Deciphering the functional landscape of phosphosites with deep neural network

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 of in vitro and 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. [Display omitted] •FuncPhos-SEQ is available for functional phosphosite prediction on human proteomic level•Both sequence features and PPI network features are crucial in FuncPhos-SEQ prediction•NADK-S48/50 phosphorylation, catalyzed by ERK1/2, could activate its enzymatic activity Liang et al. proposed the deep neural network FuncPhos-SEQ for functional phosphosite prediction on the human proteomic level and verified that NADK-S48/50 phosphorylation could activate its enzymatic activity.