Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX, USA.
Nat Commun. 2021 Feb 19;12(1):1177. doi: 10.1038/s41467-021-21211-6.
Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer's disease and Parkinson's disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io .
质谱分析能够在广泛的生物背景下实现对磷酸化蛋白质的高通量筛选。当与计算算法相结合时，磷酸化蛋白质组学数据可以推断激酶的活性，有助于鉴定各种疾病（包括癌症、阿尔茨海默病和帕金森病）中失调的激酶。为了提高激酶活性推断的可靠性，我们提出了一个基于网络的框架 RoKAI，该框架集成了各种功能信息源，以捕捉信号的协调变化。通过计算实验，我们表明激酶功能邻域中位点的磷酸化显著预测其活性。在 RoKAI 中纳入这方面的知识可以显著提高激酶活性推断方法的准确性，同时使它们对缺失的注释和量化更具鲁棒性。这使得能够鉴定研究较少的激酶，并可能为许多疾病的靶向治疗开发新的激酶抑制剂。RoKAI 可在 http://rokai.io 上作为基于网络的工具使用。
