Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry
Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry
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DOI：
10.1021/pr0341033
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发表时间：
2004-05-01
影响因子：
4.4
通讯作者：
Gammeltoft, S
中科院分区：
文献类型：
--
作者：
Hjerrild, M;Stensballe, A;Gammeltoft, S
蛋白质磷酸化在细胞调控中起着关键作用，识别磷酸化位点对于理解它们的功能意义是很重要的。在这里，我们提出了一种人工神经网络算法：NetPhosK(http://www.cbs.dtu.dk/services/NetPhosK/))，它预测蛋白激酶A的磷酸化位点。用258个实验验证的PKA磷酸化位点的正集来训练神经网络。NetPhosK的预测是！使用四种新的PKA底物：Necdin、RFX5、EN-2和WeE 1进行验证。这四种蛋白被PKA体外磷酸化，并通过质谱仪鉴定了13个PKA磷酸化位点。NetPhosK对预测这四种蛋白中的PKA位点具有100%的敏感性和41%的特异性。这些结果证明了使用集成的计算和实验方法来详细研究磷蛋白质组的潜力。
Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were! validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA in vitro and 13 PKA phosphorylation sites were identified by mass spectrometry. NetPhosK was 100% sensitive and 41% specific in predicting PKA sites in the four proteins. These results demonstrate the potential of using integrated computational and experimental methods for detailed investigations of the phosphoproteome.