School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
School of Software.
Bioinformatics. 2017 Apr 15;33(8):1197-1204. doi: 10.1093/bioinformatics/btw792.
Identifying the kinase-substrate relationships is vital to understanding the phosphorylation events and various biological processes, especially signal transductions. Although large amount of phosphorylation sites have been detected, unfortunately, it is rarely known which kinases activate those sites. Despite distinct computational approaches have been proposed to predict the kinase-substrate interactions, the prediction accuracy still needs to be improved.
In this paper, we propose a novel probabilistic model named as PhosD to predict kinase-substrate relationships based on protein domains with the assumption that kinase-substrate interactions are accomplished with kinase-domain interactions. By further taking into account protein-protein interactions, our PhosD outperforms other popular approaches on several benchmark datasets with higher precision. In addition, some of our predicted kinase-substrate relationships are validated by signaling pathways, indicating the predictive power of our approach. Furthermore, we notice that given a kinase, the more substrates are known for the kinase the more accurate its predicted substrates will be, and the domains involved in kinase-substrate interactions are found to be more conserved across proteins phosphorylated by multiple kinases. These findings can help develop more efficient computational approaches in the future.
The data and results are available at http://comp-sysbio.org/phosd.
Supplementary data are available at Bioinformatics online.
确定激酶-底物关系对于理解磷酸化事件和各种生物过程（尤其是信号转导）至关重要。尽管已经检测到大量磷酸化位点，但不幸的是，很少有人知道哪些激酶会激活这些位点。尽管已经提出了多种不同的计算方法来预测激酶-底物相互作用，但预测准确性仍有待提高。
在本文中，我们提出了一种名为 PhosD 的新概率模型，该模型基于蛋白质结构域来预测激酶-底物关系，假设激酶-底物相互作用是通过激酶结构域相互作用完成的。通过进一步考虑蛋白质-蛋白质相互作用，我们的 PhosD 在几个基准数据集上的精度均优于其他流行方法。此外，我们预测的一些激酶-底物关系通过信号通路得到了验证，表明了我们方法的预测能力。此外，我们注意到，对于给定的激酶，已知的底物越多，预测的底物就越准确，并且参与激酶-底物相互作用的结构域在多个激酶磷酸化的蛋白质中具有更高的保守性。这些发现有助于未来开发更有效的计算方法。
数据和结果可在 http://comp-sysbio.org/phosd 上获取。
补充数据可在生物信息学在线获取。
