The enormous diversity of life forms thriving in drastically different environmental milieus involves a complex interplay among constituent proteins interacting with each other. However, the organizational principles characterizing the evolution of protein interaction networks (PINs) across the tree of life are largely unknown. Here we study 4,738 PINs belonging to 16 phyla to discover phyla-specific architectural features and examine if there are some evolutionary constraints imposed on the networks' topologies. We utilized positional information of a network's nodes by normalizing the frequencies of automorphism orbits appearing in graphlets of sizes 2-5. We report that orbit usage profiles (OUPs) of networks belonging to the three domains of life are contrastingly different not only at the domain level but also at the scale of phyla. Integrating the information related to protein families, domains, subcellular location, gene ontology, and pathways, our results indicate that wiring patterns of PINs in different phyla are not randomly generated rather they are shaped by evolutionary constraints imposed on them. There exist subtle but substantial variations in the wiring patterns of PINs that enable OUPs to differentiate among different superfamilies. A deep neural network was trained on differentially expressed orbits resulting in a prediction accuracy of 85%.

翻译：在截然不同的环境条件下繁衍生息的生物多样性极其丰富，这涉及构成蛋白质之间相互作用的复杂关联。然而，表征蛋白质相互作用网络（PINs）在生命之树进化过程中的组织原则仍基本未知。本文研究了分属16个门的4738个PINs，以发现门特异性结构特征，并探讨网络拓扑是否受到某些进化约束。我们通过标准化大小为2-5的图元中自同构轨道出现的频率，利用网络节点的位置信息。研究发现，属于三个生命域的网络轨道使用谱（OUPs）不仅在域水平上，而且在门尺度上均存在显著差异。通过整合与蛋白质家族、结构域、亚细胞定位、基因本体和通路相关的信息，我们的结果表明不同门中PINs的连接模式并非随机生成，而是受制于进化约束。PINs连接模式存在微妙但显著的变异，使得OUPs能够区分不同的超家族。基于差异表达轨道训练的深度神经网络实现了85%的预测准确率。