Predicting and analyzing protein interaction networks

描述（由申请人提供）：已经通过实验确定了几种生物体的大规模蛋白质相互作用网络，这些网络的计算分析为揭示蛋白质功能和途径提供了新的机会。与此同时，尽管高通量技术有所改进，但在不久的将来将其应用于所有测序的基因组仍然不可行。因此，对于绝大多数测序的基因组，只有一小部分已知的蛋白质相互作用已被实验确定，新的计算方法提供了一个有前途的，替代的手段，用于建立大的，高置信度的相互作用图。这项研究的广泛，长期的目标是建立一个全面的研究计划，了解蛋白质相互作用，通过开发算法的分析和预测蛋白质相互作用图的互补问题。我们的具体目标是：（1）开发利用全基因组蛋白质相互作用图谱的拓扑结构和蛋白质功能之间关系的算法，以便对蛋白质的生物过程进行新的预测。(2)建立一个系统，用于使用指定相互作用或途径的共同模式的“模板”来询问蛋白质相互作用网络，以帮助发现新的实例。(3)开发一种通用的结构生物信息学方法，利用特定蛋白质相互作用界面的特性，并应用这种方法，以帮助预测Cys 2 HiS 2锌指蛋白-DNA相互作用在基因组规模。总之，我们希望所提出的工具将显着推进最先进的计算方法，用于在其细胞相互作用，通路和网络的背景下表征蛋白质。所有软件和预测将通过互联网公开提供。

DESCRIPTION (provided by applicant): Large-scale protein interaction networks have been determined experimentally for several organisms, and computational analysis of these networks provides new opportunities to uncover protein functions and pathways. At the same time, despite improvements in high-throughput technologies, it is still not feasible in the near future to apply them to all sequenced genomes. Thus, for the vast majority of sequenced genomes, only a small fraction of known protein interactions have been experimentally determined, and novel computational approaches provide a promising, alternative means for building large, high- confidence interaction maps. The broad, long-term goal of this research is to build a comprehensive research program for understanding protein interactions, by developing algorithms for the complementary problems of analyzing and predicting protein interaction maps. Our specific aims are: (1) To develop algorithms that exploit the topology of whole-genome protein interaction maps and the relationships between protein functions, in order to make novel predictions about a protein's biological process. (2) To build a system for interrogating protein interaction networks using "templates" specifying common patterns of interactions or pathways, in order to help uncover novel instances. (3) To develop a general structural bioinformatics approach for leveraging properties of specific protein interaction interfaces, and to apply this approach in order to help predict Cys2HiS2 zinc finger protein-DNA interactions at the genomic scale. Taken together, we hope that the proposed tools will significantly advance the state-of-the-art in computational approaches for characterizing proteins within the context of their cellular interactions, pathways and networks. All software and predictions will be made publicly available via the internet.