EMBER: Multi-label prediction of kinase-substrate phosphorylation events through deep learning

Kinase-catalyzed phosphorylation of proteins forms the backbone of signal transduction within the cell, enabling the coordination of numerous processes such as the cell cycle, apoptosis, and differentiation. While on the order of 10 phosphorylation events have been described, we know the specific kinase performing these functions for less than 5% of cases. The ability to predict which kinases initiate specific individual phosphorylation events has the potential to greatly enhance the design of downstream experimental studies, while simultaneously creating a preliminary map of the broader phosphorylation network that controls cellular signaling. To this end, we describe EMBER, a deep learning method that integrates kinase-phylogeny information and motif-dissimilarity information into a multi-label classification model for the prediction of kinase-motif phosphorylation events. Unlike previous deep learning methods that perform single-label classification, we restate the task of kinase-motif phosphorylation prediction as a multi-label problem, allowing us to train a single unified model rather than a separate model for each of the 134 kinase families. We utilize a Siamese network to generate novel vector representations, or an embedding, of motif sequences, and we compare our novel embedding to a previously proposed peptide embedding. Our motif vector representations are used, along with one-hot encoded motif sequences, as input to a classification network while also leveraging kinase phylogenetic relationships into our model via a kinase phylogeny-based loss function. Results suggest that this approach holds significant promise for improving our map of phosphorylation relations that underlie kinome signaling.