Dutkowski Janusz, Tiuryn Jerzy
Institute of Informatics, Warsaw University, Poland.
Bioinformatics. 2007 Jul 1;23(13):i149-58. doi: 10.1093/bioinformatics/btm194.
The increasing availability of large-scale protein-protein interaction (PPI) data has fueled the efforts to elucidate the building blocks and organization of cellular machinery. Previous studies have shown cross-species comparison to be an effective approach in uncovering functional modules in protein networks. This has in turn driven the research for new network alignment methods with a more solid grounding in network evolution models and better scalability, to allow multiple network comparison.
We develop a new framework for protein network alignment, based on reconstruction of an ancestral PPI network. The reconstruction algorithm is built upon a proposed model of protein network evolution, which takes into account phylogenetic history of the proteins and the evolution of their interactions. The application of our methodology to the PPI networks of yeast, worm and fly reveals that the most probable conserved ancestral interactions are often related to known protein complexes. By projecting the conserved ancestral interactions back onto the input networks we are able to identify the corresponding conserved protein modules in the considered species. In contrast to most of the previous methods, our algorithm is able to compare many networks simultaneously. The performed experiments demonstrate the ability of our method to uncover many functional modules with high specificity.
Information for obtaining software and supplementary results are available at http://bioputer.mimuw.edu.pl/papers/cappi.
大规模蛋白质-蛋白质相互作用（PPI）数据的日益可得推动了人们对阐明细胞机制的组成部分和组织方式的研究。先前的研究表明，跨物种比较是揭示蛋白质网络中功能模块的有效方法。这反过来又推动了对新的网络比对方法的研究，这些方法在网络进化模型方面有更坚实的基础且具有更好的可扩展性，以实现多个网络的比较。
我们基于祖先PPI网络的重建开发了一种新的蛋白质网络比对框架。该重建算法基于一种提出的蛋白质网络进化模型构建，该模型考虑了蛋白质的系统发育历史及其相互作用的进化。我们的方法在酵母、线虫和果蝇的PPI网络上的应用表明，最可能保守的祖先相互作用通常与已知的蛋白质复合物相关。通过将保守的祖先相互作用投影回输入网络，我们能够在考虑的物种中识别出相应的保守蛋白质模块。与大多数先前的方法不同，我们的算法能够同时比较多个网络。所进行的实验证明了我们的方法具有以高特异性揭示许多功能模块的能力。
