Affiliation:
1. National Demonstration Center for Experimental Biological Sciences Education, College of Biological Sciences, China Agricultural University, Beijing 100193, China
Abstract
Background:
As one of the most important reversible protein post-translation modification
types, ubiquitination plays a significant role in the regulation of many biological processes,
such as cell division, signal transduction, apoptosis and immune response. Protein ubiquitination
usually occurs when ubiquitin molecule is attached to a lysine on a target protein, which is also
known as “lysine ubiquitination”.
Objective:
In order to investigate the molecular mechanisms of ubiquitination-related biological
processes, the crucial first step is the identification of ubiquitination sites. However, conventional
experimental methods in detecting ubiquitination sites are often time-consuming and a large number
of ubiquitination sites remain unidentified. In this study, a ubiquitination site prediction method
for Arabidopsis thaliana was developed using a Support Vector Machine (SVM).
Methods:
We collected 3009 experimentally validated ubiquitination sites on 1607 proteins in A.
thaliana to construct the training set. Three feature encoding schemes were used to characterize
the sequence patterns around ubiquitination sites, including AAC, Binary and CKSAAP. The maximum
Relevance and Minimum Redundancy (mRMR) feature selection method was employed to
reduce the dimensionality of input features. Five-fold cross-validation and independent tests were
used to evaluate the performance of the established models.
Results:
As a result, the combination of AAC and CKSAAP encoding schemes yielded the
best performance with the accuracy and AUC of 81.35% and 0.868 in the independent test.
We also generated an online predictor termed as AraUbiSite, which is freely accessible at:
http://systbio.cau.edu.cn/araubisite.
Conclusion:
We developed a well-performed prediction tool for large-scale ubiquitination site
identification in A. thaliana. It is hoped that the current work will speed up the process of identification
of ubiquitination sites in A. thaliana and help to further elucidate the molecular mechanisms
of ubiquitination in plants.
Funder
National Training Program of Innovation and Entrepreneurship for Undergraduates
Publisher
Bentham Science Publishers Ltd.
Subject
Computational Mathematics,Genetics,Molecular Biology,Biochemistry
Cited by
26 articles.
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