Abstract
Computational methods for post-translational modification (PTM) site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of PTM patterns from the raw sequences, and hence it provides a powerful tool for improvement of post-translational modification site prediction. In our previous work, we proposed MusiteDeep, the first deep-learning framework for predicting the phosphorylation, one of the well-studied PTMs. The previous MusiteDeep takes protein raw sequence as the input and uses convolutional neural networks with a two-dimensional attention mechanism. It achieved over a 50% improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data. In this work, we extended to explore more types of PTMs, including acetylation, methylation and glycosylation. New deep-learning architecture and new training strategy are proposed for better performance. Web server for more PTM site predictions and complex motif visualization will be developed in the future.