Improved Prediction of Palmitoylation Sites Using PWMs and SVM

Abstract

Protein palmitoylation is an important and common post-translational lipid modification of proteins and plays a critical role in various cellular processes. Identification of Palmitoylation sites is fundamental to decipher the mechanisms of these biological processes. However, experimental determination of palmitoylation residues without prior knowledge is laborious and costly. Thus computational approaches for prediction of palmitoylation sites in proteins have become highly desirable. Here, we propose PPWMs, a computational predictor using Position Weight Matrices (PWMs) encoding scheme and support vector machine (SVM) for identifying protein palmitoylation sites. Our PPWMs shows a nice predictive performance with the area under the ROC curve (AUC) of 0.9472 for the S-palmitoylation sites prediction and 0.9964 for the N-palmitoylation sites prediction on the newly proposed dataset. Comparison results show the superiority of PPWMs over two existing widely known palmitoylation site predictors CSS-Palm 2.0 and CKSAAP-Palm in many cases. Moreover, an attempt of incorporating structure information such as accessible surface area (ASA) and secondary structure (SS) into prediction is made and the structure characteristics are analyzed roughly. The corresponding software can be freely downloaded from http://math.cau.edu.cn/PPWMs.html.