Predicting and analyzing protein interaction networks

预测和分析蛋白质相互作用网络

• 批准号: 7942219
• 负责人: MONA SINGH
• 金额: $ 20万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7942219
• 关键词: Algorithms; Amino Acid Sequence; Amino Acids; Animal Model; Arts; Binding; Biochemical; Biochemical Pathway; Bioinformatics; Biological Process; Cells; Characteristics; Computer Analysis; Computer software; Consensus; DNA; DNA-Protein Interaction; Data; Disease; Drug Delivery Systems; Evolution; Future; Genome; Genomics; Goals; Hand; Health; Human; Internet; Maps; Mediating; Methods; Mining; Modeling; Organism; Pathway Analysis; Pathway interactions; Pattern; Peptide Sequence Determination; Property; Protein Tyrosine Kinase; Proteins; Research; Research Personnel; SH3 Domains; Saccharomyces cerevisiae; Set protein; Signal Transduction; Specific qualifier value; Specificity; Src homology 2 domain-containing, transforming protein 1; Structure; System; Testing; Time; Two-Hybrid System Techniques; Work; Yeasts; Zinc Fingers; genome sequencing; high throughput technology; human disease; improved; novel; programs; protein function; tool

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.

大规模蛋白质相互作用网络已通过实验确定了几个 这些网络的生物和计算分析提供了新的机会来揭示 蛋白质功能和途径。同时，尽管有所改善 高通量技术，在不久的将来仍然不可行地将它们应用于所有测序 基因组。因此，对于绝大多数测序基因组，只有一小部分已知蛋白 相互作用已被实验确定，新颖 计算方法提供了一种有前途的，替代的手段，用于建造大型，高的 置信互动图。这项研究的广泛长期目标是建立 通过开发算法来理解蛋白质相互作用的综合研究计划 对于分析和预测蛋白质相互作用图的互补问题。我们的具体 目的是：（1）开发利用全基因组蛋白质相互作用拓扑的算法 地图和蛋白质功能之间的关系，以做出有关 蛋白质的生物过程。 （2）构建用于询问蛋白质相互作用网络的系统 使用“模板”指定相互作用或途径的常见模式，以帮助发现 新颖的实例。 （3）开发一种利用的一般结构生物信息学方法 特定蛋白质相互作用界面的特性，并采用这种方法以帮助 预测基因组量表的Cys2HIS2锌指蛋白-DNA相互作用。总的来说，我们 希望所提出的工具能够大大推动计算中最新的进步 在其细胞相互作用，途径和 网络。所有软件和预测将通过互联网公开提供。