Top 10 performing oversamplers for DTS2 versus baseline (no oversampling and SMOTE) averaged across four classifiers.

Lysine glutarylation is a newly reported post-translational modification (PTM) that plays significant roles in regulating metabolic and mitochondrial processes. Accurate identification of protein glutarylation is the primary task to better investigate molecular functions and various applications. Due to the common disadvantages of the time-consuming and expensive nature of traditional biological sequencing techniques as well as the explosive growth of protein data, building precise computational models to rapidly diagnose glutarylation is a popular and feasible solution. In this work, we proposed a novel AdaBoost-based predictor called iGlu_AdaBoost to distinguish glutarylation and non-glutarylation sequences. Here, the top 37 features were chosen from a total of 1768 combined features using Chi2 following incremental feature selection (IFS) to build the model, including 188D, the composition of k-spaced amino acid pairs (CKSAAP), and enhanced amino acid composition (EAAC). With the help of the hybrid-sampling method SMOTE-Tomek, the AdaBoost algorithm was performed with satisfactory recall, specificity, and AUC values of 87.48%, 72.49%, and 0.89 over 10-fold cross validation as well as 72.73%, 71.92%, and 0.63 over independent test, respectively. Further feature analysis inferred that positively charged amino acids RK play critical roles in glutarylation recognition. Our model presented the well generalization ability and consistency of the prediction results of positive and negative samples, which is comparable to four published tools. The proposed predictor is an efficient tool to find potential glutarylation sites and provides helpful suggestions for further research on glutarylation mechanisms and concerned disease treatments.