovered as the primary kinases responsible for NADK-S48/50 phosphorylation. Moreover, FuncPhos-SEQ is developed as an online server.