iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice

Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice. For each PTM-specific model, we adopted eight feature encoding schemes, including sequence-based features, physicochemical property-based features and spatial mapping information-based features. The optimal feature set was identified from each encoding, and their respective models were established. Extensive experimental results show that iRice-MS always display excellent performance on 5-fold cross-validation and independent dataset test. In addition, our novel approach provides the superiority to other existing tools in terms of AUC value. Based on the proposed model, a web server named iRice-MS was established and is freely accessible at http://lingroup.cn/server/iRice-MS.

Automated summary

In this study, a comprehensive method called iRice-MS based on eXtreme Gradient Boosting (XGBoost) was developed to identify multiple post-translational modifications (PTMs) in rice. The method displayed excellent performance in cross-validation and independent dataset test, and showed superiority to existing tools in terms of AUC value.