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Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence

机译:通过蛋白质背景和序列的整合模型预测激酶特异性磷酸化位点

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摘要

The identification of kinase substrates and the specific phosphorylation sites they regulate is an important factor in understanding protein function regulation and signalling pathways. Computational prediction of kinase targets – assigning kinases to putative substrates, and selecting from protein sequence the sites that kinases can phosphorylate – requires the consideration of both the cellular context that kinases operate in, as well as their binding affinity. This consideration enables investigation of how phosphorylation influences a range of biological processes. We report here a novel probabilistic model for the classification of kinase-specific phosphorylation sites from sequence across three model organisms: human, mouse and yeast. The model incorporates position-specific amino acid frequencies, and counts of co-occurring amino acids from kinase binding sites in a kinase- and family-specific manner. We show how this model can be seamlessly integrated with protein interactions and cell-cycle abundance profiles. When evaluating the prediction accuracy of our method, PhosphoPICK, on an independent hold-out set of kinase-specific phosphorylation sites, we found it achieved an average specificity of 97% while correctly predicting 32% of true positives. We also compared PhosphoPICK’s ability, through cross-validation, to predict kinase-specific phosphorylation sites with alternative methods, and found that at high levels of specificity PhosphoPICK outperforms alternative methods for most comparisons made. We investigated the relationship between experimentally confirmed phosphorylation sites and predicted nuclear localisation signals by predicting the most likely kinases to be regulating the phosphorylated residues immediately upstream or downstream from the localisation signal. We show that kinases PKA, Akt1 and AurB have an over-representation of predicted binding sites at particular positions downstream from predicted nuclear localisation signals, demonstrating an important role for these kinases in regulating the nuclear import of proteins.
机译:激酶底物及其调节的特定磷酸化位点的鉴定是理解蛋白质功能调节和信号通路的重要因素。激酶靶标的计算预测(将激酶分配给推定的底物,并从蛋白质序列中选择激酶可以磷酸化的位点),需要考虑激酶在其中起作用的细胞环境及其结合亲和力。该考虑因素使得能够研究磷酸化如何影响一系列生物过程。我们在这里报告了一种新的概率模型,用于从三种模式生物(人类,小鼠和酵母菌)中的序列对激酶特异性磷酸化位点进行分类。该模型包含特定位置的氨基酸频率,以及以激酶和家族特异性方式来自激酶结合位点的共现氨基酸计数。我们展示了如何将此模型与蛋白质相互作用和细胞周期丰度概况无缝整合。在评估我们的方法PhosphoPICK的预测准确性时,在一组独立的激酶特异性磷酸化位点上,我们发现它的平均特异性为97%,而正确地预测了32%的真实阳性。我们还通过交叉验证比较了PhosphoPICK通过其他方法预测激酶特异性磷酸化位点的能力,并发现在高特异性的情况下,PhosphoPICK在大多数比较中均优于其他方法。我们通过预测最可能的激酶来调节定位信号上游或下游的磷酸化残基,研究了实验证实的磷酸化位点与预测的核定位信号之间的关系。我们表明,激酶PKA,Akt1和AurB在预测的核定位信号下游的特定位置具有预测的结合位点的过度表达,证明了这些激酶在调节蛋白质的核输入中的重要作用。

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