Predicting and analyzing protein interaction networks

预测和分析蛋白质相互作用网络

• 批准号: 8525403
• 负责人: MONA SINGH
• 金额: $ 29.67万
• 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 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8525403
• 关键词: Affect; Binding; Binding Sites; Bioinformatics; Biological Models; Biological Process; C2H2 Zinc Finger; Cell physiology; Computer software; Computing Methodologies; DNA; DNA Binding; DNA-Protein Interaction; Data; Disease; Disease Pathway; Drug Targeting; Evolution; Exhibits; Gene Expression Regulation; Genome; Genomics; Goals; Grant; Homologous Protein; Human; Internet; Knowledge; Laboratories; Maps; Methods; Mutation; Nucleic Acid Regulatory Sequences; Organism; Pathway interactions; Pattern; Play; Process; Promoter Regions; Protein Binding; Proteins; RNA-Binding Proteins; Research; Role; Specificity; System; Techniques; Technology; Variant; Work; Zinc Fingers; base; comparative; computer framework; computerized tools; disorder risk; genetic regulatory protein; genome sequencing; interest; novel; software development; transcription factor

DESCRIPTION (provided by applicant): Molecular interactions are the underlying basis of all processes that are executed in an organism, and their complete mapping would be a great aid in understanding and interpreting both normal and disease functioning. Transcriptional regulatory interactions are of particular interest as they are critical in the proper spatial and temporal regulation of genes. This proposal aims to develop several novel and complementary computational methods for predicting transcription factor interactions and specificities, and for uncovering their conservation and variation across organisms. Taken together, these methods will vastly expand our knowledge of eukaryotic regulatory networks and their underlying principles. We will devise a combined constrained optimization and statistical approach to predict the DNA-binding specificities of multidomain C2H2 zinc finger proteins; these proteins comprise the largest class of transcription factors in eukaryotic genomes. We will also establish a novel comparative sequence framework for determining binding specificity variation amongst homologous transcription factors, as network divergence underlies much of the observed phenotypic and functional diversity between and within organisms; this framework will be applied to explore the extent to which changes in transcription factors can affect regulatory network variation across organisms. Finally, we will develop a cross-genomic framework for predicting genomic binding sites for transcription factors with known specificities, along with analysis techniques for inferring interactions amongst these transcription factors across organisms. The DNA-binding specificities for an increasing number of transcription factors are being determined, and this large-scale data presents new opportunities to map transcription factor binding sites and to uncover transcription factor- transcription factor interactions across organisms; these interactions are an important component of regulatory networks and their variation plays a key role in network divergence. Successful completion of these aims will result in computational methods that will significantly increase the rate with which transcriptional networks are characterized and will reveal fundamental aspects of their functioning and evolution. All developed software will be made publicly available.

描述（由申请人提供）：分子相互作用是生物体中执行的所有过程的根本基础，它们的完整图谱将有助于理解和解释正常和疾病功能。转录调控相互作用特别令人感兴趣，因为它们对于基因的适当空间和时间调控至关重要。该提案旨在开发几种新颖且互补的计算方法，用于预测转录因子相互作用和特异性，并揭示它们在生物体中的保守性和变异。总而言之，这些方法将极大地扩展我们对真核调控网络及其基本原理的了解。我们将设计一种组合的约束优化和统计方法来预测多域 C2H2 锌指蛋白的 DNA 结合特异性；这些蛋白质包含真核基因组中最大的一类转录因子。我们还将建立一个新的比较序列框架，用于确定同源转录因子之间的结合特异性变异，因为网络差异是生物体之间和生物体内观察到的表型和功能多样性的基础；该框架将用于探索转录因子的变化在多大程度上影响生物体之间的调控网络变化。最后，我们将开发一个跨基因组框架，用于预测具有已知特异性的转录因子的基因组结合位点，以及用于推断这些跨生物体转录因子之间相互作用的分析技术。越来越多的转录因子的 DNA 结合特异性正在被确定，这些大规模数据为绘制转录因子结合位点图谱和揭示跨生物体的转录因子-转录因子相互作用提供了新的机会；这些相互作用是监管网络的重要组成部分，其变化在网络分歧中发挥着关键作用。成功完成这些目标将产生计算方法，这些方法将显着提高转录网络的表征速度，并揭示其功能和进化的基本方面。所有开发的软件都将公开。