Ingrell Christian R, Miller Martin L, Jensen Ole N, Blom Nikolaj
University of Southern Denmark, Campusvej 55, DK-5230, Odense M, Denmark.
Bioinformatics. 2007 Apr 1;23(7):895-7. doi: 10.1093/bioinformatics/btm020. Epub 2007 Feb 5.
We here present a neural network-based method for the prediction of protein phosphorylation sites in yeast--an important model organism for basic research. Existing protein phosphorylation site predictors are primarily based on mammalian data and show reduced sensitivity on yeast phosphorylation sites compared to those in humans, suggesting the need for an yeast-specific phosphorylation site predictor. NetPhosYeast achieves a correlation coefficient close to 0.75 with a sensitivity of 0.84 and specificity of 0.90 and outperforms existing predictors in the identification of phosphorylation sites in yeast.
The NetPhosYeast prediction service is available as a public web server at http://www.cbs.dtu.dk/services/NetPhosYeast/.
我们在此提出一种基于神经网络的方法，用于预测酵母中的蛋白质磷酸化位点——基础研究的重要模式生物。现有的蛋白质磷酸化位点预测器主要基于哺乳动物数据，与人类相比，对酵母磷酸化位点的敏感性较低，这表明需要一种酵母特异性的磷酸化位点预测器。NetPhosYeast的相关系数接近0.75，敏感性为0.84，特异性为0.90，在识别酵母中的磷酸化位点方面优于现有预测器。
NetPhosYeast预测服务可作为公共网络服务器在http://www.cbs.dtu.dk/services/NetPhosYeast/上获取。
