ESA-UbiSite: Accurate prediction of human ubiquitination sites by identifying a set of effective negatives

Jyun Rong Wang, Wen Lin Huang, Ming Ju Tsai, Kai Ti Hsu, Hui Ling Huang, Shinn-Ying Ho*

*Corresponding author for this work

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

Motivation: Numerous ubiquitination sites remain undiscovered because of the limitations of mass spectrometry-based methods. Existing prediction methods use randomly selected non-validated sites as non-ubiquitination sites to train ubiquitination site prediction models. Results: We propose an evolutionary screening algorithm (ESA) to select effective negatives among non-validated sites and an ESA-based prediction method, ESA-UbiSite, to identify human ubiquitination sites. The ESA selects non-validated sites least likely to be ubiquitination sites as training negatives. Moreover, the ESA and ESA-UbiSite use a set of well-selected physicochemical properties together with a support vector machine for accurate prediction. Experimental results show that ESA-UbiSite with effective negatives achieved 0.92 test accuracy and a Matthews's correlation coefficient of 0.48, better than existing prediction methods. The ESA increased ESA-UbiSite's test accuracy from 0.75 to 0.92 and can improve other post-translational modification site prediction methods.

Original languageEnglish
Pages (from-to)661-668
Number of pages8
JournalBioinformatics
Volume33
Issue number5
DOIs
StatePublished - 1 Jan 2017

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