Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods - Qiu, Wang-Ren; Guan, Meng-Yue; Wang, Qian-Kun; Lou, Li-Liang; Xiao, Xuan (Frontiers in Endocrinology, 2022)
Pupylation is an important post-translational modification in proteins and plays a key role in the cell function of microorganisms, and then accurate prediction of pupylation proteins and specified sites is of great significance for the study of basic biological processes and development of related drugs since it would greatly save experimental costs and improve work efficiency. In this work, we first constructed a model for identifying pupylation proteins. To improve the proposed pupylation protein prediction model, the KNN scoring matrix model based on functional domain GO annotation and the word embedding model were used to extract the features and Random Under-sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied to balance the dataset. Finally, the balanced data sets were input into Extreme Gradient Boosting (XGBoost). The performance of 10-fold cross-validation shows that accuracy (ACC), Matthew's correlation coefficient (MCC) and area under the ROC curve (AUC) are 95.23%, 0.8100, 0.9864, respectively. For the pupylation sites prediction model. Six feature extraction codes, TPC, AAI, One-hot, PseAAC, CKSAAP, and Word Embedding were severed to extract protein sequence features, and chi-square test was employed for feature selection. Rigorous 10-fold cross-validations indicated that the accuracies are very high and outperformed its existing counterparts. Finally, for the convenience of researchers, PUP-PS-Fuse has been established at https://bioinfo.jcu.edu.cn/PUP-PS-Fuse.