RBind: computational network method to predict RNA binding sites

被引:40
|
作者
Wang, Kaili [1 ,2 ]
Jian, Yiren [3 ]
Wang, Huiwen [1 ,2 ]
Zeng, Chen [1 ,2 ,3 ]
Zhao, Yunjie [1 ,2 ]
机构
[1] Cent China Normal Univ, Inst Biophys, Wuhan 430079, Hubei, Peoples R China
[2] Cent China Normal Univ, Dept Phys, Wuhan 430079, Hubei, Peoples R China
[3] George Washington Univ, Dept Phys, Washington, DC 20052 USA
基金
中国国家自然科学基金;
关键词
DIRECT-COUPLING ANALYSIS; NONCODING RNAS; PROTEIN STRUCTURES; TERTIARY STRUCTURE; FUNCTIONAL SITES; COEVOLUTION; SERVER; DNA; MOLECULES; SEQUENCES;
D O I
10.1093/bioinformatics/bty345
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: Non-coding RNA molecules play essential roles by interacting with other molecules to perform various biological functions. However, it is difficult to determine RNA structures due to their flexibility. At present, the number of experimentally solved RNA-ligand and RNA-protein structures is still insufficient. Therefore, binding sites prediction of non-coding RNA is required to understand their functions. Results: Current RNA binding site prediction algorithms produce many false positive nucleotides that are distance away from the binding sites. Here, we present a network approach, RBind, to predict the RNA binding sites. We benchmarked RBind in RNA-ligand and RNA-protein datasets. The average accuracy of 0.82 in RNA-ligand and 0.63 in RNA-protein testing showed that this network strategy has a reliable accuracy for binding sites prediction.
引用
收藏
页码:3131 / 3136
页数:6
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