Explicit representation of protein activity states significantly improves causal discovery of protein phosphorylation networks
Explicit representation of protein activity states significantly improves causal discovery of protein phosphorylation networks
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DOI：
10.1186/s12859-020-03676-2
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发表时间：
2020-09-17
影响因子：
3
通讯作者：
Lu, Xinghua
中科院分区：
文献类型：
--
作者：
Liu, Jinling;Ma, Xiaojun;Lu, Xinghua
背景蛋白质磷酸化网络在细胞信号转导中起着重要作用。在这些网络中，蛋白激酶的磷酸化通常导致其激活，这反过来又会使其下游靶蛋白磷酸化。磷酸化网络本质上是一个因果网络，可以通过因果推理算法来学习。先前的努力已经将这样的算法应用于测量蛋白质磷酸化水平的数据，假设磷酸化水平代表蛋白质活性状态。然而，激酶的磷酸化状态并不总是反映其活性状态，因为抑制剂或突变等干预措施可以直接影响其活性状态，而不会改变其磷酸化状态。因此，当细胞系统受到广泛的扰动时，蛋白质磷酸化状态之间的统计关系可能会被破坏，从而难以重建真正的蛋白质磷酸化网络。在这里，我们描述了一种新的框架来解决这个challenge.ResultsWe已经开发了一个因果发现框架，明确表示每个蛋白激酶的活性状态作为一个不可测量的变量，并开发了一种新的算法称为“InferA”来推断蛋白质的活性状态，这使我们能够将蛋白质磷酸化水平，药理干预和先验知识。我们将我们的框架应用于模拟数据集和真实世界的数据集。模拟实验表明，蛋白激酶活性状态的显式表示允许人们有效地表示干预措施的影响，从而使我们的框架能够准确地恢复地面实况因果网络。从现实世界的数据集的结果表明，蛋白质的活性状态的显式表示允许一个有效的和数据驱动的先验知识的整合InferA，这进一步导致恢复的磷酸化网络，这是更符合实验结果.ConclusionsExplicit表示的蛋白质的活性状态，我们的新框架显着提高了因果发现的蛋白质磷酸化网络。
BackgroundProtein phosphorylation networks play an important role in cell signaling. In these networks, phosphorylation of a protein kinase usually leads to its activation, which in turn will phosphorylate its downstream target proteins. A phosphorylation network is essentially a causal network, which can be learned by causal inference algorithms. Prior efforts have applied such algorithms to data measuring protein phosphorylation levels, assuming that the phosphorylation levels represent protein activity states. However, the phosphorylation status of a kinase does not always reflect its activity state, because interventions such as inhibitors or mutations can directly affect its activity state without changing its phosphorylation status. Thus, when cellular systems are subjected to extensive perturbations, the statistical relationships between phosphorylation states of proteins may be disrupted, making it difficult to reconstruct the true protein phosphorylation network. Here, we describe a novel framework to address this challenge.ResultsWe have developed a causal discovery framework that explicitly represents the activity state of each protein kinase as an unmeasured variable and developed a novel algorithm called "InferA" to infer the protein activity states, which allows us to incorporate the protein phosphorylation level, pharmacological interventions and prior knowledge. We applied our framework to simulated datasets and to a real-world dataset. The simulation experiments demonstrated that explicit representation of activity states of protein kinases allows one to effectively represent the impact of interventions and thus enabled our framework to accurately recover the ground-truth causal network. Results from the real-world dataset showed that the explicit representation of protein activity states allowed an effective and data-driven integration of the prior knowledge by InferA, which further leads to the recovery of a phosphorylation network that is more consistent with experiment results.ConclusionsExplicit representation of the protein activity states by our novel framework significantly enhances causal discovery of protein phosphorylation networks.