Vagiona Aimilia-Christina, Notopoulou Sofia, Zdráhal Zbyněk, Gonçalves-Kulik Mariane, Petrakis Spyros, Andrade-Navarro Miguel A
Faculty of Biology, Insitute of Organismic and Molecular Evolution, Johannes Gutenberg University, Biozentrum I, Mainz, Germany.
Institute of Applied Biosciences/Centre for Research and Technology Hellas, Thessaloniki, Greece.
PLoS One. 2025 Mar 3;20(3):e0319084. doi: 10.1371/journal.pone.0319084. eCollection 2025.
Protein-protein interactions (PPIs) form a complex network called "interactome" that regulates many functions in the cell. In recent years, there is an increasing accumulation of evidence supporting the existence of a hyperbolic geometry underlying the network representation of complex systems such as the interactome. In particular, it has been shown that the embedding of the human Protein-Interaction Network (hPIN) in hyperbolic space (H2) captures biologically relevant information. Here we explore whether this mapping contains information that would allow us to predict the function of PPIs, more specifically interactions related to post-translational modification (PTM). We used a random forest algorithm to predict PTM-related directed PPIs, concretely, protein phosphorylation and dephosphorylation, based on hyperbolic properties and centrality measures of the hPIN mapped in H2. To evaluate the efficacy of our algorithm, we predicted PTM-related PPIs of ataxin-1, a protein which is responsible for Spinocerebellar Ataxia type 1 (SCA1). Proteomics analysis in a cellular model revealed that several of the predicted PTM-PPIs were indeed dysregulated in a SCA1-related disease network. A compact cluster composed of ataxin-1, its dysregulated PTM-PPIs and their common upstream regulators may represent critical interactions for disease pathology. Thus, our algorithm may infer phosphorylation activity on proteins through directed PPIs.
蛋白质-蛋白质相互作用（PPI）形成一个名为“相互作用组”的复杂网络，该网络调节细胞中的许多功能。近年来，越来越多的证据支持在诸如相互作用组等复杂系统的网络表示背后存在双曲几何。特别是，已经表明人类蛋白质相互作用网络（hPIN）在双曲空间（H2）中的嵌入捕获了生物学相关信息。在这里，我们探讨这种映射是否包含能够让我们预测PPI功能的信息，更具体地说是与翻译后修饰（PTM）相关的相互作用。我们使用随机森林算法，基于映射到H2中的hPIN的双曲性质和中心性度量，来预测与PTM相关的有向PPI，具体而言是蛋白质磷酸化和去磷酸化。为了评估我们算法的有效性，我们预测了ataxin-1的与PTM相关的PPI，ataxin-1是一种导致1型脊髓小脑共济失调（SCA1）的蛋白质。在细胞模型中的蛋白质组学分析表明，在与SCA1相关的疾病网络中，一些预测的PTM-PPI确实失调。由ataxin-1、其失调的PTM-PPI及其共同的上游调节因子组成的紧密簇可能代表疾病病理学中的关键相互作用。因此，我们的算法可以通过有向PPI推断蛋白质上的磷酸化活性。
