Computational Genomics of Signal Transduction

信号转导的计算基因组学

• 批准号: 6946930
• 负责人: Igor B. Jouline
• 金额: $ 18.74万
• 依托单位: UNIVERSITY OF TENNESSEE KNOXVILLE
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-06 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6946930
• 关键词: bioinformatics; biological signal transduction; chemotaxis; computational biology; functional /structural genomics; high throughput technology; microorganism; molecular biology information system; prokaryote; protein protein interaction; protein structure function

DESCRIPTION (provided by applicant): The goal of this proposal is to translate genomic sequence data into high-quality biological knowledge on microbial signal transduction. Signal transduction pathways control important cellular activities ranging from virulence and antibiotic resistance in bacterial pathogens to intracellular communication and coordination of complex cellular functions in humans. Signal transduction is one of the most problematic areas for current genome annotation protocols because of the high sequence variability of input and output domains and mosaic architecture of signal transduction proteins. We will achieve the goal of this proposal via high-throughput genome processing, sophisticated computational protein sequence analysis, and collaborations with leading experimental scientists. The central questions in the biology of signal transduction are: (1) What proteins comprise signal transduction pathways and (2) How the proteins transduce signals. To address these questions, we will first develop a high-throughput computational approach to improve function prediction for signal transduction proteins in microbial genomes combined with experimental validation of selected targets by our collaborators (SPECIFIC AIM 1). Second, we will develop an innovative computational approach to predict contact sites in interacting proteins within the best-studied signal transduction pathway (SPECIFIC AIM 2). These predictions will also be validated by our collaborators. A Knowledge Environment developed under SPECIFIC AIM 3 will integrate results obtained under this proposal and provide free access to the data and computational resources. In the long term, this research will allow us to generalize the computational approaches to signal transduction developed in simple microbial systems and extend them to complex eukaryotic systems, including those in humans. This research will also have an immediate impact on understanding the biology of human pathogens and antimicrobial drug design, and contribute to improvement of automated annotation in primary sequence databases.

描述（由申请人提供）： 该提案的目标是将基因组序列数据转化为关于微生物信号转导的高质量生物学知识。信号转导途径控制着重要的细胞活动，从细菌病原体的毒力和抗生素抗性到人类复杂细胞功能的细胞内通讯和协调。由于信号转导蛋白的输入和输出结构域的高度序列变异性和嵌合结构，信号转导是当前基因组注释方案中最有问题的领域之一。我们将通过高通量基因组处理、复杂的计算蛋白质序列分析以及与领先的实验科学家的合作来实现这一目标。信号转导生物学中的中心问题是：（1）哪些蛋白质构成信号转导通路;（2）蛋白质如何传递信号。为了解决这些问题，我们将首先开发一种高通量计算方法，以改善微生物基因组中信号转导蛋白的功能预测，并结合我们合作者对选定靶标的实验验证（SPECIFIC AIM 1）。其次，我们将开发一种创新的计算方法来预测最好的研究信号转导途径（特定目的2）内相互作用蛋白质的接触位点。这些预测也将得到我们合作者的验证。在具体目标3下开发的知识环境将整合在该提案下获得的结果，并提供免费访问数据和计算资源的机会。从长远来看，这项研究将使我们能够将计算方法推广到简单微生物系统中开发的信号转导，并将其扩展到复杂的真核系统，包括人类系统。这项研究也将对理解人类病原体的生物学和抗微生物药物设计产生直接影响，并有助于改进一级序列数据库中的自动注释。