iGlu-Lys: A Predictor for Lysine Glutarylation Through Amino Acid Pair Order Features

As one of the new posttranslational modification, lysine glutarylation has been identified in both prokaryotic and eukaryotic cells. These glutarylated proteins are involved in various cellular functions, such as translation, metabolism, and exhibited diverse subcellular localizations. Experimental identification of lysine glutarylation sites was founded in 2014 and also identified its deglutarylase sirturn 5(SIRT 5). Computational prediction of lysine glutarylation could be a complementary way to the experimental technique. In this work, the lysine glutarylation predictor iGlu-Lys has been developed based on the machine learning scheme. We have selected the best feature scheme which took the amino acid pair order and special-position information into account from four constructions. The machine learning algorithm support vector machine has been adopted and its performance has been measured for different window length of peptides. In the 10-fold cross-validation with window length 19, the AUC and MCC were 0.8944 and 0.5098, respectively. Different ROC curves in 6-, 8-, and 10-fold cross-validations were very close which illustrated the robustness of our predictor. The results of iGLu-Lys were better than the existing method GlutPred. Meanwhile, a free webserver for iGlu-Lys is accessible at http://app.aporc.org/iGlu-Lys/.