PDF(Pubmed)
Abstract：
Biological networks are characterized by diverse interactions and dynamics in time and space. Many regulatory modules operate in parallel and are interconnected with each other. Some pathways are functionally known and annotated accordingly, e.g., endocytosis, migration, or cytoskeletal rearrangement. However, many interactions are not so well characterized. For reconstructing the biological complexity in cellular networks, we combine here existing experimentally confirmed and analyzed interactions with a protein-interaction inference framework using as basis experimentally confirmed interactions from other organisms. Prediction scoring includes sequence similarity, evolutionary conservation of interactions, the coexistence of interactions in the same pathway, orthology as well as structure similarity to rank and compare inferred interactions. We exemplify our inference method by studying host-pathogen interactions during infection of Mus musculus (phagolysosomes in alveolar macrophages) with Aspergillus fumigatus (conidia, airborne, asexual spores). Three of nine predicted critical host-pathogen interactions could even be confirmed by direct experiments. Moreover, we suggest drugs that manipulate the host-pathogen interaction.
摘要：
生物网络的特点是在时间和空间上有不同的相互作用和动力学。许多调节模块并行操作并且彼此互连。一些途径在功能上是已知的，并相应地进行了注释，例如，内吞作用,迁移,或细胞骨架重排。然而,许多相互作用没有很好地表征。为了重建细胞网络中的生物复杂性，我们将现有的实验证实和分析的相互作用与蛋白质相互作用推断框架结合起来，使用实验证实的来自其他生物体的相互作用作为基础。预测评分包括序列相似性，相互作用的进化守恒，同一途径中相互作用的共存，正交学以及结构相似性，以对推断的相互作用进行排序和比较。我们通过研究用烟曲霉（分生孢子，分生孢子，机载，无性孢子）。甚至可以通过直接实验证实九个预测的关键宿主-病原体相互作用中的三个。此外，我们建议使用操纵宿主-病原体相互作用的药物。
