TSC_ATP: A two-stage classifier for predicting protein-ATP binding sites from protein sequence
TSC_ATP: A two-stage classifier for predicting protein-ATP binding sites from protein sequence
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DOI：
10.1109/cibcb.2015.7300330
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
Bryan J. Andrews;Jing Hu
中科院分区：
文献类型：
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作者：
Bryan J. Andrews;Jing Hu
众所周知，三磷酸腺苷(ATP)与蛋白质结合，在新陈代谢、细胞信号转导和辅因子等方面发挥重要作用。因此，确定蛋白质上的ATP结合位点对于理解这些机制至关重要。在本文中，我们提出了一种计算方法，可以利用序列信息准确地预测蛋白质上的ATP结合位点。该算法采用双层结构进行组织。该方法首先利用每个残基的进化轮廓信息，采用K-近邻(K-NN)方法进行预测。第一层分类器的输出与其他11个特征一起用作第二层分类器的输入特征。最终的方法在两个基准数据集上的ROC曲线下面积分别为0.829和0.860。
It is well known that adenine triphosphate (ATP) binds with proteins to play important roles in metabolism, cell signaling, and as cofactor. Therefore it is crucial to identify ATP-binding sites on proteins to understand these mechanisms. In this paper, we present a computational method that can accurately predict ATP-binding sites on proteins using sequence-derived information. The algorithm is organized in a two-layered structure. The method first makes prediction by a K-Nearest Neighbors (K-NN) method using evolutionary profile information of each residue. The output of the first-layer classifier serves as an input feature together with other 11 features to a second-layer classifier. The final method achieved an AUC (area under the ROC curve) of 0.829 and 0.860 respectively on two benchmark datasets.