论文标题
Deepautopin：一种基于自动轨道的深神经网络，用于表征生命之树的蛋白质相互作用的组织多样性
DeepAutoPIN: An automorphism orbits based deep neural network for characterizing the organizational diversity of protein interactomes across the tree of life
论文作者
论文摘要
生命形式的巨大多样性在截然不同的环境环境中蓬勃发展，涉及组成蛋白之间相互作用的复杂相互作用。但是，在整个生命树中表征蛋白质相互作用网络（PIN）演变的组织原理在很大程度上是未知的。在这里，我们研究了属于16个门的4,738个销钉，以发现门特异性建筑特征，并检查网络拓扑中是否施加了一些进化约束。我们通过将出现在2-5尺寸的图形中的自动形态轨道的频率归一化来利用网络节点的位置信息。我们报告说，属于生命的三个领域的网络的轨道使用曲线（OUP）不仅在域级别，而且在门的规模上也有所不同。整合了与蛋白质家族，结构域，亚细胞位置，基因本体论和途径相关的信息，我们的结果表明，不同门类的销钉的接线模式不是随机生成的，而是由对其施加的进化约束所塑造的。引脚的接线模式存在细微但实质性的变化，这些销钉使OUP能够区分不同的超家族。对差异表达的轨道进行了深层神经网络的培训，导致预测准确性为85％。
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%.
