Schoenrock Andrew, Burnside Daniel, Moteshareie Houman, Pitre Sylvain, Hooshyar Mohsen, Green James R, Golshani Ashkan, Dehne Frank, Wong Alex
School of Computer Science, Carleton University, Ottawa, Canada.
Department of Biology, Carleton University, Ottawa, Canada.
PLoS One. 2017 Mar 1;12(3):e0171920. doi: 10.1371/journal.pone.0171920. eCollection 2017.
Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.
近年来,对蛋白质-蛋白质相互作用网络和基因相互作用网络进化的研究兴趣日益浓厚,但缺乏大规模高质量的比较数据集成为了一个障碍。在此,我们对来自五个亲缘关系密切的酵母物种的计算预测蛋白质-蛋白质相互作用(PPI)网络进行了比较分析。我们使用了蛋白质-蛋白质相互作用预测引擎(PIPE),它利用已知相互作用的数据库进行基于序列的PPI预测,以生成高质量的预测相互作用组。模拟蛋白质组和相应的PPI网络被用来为PPI网络进化的程度和性质提供零假设预期。我们发现了PPI保守性的有力证据,约四分之一的蛋白质组中PPI的变化水平低于预期。此外,我们发现序列分歧对预测的PPI网络变化的预测效果不佳。我们的分析确定了一些功能类别,其PPI变化比预期的少,这表明对PPI存在纯化选择。我们的结果证明了在研究亲缘关系密切的生物体进化时考虑预测的PPI网络的额外益处。