RecSNO: Prediction of Protein S-Nitrosylation Sites Using a Recurrent Neural Network

S-Nitrosylation modification is one of the most important post-translational modifications; it plays a critical role in a vast variety of biological processes and is related to various diseases. Identification of S-Nitrosylation sites in proteins is crucial for understanding and controlling basic biological processes. The conventional experimental identification methods are laborious and cost in-efficient. To overcome these issues, computational biological approaches are under consideration, including use of machine learning and deep learning algorithms. All existing S-Nitrosylation predictors use the handicraft feature extraction method and could be improved upon. We propose an end-to-end deep learning based S-Nitrosylation site predictor with an embedded layer and bidirectional long short-term memory. The proposed method uses protein sequences as inputs without any need for complex features interventions. This sequence-based protein prediction method is associated with a significant improvement in identification of S-Nitrosylation sites. More specifically, the best prediction of the proposed architecture showed an improvement of in MCC 3% on 5-fold cross validation and 5% on an independent test dataset. Finally, the user-friendly publicly available webserver is accessible at http://nsclbio.jbnu.ac.kr/tools/RecSNO/.