Computational prediction of ubiquitination protein using evolutionary profiles and functional domains
Ubiquitination, as a post-translational modification, is a crucial biological process presented in cell signaling, death and localization. Identification of ubiquitination protein is of fundamental importance for understanding molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins remain undiscovered, even in well studied model organisms. To reduce experimental costs, computational (in silico) methods have been introduced to predict ubiquitination sites. If we can predict whether a query protein can be ubiquitinated or not, it is meaningful by itself and helpful for predicting ubiquitination sites. However, all the computational methods so far only predict ubiquitination sites, with unsatisfactory accuracy. In this study, we developed the first computational method for predicting ubiquitination proteins without relying on ubiquitination site prediction. The method extracts features from sequence conservation information via a grey system model, as well as functional domain annotation and subcellular localization. Together with the detailed feature analysis and application of the Relief feature selection algorithm, the results of 5-fold cross-validation on three datasets achieved a high accuracy of 0.8981, with the Matthew's correlation coefficient 0.7963. Our study may guide the related experimental design and provide useful insights for studying the mechanisms and modulation of ubiquitination pathways.