Prediction of Rare Palmitoylation Events in Proteins

Abstract

Abstract

Palmitoylation directs many cellular processes such as protein trafficking, sorting, signaling, interactions with other biomolecules, to name a few. Palmitoylation commonly occurs on cysteine; however, occasional palmitoylation of few other amino acids has also been reported. To date, comprehensive analysis on occasional palmitoylation is unavailable. In the present study, we reported a computational method to predict palmitoylation of glycine and serine residues in a protein. The method is based on support vector machine (SVM). It was trained on position-specific scoring matrix of amino acids that surrounds palmitoylated glycine and serine. During training, SVM models were evaluated on leave-one-out cross validation, and the maximum prediction accuracies achieved during training were 100% glycine palmitoylation and 99.94% for serine palmitoylation. Similar prediction for performance was also shown on independent data sets. The two SVM models were used to develop a prediction method called RAREPalm. We provide web-server and standalone of RAREPalm, using the user that can predict the potential glycine and serine palmitoylation site(s) in a protein. Comparative analysis of glycine, serine, and cysteine palmitoylation was also done to analyze pathways and classes to which different forms of palmitoylation belong. We hope that our attempt will be useful in finding more glycine and serine that may undergo palmitoylation and expanding the information on these lesser known sites of palmitoylation.