COMPUTATIONAL TOOLS FOR CANCER PROTEOMICS

癌症蛋白质组学计算工具

• 批准号: 7224654
• 负责人: KATHERYN A RESING
• 金额: $ 41.81万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-28 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7224654
• 关键词: ions; neoplasm /cancer; peptides; phosphopeptides; phosphorylation; proteins; proteomics

DESCRIPTION (provided by applicant): The goal of this application is to develop new computational methods to profile protein expression and phosphorylation changes in response to signaling pathways and disease states, directly supporting studies of melanoma and prostate cancer carried out in the laboratories of three collaborators. Shotgun proteomics using multidimensional LC/MSMS approaches that are based on peptide gas phase fragmentation, such as MuDPIT, have proven effective in idenfitying proteins in complex samples. However, there are serious limitations with respect to depth of sampling proteins in complex mixtures, accuracy of assigning peptide sequences to MSMS spectra, ambiguities in distinguishing protein isoforms, quantification of protein abundances, and characterization of posttranslational modifications, such as phosphorylation. In addition, methods are needed to handle problems arising with complex mixtures, such as peaks that overlap in mass and elution, peptides eluting in many fractions during multidimensional separation, and clustering of peptides/proteins based on multivariate measurements. The proposed experiments will develop new computational tools to create an integrated software system which will address these goals. The specific aims are to (1) develop computational tools for quantifying changes in protein abundances from samples fractionated by multidimensional LC, (2) increase the accuracy of peptide and protein identifications by improving algorithms for theoretical MS/MS spectral predictions, (3) develop statistical and computational methods to improve phosphopeptide analyses in complex samples, and (4) develop an Image Recognition Neural Network strategy for clustering peptide and phosphopeptide features within multidimensional datasets between many samples. Completion of these aims will address outstanding unsolved obstacles in shotgun proteomics and provide robust computational tools to achieve accurate and sensitive protein profiling, assessment of differential phosphorylation, integration of multivariate datasets from multiple platforms and samples, and new algorithms for rapid delineation of disease discriminators in proteomics datasets. As these tools are developed, they will be applied to three projects involving proteomics for basic and clinical cancer research, profiling molecular changes in cancer cells, tissues, and fluids for cancer biomarker discovery. Data collection for all three of projects will be carried out using LTQ-Orbitrap and 4000 QTrap mass spectrometry instruments available in our biomolecular mass spectrometry core facility, where investigators will access the software under development for data reduction. This will provide continual feedback from investigators about results and experiences, which will allow the team to respond by troubleshooting software and adding further analytical capabilities for the needs of real-world samples.

描述（由申请人提供）： 该应用的目的是开发新的计算方法来介绍蛋白质表达和磷酸化的变化，以响应信号通路和疾病状态，直接支持对三个合作者实验室进行的黑色素瘤和前列腺癌的研究。使用基于肽气相碎片（例如Mudpit）的多维LC/MSMS方法的shot弹枪蛋白质组学已被证明有效地有效地在复杂样品中的同化蛋白质中有效。然而，关于复杂混合物中采样蛋白的深度，将肽序列分配给MSMS光谱，区分蛋白质同工型的歧义，蛋白质丰度的定量和转换后修饰的表征（例如磷酸化）的准确性存在严重局限。此外，需要方法来处理复杂混合物引起的问题，例如质量和洗脱的峰，多维分离过程中许多级分的肽以及基于多元测量值的肽/蛋白质聚类。拟议的实验将开发新的计算工具来创建一个集成的软件系统，以解决这些目标。 The specific aims are to (1) develop computational tools for quantifying changes in protein abundances from samples fractionated by multidimensional LC, (2) increase the accuracy of peptide and protein identifications by improving algorithms for theoretical MS/MS spectral predictions, (3) develop statistical and computational methods to improve phosphopeptide analyses in complex samples, and (4) develop an Image Recognition Neural Network strategy for在许多样品之间的多维数据集中，将肽和磷酸肽特征聚集。这些目的的完成将解决shot弹枪蛋白质组学中的未解决的障碍，并提供强大的计算工具，以实现准确和敏感的蛋白质分析，评估差异磷酸化，从多个平台和样品中整合多变量数据集的整合，以及新的算法在蛋白质蛋白酶中的快速依据。随着这些工具的开发，它们将应用于涉及基础癌症和临床癌症研究的蛋白质组学的三个项目，对癌细胞，组织和流体的分子变化进行了癌症生物标志物发现的分子变化。所有三个项目的数据收集将使用我们的生物分子质谱核心设施中可用的LTQ-Orbitrap和4000个QTRAP质谱工具进行，研究人员将在其中访问正在开发的软件以减少数据。这将为调查人员提供有关结果和经验的持续反馈，这将使团队能够通过对软件进行故障排除并为现实世界样本的需求增加进一步的分析功能来做出响应。