Name
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. The SVM models integrating the MDDLogo-identified substrate motifs could yield an average accuracy of 68.70 percent. Furthermore, the independent testing result showed that the MDDLogo-clustered SVM models could provide a promising accuracy 78.50 percent and perform better than other prediction tools. Two cases have demonstrated the effective prediction of ubiquitination sites with corresponding substrate motifs. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TCBB.2016.2520939 | IEEE/ACM Trans. Comput. Biology Bioinform. |
Keywords | Field | DocType |
Proteins,Amino acids,Support vector machines,Substrates,Testing,Predictive models | Proteomics,Amino acid composition,Computer science,Ubiquitin,Support vector machine,Amino Acid Motifs,Proteome,Artificial intelligence,Lysine,Bioinformatics,Machine learning,Evolutionary information | Journal |
Volume | Issue | ISSN |
14 | 2 | 1545-5963 |
Citations | PageRank | References |
2 | 0.40 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Van-Nui Nguyen | 1 | 13 | 1.93 |
| Kai-Yao Huang | 2 | 115 | 7.91 |
| Chien-Hsun Huang | 3 | 12 | 1.73 |
| K. Robert Lai | 4 | 263 | 26.04 |
| Tzong-Yi Lee | 5 | 617 | 37.18 |