Predicting Palmitoylation Sites Using a Regularised Bio-basis Function Neural Network

Yang, Zheng Rong

Palmitoylation is one of the most important post-translational modifications involving molecular signalling activities. Two simple methods have been developed very recently for predicting palmitoylation sites, but the sensitivity (the prediction accuracy of palmitoylation sites) of both methods is low (< 65%). A regularised bio-basis function neural network is implemented in this paper aiming to improve the sensitivity. A set of protein sequences with experimentally determined palmitoylation sites are downloaded from NCBI for the study. The protein-oriented cross-validation strategy is used for proper model construction. The experiments show that the regularised bio-basis function neural network significantly outperforms the two existing methods as well as the support vector machine and the radial basis function neural network. Specifically the sensitivity has been significantly improved with a slightly improved specificity (the prediction accuracy of non-palmitoylation sites).