Proteomic Phosphopeptide Chip Technology for Protein Profiling

用于蛋白质分析的蛋白质组磷酸肽芯片技术

• 批准号: 7622959
• 负责人: XIAOLIAN GAO
• 金额: $ 53.67万
• 依托单位: UNIVERSITY OF HOUSTON
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-27 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7622959
• 关键词: Address; Antibodies; Applications Grants; Basic Science; Binding; Binding Proteins; Biochemical Reaction; Bioinformatics; Biological; Cell division; Clinical; Computational Science; Computer Simulation; Computer software; DNA; Data; Data Analyses; Databases; Development; Diagnosis; Disease; Disease regression; Ensure; Epitopes; Experimental Designs; Facility Construction Funding Category; Growth; Information Resources; Link; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Measurement; Measures; Methods; Microfluidic Microchips; Microfluidics; Modeling; Molecular Profiling; Numbers; PTPN11 gene; Peptide Mapping; Peptide Signal Sequences; Peptides; Phase; Phosphopeptides; Phosphorylation Site; Phosphotransferases; Process; Protein Binding; Protein Kinase; Proteins; Proteomics; Publishing; Research Personnel; Resource Informatics; Sampling; Signal Transduction; Software Tools; Staging; Standards of Weights and Measures; System; Technology; Testing; Time; Work; Writing; anticancer research; base; database design; density; design; interest; microchip; novel; programs; protein aminoacid sequence; protein expression; protein function; research study; response; software development; src Homology Region 2 Domain; technology development; tool; web-accessible

DESCRIPTION (provided by applicant): This R21/R33 grant application is in response to NIH/NCI RFA-CA-07-005 for "Advanced Proteomic Platforms and Computational Sciences for the NCI Clinical Proteomic Technologies Initiative". Cancers are malignant growths caused by misregulated and uncontrolled cell division; these abnormal cellular activities are typically accompanied by unusual protein expression profiles1. Existing proteomics technologies cannot meet the demand of reliable, sensitive, accurate determinations of these disease-related molecular profiles; to achieve reproducible quality, high throughput, and affordable proteomic analyses, many challenging technological hurdles must first be addressed. We propose developing a proteomic phosphopeptide (PPEP) microchip technology platform that profiles proteins carrying phosphopeptide binding domains (PPBDs); using profiles generated in these experiments, along with predicative computational modeling based on both experimental data and a comprehensive PPEP and PPBD interaction database, we will demonstrate specific and quantitative measurements related to protein functions for proteins of significant biological importance. The strength of our proposed work lies in the integration of an already established array technology and a highly promising bioinformatics platform. The methods developed will enable many researchers to rapidly and vigorously develop peptide arrays for quantitative measurement of the proteins in the biological systems of their own interest or to use standard domain-optimized peptide arrays to systematically profile biological samples of basic research or clinical importance. Over the long term, the methods we develop will be used to establish domain-recognition systems for other types of domain-carrying proteins measurements. These capabilities of reliable measurement of proteins as a function of disease states are essential for cancer research, diagnosis and treatment.

描述(由申请人提供)： 这项R21/R33资助申请是对NIH/NCI RFA-CA-07-005《NCI临床蛋白质组技术倡议的高级蛋白质组平台和计算科学》的响应。癌症是由失控的细胞分裂引起的恶性生长；这些异常的细胞活动通常伴随着异常的蛋白质表达谱1。现有的蛋白质组学技术不能满足可靠、灵敏、准确地测定这些疾病相关分子图谱的需求；要获得可重复的质量、高通量和负担得起的蛋白质组分析，必须首先解决许多具有挑战性的技术障碍。我们建议开发一个蛋白质组学磷肽(PPEP)微芯片技术平台，它对携带磷酸肽结合结构域(PPBD)的蛋白质进行图谱分析；使用这些实验中产生的图谱，以及基于实验数据和全面的PPEP和PPBD相互作用数据库的预测性计算模型，我们将展示与具有重要生物学意义的蛋白质的蛋白质功能相关的具体和定量测量。我们提议的工作的优势在于将已经建立的阵列技术和非常有前途的生物信息学平台整合在一起。所开发的方法将使许多研究人员能够快速而有力地开发多肽阵列，用于对他们感兴趣的生物系统中的蛋白质进行定量测量，或者使用标准的领域优化的多肽阵列来系统地描述基础研究或临床重要的生物样本。从长远来看，我们开发的方法将被用于建立其他类型的携带结构域的蛋白质测量的结构域识别系统。这些根据疾病状态对蛋白质进行可靠测量的能力对癌症研究、诊断和治疗至关重要。