Predicting and analyzing protein interaction networks

预测和分析蛋白质相互作用网络

• 批准号: 7187344
• 负责人: MONA SINGH
• 金额: $ 25.84万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-18 至 2011-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7187344
• 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; Molecular Models; Organism; Pathway Analysis; Pathway interactions; Pattern; Peptide Sequence Determination; Property; Protein Interaction Mapping; Protein Tyrosine Kinase; Protein-Protein Interaction Map; 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

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.

描述（由申请人提供）：大规模蛋白质相互作用网络已通过实验确定了几种生物，这些网络的计算分析为发现蛋白质功能和途径提供了新的机会。同时，尽管高通量技术有所改进，但在不久的将来，将它们应用于所有测序基因组仍然是不可行的。因此，对于绝大多数测序基因组，仅实验确定了一小部分已知蛋白质相互作用，而新颖的计算方法为构建大型，高置信互动图提供了一种有希望的替代手段。这项研究的广泛长期目标是通过为分析和预测蛋白质相互作用图的互补问题开发算法来建立一项全面的研究计划，以理解蛋白质相互作用。我们的具体目的是：（1）开发算法来利用全基因组蛋白相互作用图和蛋白质功能之间的关系，以对蛋白质的生物过程做出新的预测。 （2）使用“模板”来构建用于询问蛋白质相互作用网络的系统，以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）为利用特定蛋白质相互作用界面的特性开发一种通用的结构生物信息学方法，并应用此方法以帮助预测Cys2HIS2锌指蛋白-DNA在基因组规模上的相互作用。综上所述，我们希望所提出的工具能够在计算方法中显着推进最先进的方法，以表征蛋白质在其细胞相互作用，途径和网络的背景下。所有软件和预测将通过互联网公开提供。