Protein phosphorylation by kinases plays crucial roles in various biological processes including signal transduction and tumorigenesis, thus a better understanding of protein phosphorylation events in cells is fundamental for studying protein functions and designing drugs to treat diseases caused by the malfunction of phosphorylation. Although a large number of phosphorylation sites in proteins have been identified using high-throughput phosphoproteomic technologies, their specific catalyzing kinases remain largely unknown. Therefore, computational methods are urgently needed to predict the kinases that catalyze the phosphorylation of these sites. We developed KSP, a new algorithm for predicting catalyzing kinases for experimentally identified phosphorylation sites in human proteins. KSP constructs a network based on known protein-protein interactions and kinase-substrate relationships. Based on the network, it computes an affinity score between a phosphorylation site and kinases, and returns the top-ranked kinases of the score as candidate catalyzing kinases. When tested on known kinase-substrate pairs, KSP outperforms existing methods including NetworKIN, iGPS, and PKIS. We developed a novel accurate tool for predicting catalyzing kinases of known phosphorylation sites. It can work as a complementary network approach for sequence-based phosphorylation site predictors.

中文翻译：

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KSP：一种预测蛋白质中磷酸化位点催化激酶的综合方法。

激酶的蛋白磷酸化在包括信号转导和肿瘤发生在内的各种生物学过程中都起着至关重要的作用，因此，更好地了解细胞中的蛋白磷酸化事件对于研究蛋白功能和设计治疗由磷酸化功能障碍引起的疾病的药物至关重要。尽管已使用高通量磷酸蛋白质组学技术鉴定了蛋白质中的大量磷酸化位点，但它们的特异性催化激酶在很大程度上仍然未知。因此，迫切需要计算方法来预测催化这些位点磷酸化的激酶。我们开发了KSP，这是一种新算法，可预测人类蛋白中实验确定的磷酸化位点的催化激酶。KSP基于已知的蛋白质-蛋白质相互作用和激酶-底物关系构建网络。基于网络，它计算磷酸化位点和激酶之间的亲和力得分，并返回得分最高的激酶作为候选催化激酶。在已知的激酶-底物对上进行测试时，KSP的性能优于现有方法，包括NetworKIN，iGPS和PKIS。我们开发了一种新颖的精确工具，用于预测已知磷酸化位点的催化激酶。它可以作为基于序列的磷酸化位点预测子的补充网络方法。KSP的性能优于现有方法，包括NetworKIN，iGPS和PKIS。我们开发了一种新颖的精确工具，用于预测已知磷酸化位点的催化激酶。它可以作为基于序列的磷酸化位点预测子的补充网络方法。KSP的性能优于现有方法，包括NetworKIN，iGPS和PKIS。我们开发了一种新颖的精确工具，用于预测已知磷酸化位点的催化激酶。它可以作为基于序列的磷酸化位点预测子的补充网络方法。