Towards more accurate prediction of ubiquitination sites: a comprehensive review of current methods, tools and features.

Abstract

Protein ubiquitination is one of the most important reversible post-translational modifications (PTMs). In many biochemical, pathological and pharmaceutical studies on understanding the function of proteins in biological processes, identification of ubiquitination sites is an important first step. However, experimental approaches for identifying ubiquitination sites are often expensive, labor-intensive and time-consuming, partly due to the dynamics and reversibility of ubiquitination. In silico prediction of ubiquitination sites is potentially a useful strategy for whole proteome annotation. A number of bioinformatics approaches and tools have recently been developed for predicting protein ubiquitination sites. However, these tools have different methodologies, prediction algorithms, functionality and features, which complicate their utility and application. The purpose of this review is to aid users in selecting appropriate tools for specific analyses and circumstances. We first compared five popular webservers and standalone software options, assessing their performance on four up-to-date ubiquitination benchmark datasets from Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Arabidopsis thaliana. We then discussed and summarized these tools to guide users in choosing among the tools efficiently and rapidly. Finally, we assessed the importance of features of existing tools for ubiquitination site prediction, ranking them by performance. We also discussed the features that make noticeable contributions to species-specific ubiquitination site prediction.