Position-specific analysis and prediction of protein pupylation sites based on multiple features.
Pupylation is one of the most important posttranslational modifications of proteins; accurate identification of pupylation sites will facilitate the understanding of the molecular mechanism of pupylation. Besides the conventional experimental approaches, computational prediction of pupylation sites is much desirable for their convenience and fast speed. In this study, we developed a novel predictor to predict the pupylation sites. First, the maximum relevance minimum redundancy (mRMR) and incremental feature selection methods were made on five kinds of features to select the optimal feature set. Then the prediction model was built based on the optimal feature set with the assistant of the support vector machine algorithm. As a result, the overall jackknife success rate by the new predictor on a newly constructed benchmark dataset was 0.764, and the Mathews correlation coefficient was 0.522, indicating a good prediction. Feature analysis showed that all features types contributed to the prediction of protein pupylation sites. Further site-specific features analysis revealed that the features of sites surrounding the central lysine contributed more to the determination of pupylation sites than the other sites.
College of Computer Science and Information Technology, Northeast Normal University, 2555 Jingyue Street, Changchun 130117, China ; Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130117, China.
Xiaowei
X
Zhao
Jiangyan
J
Dai
Qiao
Q
Ning
Zhiqiang
Z
Ma
Minghao
M
Yin
Pingping
P
Sun
D000465:Algorithms; D019295:Computational Biology; D030562:Databases, Protein; D008239:Lysine / Q000235:genetics / Q000378:metabolism; D011499:Protein Processing, Post-Translational / Q000235:genetics*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D060388:Support Vector Machine
D011506:Proteins; D008239:Lysine