Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Katholieke Universiteit Leuven, B3000 Leuven, Belgium.
Bioinformatics. 2011 Oct 15;27(20):2859-65. doi: 10.1093/bioinformatics/btr475. Epub 2011 Aug 16.
Phosphorylation by protein kinases is a central theme in biological systems. Aberrant protein kinase activity has been implicated in a variety of human diseases (e.g. cancer). Therefore, modulation of kinase activity represents an attractive therapeutic approach for the treatment of human illnesses. Thus, identification of signature peptides is crucial for protein kinase targeting and can be achieved by using PamChip(®) microarray technology. We propose a flexible semiparametric mixed model for analyzing PamChip(®) data. This approach enables the estimation of the phosphorylation rate (Velocity) as a function of time together with pointwise confidence intervals.
Using a publicly available dataset, we show that our model is capable of adequately fitting the kinase activity profiles and provides velocity estimates over time. Moreover, it allows to test for differences in the velocity of kinase inhibition between responding and non-responding cell lines. This can be done at individual time point as well as for the entire velocity profile.
Supplementary data are available at Bioinformatics online.
蛋白质激酶的磷酸化是生物系统的一个核心主题。异常的蛋白激酶活性与多种人类疾病（如癌症）有关。因此，调节激酶活性是治疗人类疾病的一种有吸引力的治疗方法。因此，鉴定特征肽对于蛋白激酶靶向至关重要，可以通过使用 PamChip（®）微阵列技术来实现。我们提出了一种灵活的半参数混合模型来分析 PamChip（®）数据。这种方法可以估计磷酸化速率（Velocity）作为时间的函数，同时给出逐点置信区间。
使用公开可用的数据集，我们表明我们的模型能够充分拟合激酶活性谱，并提供随时间的速度估计。此外，它还允许测试在响应和非响应细胞系之间激酶抑制的速度差异。这可以在单个时间点以及整个速度曲线进行。
补充数据可在在线生物信息学中获得。
