Format

Send to

Choose Destination
See comment in PubMed Commons below
Int J Mol Sci. 2011;12(12):8347-61. doi: 10.3390/ijms12128347. Epub 2011 Nov 28.

Prediction of lysine ubiquitylation with ensemble classifier and feature selection.

Author information

1
College of Life Science, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China; College of Computer Science, Northeast Normal University, 2555 Jingyue Street, Changchun 13017, China. zhaoxw303@nenu.edu.cn

Abstract

Ubiquitylation is an important process of post-translational modification. Correct identification of protein lysine ubiquitylation sites is of fundamental importance to understand the molecular mechanism of lysine ubiquitylation in biological systems. This paper develops a novel computational method to effectively identify the lysine ubiquitylation sites based on the ensemble approach. In the proposed method, 468 ubiquitylation sites from 323 proteins retrieved from the Swiss-Prot database were encoded into feature vectors by using four kinds of protein sequences information. An effective feature selection method was then applied to extract informative feature subsets. After different feature subsets were obtained by setting different starting points in the search procedure, they were used to train multiple random forests classifiers and then aggregated into a consensus classifier by majority voting. Evaluated by jackknife tests and independent tests respectively, the accuracy of the proposed predictor reached 76.82% for the training dataset and 79.16% for the test dataset, indicating that this predictor is a useful tool to predict lysine ubiquitylation sites. Furthermore, site-specific feature analysis was performed and it was shown that ubiquitylation is intimately correlated with the features of its surrounding sites in addition to features derived from the lysine site itself. The feature selection method is available upon request.

KEYWORDS:

ensemble classifier; lysine ubiquitylation sites; support vector machine; ubiquitylation

PMID:
22272076
PMCID:
PMC3257073
DOI:
10.3390/ijms12128347
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Multidisciplinary Digital Publishing Institute (MDPI) Icon for PubMed Central
    Loading ...
    Support Center