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

作者：Wang Jyun Rong; Huang Wen Lin; Tsai Ming Ju; Hsu Kai Ti; Huang Hui Ling; Ho Shinn Ying^*

来源：Bioinformatics, 2017, 33(5): 661-668.

DOI：10.1093/bioinformatics/btw701

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.

• 出版日期2017-3-1