Proteomic Phosphopeptide Chip Technology for Protein Profiling

描述（由申请人提供）： 该R21/R33拨款申请是对NIH/NCI RFA-CA-07-005“NCI临床蛋白质组学技术倡议的高级蛋白质组学平台和计算科学”的回应。癌症是由失调和不受控制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不寻常的蛋白质表达谱1。现有的蛋白质组学技术不能满足这些疾病相关分子谱的可靠、灵敏、准确测定的需求;为了实现可重复的质量、高通量和负担得起的蛋白质组学分析，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磷酸肽（PPEP）微芯片技术平台，配置文件携带磷酸肽结合结构域（PPBD）的蛋白质，使用这些实验中产生的配置文件，沿着预测计算模型的基础上，实验数据和全面的PPEP和PPBD相互作用数据库，我们将展示特定的和定量的测量相关的蛋白质功能的蛋白质具有重要的生物学意义。我们所提出的工作的优势在于整合了已经建立的阵列技术和非常有前途的生物信息学平台。所开发的方法将使许多研究人员能够快速而有力地开发肽阵列，用于定量测量他们感兴趣的生物系统中的蛋白质，或者使用标准结构域优化的肽阵列来系统地分析基础研究或临床重要性的生物样品。从长远来看，我们开发的方法将用于建立其他类型的结构域携带蛋白质测量的结构域识别系统。这些可靠测量蛋白质作为疾病状态的函数的能力对于癌症研究、诊断和治疗是必不可少的。

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.