BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences.
BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences.
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DOI：
10.1093/nar/gkl298
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发表时间：
2006-07-01
影响因子：
14.9
通讯作者：
Brown SJ
中科院分区：
文献类型：
--
作者：
Wang L;Brown SJ
BindN（）以氨基酸序列作为输入，并使用支持向量机（SVM）预测潜在的DNA或RNA结合残基。从蛋白质数据库（PDB）中选择具有已知DNA或RNA结合残基的蛋白质数据集，并使用编码有三个序列特征的数据实例构建SVM模型，所述三个序列特征包括侧链pKa值、疏水性指数和氨基酸的分子量。结果表明，预测DNA结合残基的灵敏度为69.40%，特异性为70.47%，而预测RNA结合残基的灵敏度为66.28%，特异性为69.84%。与以前的研究相比，SVM模型似乎更准确，更有效的在线预测。BindN为基于一级序列数据理解DNA和RNA结合蛋白的功能提供了有用的工具。
BindN () takes an amino acid sequence as input and predicts potential DNA or RNA-binding residues with support vector machines (SVMs). Protein datasets with known DNA or RNA-binding residues were selected from the Protein Data Bank (PDB), and SVM models were constructed using data instances encoded with three sequence features, including the side chain pKa value, hydrophobicity index and molecular mass of an amino acid. The results suggest that DNA-binding residues can be predicted at 69.40% sensitivity and 70.47% specificity, while prediction of RNA-binding residues achieves 66.28% sensitivity and 69.84% specificity. When compared with previous studies, the SVM models appear to be more accurate and more efficient for online predictions. BindN provides a useful tool for understanding the function of DNA and RNA-binding proteins based on primary sequence data.
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影响因子：
14.9
作者：
Jones, S;Shanahan, HP;Thornton, JM
通讯作者：
Thornton, JM
影响因子：
14.9
作者：
Jones, S;Daley, DTA;Thornton, JM
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Thornton, JM
影响因子：
8
作者：
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通讯作者：
Bradley, AP
影响因子：
5.6
作者：
Draper, DE
通讯作者：
Draper, DE
影响因子：
3
作者：
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通讯作者：
Sarai A