Mass spectrometry enables high-throughput screening of phospho-proteins 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.

中文翻译：

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使用功能网络可靠地推断激酶活性

质谱分析能够在广泛的生物学环境中对磷蛋白进行高通量筛选。当通过计算算法进行补充时，磷酸化蛋白质组学数据可以推断出激酶的活性，从而有助于在包括癌症，阿尔茨海默氏病和帕金森氏病在内的各种疾病中鉴定失调的激酶。为了增强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，以捕获信号中的协调变化。通过计算实验，我们显示了激酶功能区位点的磷酸化显着预测了其活性。将这些知识纳入RoKAI可以不断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定量分析更加可靠。这使得能够鉴定出未被充分研究的激酶，并有可能导致针对多种疾病的靶向治疗的新型激酶抑制剂的开发。RoKAI可作为基于Web的工具在http://rokai.io上获得。