COMPUTATIONAL TOOLS FOR CANCER PROTEOMICS

描述(由申请人提供)： 这项应用的目标是开发新的计算方法来分析蛋白质表达和磷酸化变化，以响应信号通路和疾病状态，直接支持在三个合作者的实验室进行的黑色素瘤和前列腺癌研究。基于多肽气相裂解的多维LC/MSMS蛋白质组学方法，如泥坑，已被证明在鉴定复杂样品中的蛋白质方面是有效的。然而，在复杂混合物中蛋白质的采样深度、将肽序列分配给MSMS谱的准确性、区分蛋白质异构体的模糊性、蛋白质丰度的量化以及翻译后修饰的表征(如磷酸化)方面，存在严重的限制。此外，还需要一些方法来处理复杂混合物中出现的问题，例如峰的质量和洗脱重叠、多维分离过程中多个组分的多肽洗脱以及基于多变量测量的多肽/蛋白质的聚集。拟议的实验将开发新的计算工具，以创建一个集成的软件系统，以实现这些目标。其具体目标是(1)开发用于从多维LC分离的样品中量化蛋白质丰度变化的计算工具，(2)通过改进用于理论MS/MS光谱预测的算法来提高肽和蛋白质鉴定的准确性，(3)开发统计和计算方法来改进复杂样品中的磷肽分析，以及(4)开发图像识别神经网络策略，用于在多个样品之间的多维数据集中聚类肽和磷酸肽特征。这些目标的完成将解决鸟枪式蛋白质组学中尚未解决的突出障碍，并提供强大的计算工具，以实现准确和灵敏的蛋白质图谱、差异磷酸化的评估、来自多个平台和样本的多变量数据集的集成，以及用于快速描述蛋白质组学数据集中的疾病鉴别器的新算法。随着这些工具的开发，它们将被应用于三个项目，包括用于基础和临床癌症研究的蛋白质组学，用于发现癌症生物标记物的癌细胞、组织和体液的分子变化。所有三个项目的数据收集将使用我们生物分子质谱学核心设施中的LTQ-Orbitrap和4000 QTrap质谱仪进行，调查人员将在那里访问正在开发的数据简化软件。这将提供调查人员对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增加针对真实世界样本需求的进一步分析能力来做出回应。

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.