A New Scheme to Characterize and Identify Protein Ubiquitination Sites

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Van-Nui NguyenTzong-Yi Lee

Abstract

Protein ubiquitination, involving the conjugation of ubiquitin on lysine residue, serves as an important modulator of many cellular functions in eukaryotes. Recent advancements in proteomic technology have stimulated increasing interest in identifying ubiquitination sites. However, most computational tools for predicting ubiquitination sites are focused on small-scale data. With an increasing number of experimentally verified ubiquitination sites, we were motivated to design a predictive model for identifying lysine ubiquitination sites for large-scale proteome dataset. This work assessed not only single features, such as amino acid composition (AAC), amino acid pair composition (AAPC) and evolutionary information, but also the effectiveness of incorporating two or more features into a hybrid approach to model construction. The support vector machine (SVM) was applied to generate the prediction models for ubiquitination site identification. Evaluation by five-fold cross-validation showed that the SVM models learned from the combination of hybrid features delivered a better prediction performance. Additionally, a motif discovery tool, MDDLogo, was adopted to characterize the potential substrate motifs of ubiquitination sites. Th...Continue Reading

Citations

Dec 24, 2018·Briefings in Functional Genomics·Wenying HeQuan Zou
Oct 27, 2020·Frontiers in Cell and Developmental Biology·Hongfei WangTzong-Yi Lee

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