Capsulated Graph Neural Network for Ubiquitylation Sites Prediction

Ubiquitylation is a critical post-translational modification (PTM) process that performs a critical role in a wide range of biological functions and is closely related to hallmarks of cancer, such as DNA damage response and oxidative stress. Over the past several years, deep learning have been widely employed in protein ubiquitylation site prediction tools. However, existing deep learning tools have a common feature that they treat protein sequences as input without considering spatial information of protein. This work exploits the three-dimensional structural protein to develop a novel graph-driven ubiquitylation site predictive model (GraphUbiquSite) combing capsule module to improve predictive accuracy. According to the experimental results on Protein Lysine Modification Database (PLMD), the proposed model can archive better performance in comparison with the state-of-the-art methods.