Probabilistic prediction of protein phosphorylation sites using classification relevance units machines

Abstract

Phosphorylation is an important post-translational modification of proteins that is essential to the regulation of many cellular processes. Although most of the phosphorylation sites discovered in protein sequences have been identified experimentally, the in vivo and in vitro discovery of the sites is an expensive, time-consuming and laborious task. Therefore, the development of computational methods for prediction of protein phosphorylation sites has drawn considerable attention. In this work, we present a kernel-based probabilistic Classification Relevance Units Machine (CRUM) for in silico phosphorylation site prediction. In comparison with the popular Support Vector Machine (SVM) CRUM shows comparable predictive performance and yet provides a more parsimonious model. This is desirable since it leads to a reduction in prediction run-time, which is important in predictions on large-scale data. Furthermore, the CRUM training algorithm has lower run-time and memory complexity and has a simpler parameter selection scheme than the Relevance Vector Machine (RVM) learning algorithm. To further investigate the viability of using CRUM in phosphorylation site prediction, we construct multiple CRUM predictors using different combinations of three phosphorylation site features – BLOSUM encoding, disorder, and amino acid composition. The predictors are evaluated through cross-validation and the results show that CRUM with BLOSUM feature is among the best performing CRUM predictors in both cross-validation and benchmark experiments. A comparative study with existing prediction tools in an independent benchmark experiment suggests possible direction for further improving the predictive performance of CRUM predictors.1

In silico prediction and characterization of protein post-translational modifications

“…Reproduced from Figure 1 (panel A), Figure 3 (panel B) and Figure 4 (panel C) in [119]. M A N U S C R I P T phosphorylation general and kinase-specific sites [7] kinase-specificity with indirect relationship [8] phosphorylation and conformational flexibility [9] association patterns around phosphorylation sites [10] random forests [11] relevance units machines [12] sequence with feature selection [13] in eukaryotes [14] in bacteria [15] lysine acetylation support vector machines [16,17] bi-relative adapted binomial score Bayes feature representation [18] logistic regression classifiers [19] solvent accessibility and acetylation [20] PTM crosstalk [21] combination of multiple features [22] position-specificity [23] sequence and functional features [24] characterization of KAT acetylation sites [25] proteome-wide prediction [26] lysine acetylation in context [27] prediction of KAT acetylation sites [28] lysine methylation combination of multiple features [22] conditional random field [29] composition of K-spaced amino acid pairs [30] glycosylation O-glycosylation [31] N-, O-and C-glycosites in eukaryotes [32] in evolutionarily distant eukaryotes [33] O-GlcNAc [34] amidation feature selection [35] -carboxylation random forest [36] scoring matrices based on evolutionary information [37] disulfides feature selection [38] S-glutathionylation protein sequence …”