Predicting and analyzing protein interaction networks

大规模的蛋白质相互作用网络已经被实验确定为几个 有机体，而对这些网络的计算分析提供了新的机会 蛋白质的功能和途径。与此同时，尽管情况有所改善， 高通量技术，但在不久的将来将它们应用于所有测序仍然不可行 基因组。因此，对于绝大多数已测序的基因组来说，只有一小部分已知蛋白质 相互作用已经通过实验确定了，而且是新颖的 计算方法提供了一种很有前途的替代方法，用于建造大型、高度和 置信度相互作用图。这项研究的广泛、长期目标是建立一个 通过开发算法来了解蛋白质相互作用的全面研究计划 用于分析和预测蛋白质相互作用图的补充问题。我们的特定 目标是：(1)开发利用全基因组蛋白质相互作用的拓扑结构的算法 图谱和蛋白质功能之间的关系，以便对 蛋白质的生物过程。(2)构建蛋白质相互作用网络查询系统 使用“模板”指定交互或路径的常见模式，以帮助发现 新奇的例子。(3)开发一个通用的结构生物信息学方法，以利用 特定蛋白质相互作用界面的性质，并应用这种方法来帮助 在基因组水平上预测Cys2HiS2锌指蛋白与DNA的相互作用。总而言之，我们 我希望所提出的工具将显著提高计算领域的最先进水平 根据蛋白质的细胞相互作用、途径和性质来表征蛋白质的方法 网络。所有软件和预测都将通过互联网公开提供。

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.