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Computational approaches to protein identification and quantification using MS/MS

使用 MS/MS 进行蛋白质鉴定和定量的计算方法

基本信息

批准号：
8902210
负责人：
Predrag Radivojac
金额：
$ 41.13万
依托单位：
TRUSTEES OF INDIANA UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-05-01 至 2017-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8902210
关键词：
Address Algorithmic Software Algorithms Analytical Chemistry Applications Grants Area Bioinformatics Biological Cells Chemicals Collaborations Communities Complex Computational Technique Computer software Computing Methodologies Coupled Custom Data Data Set Development Discipline Funding Gases Goals Indiana Ions Label Learning Liquid Chromatography Machine Learning Mass Spectrum Analysis Measures Methodology Methods Modeling Occupations Peptide Library Peptides Phase Play Population Post-Translational Modification Site Post-Translational Protein Processing Procedures Process Proteins Proteomics Relative (related person)Reproducibility Research Activity Research Personnel Role Sampling Scientist Site Spectrometry Staging Synthesis Chemistry Synthetic Peptide Libraries Techniques Tissues Training Universities Work base crosslink experience improved instrument instrumentation ion mobility model development novel programs protein protein interaction research study response sound tandem mass spectrometry

项目摘要

(Not modified) Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) is a widely used platform for high-throughput identification and quantification of proteins in biological samples. In addition to experimental steps in the pipeline, computational and statistical procedures play important roles in determining the content of the mixture. However, even with the best analytical platforms and modern software, only a small fraction of spectra are typically identified, thus directly impacting the quality of the biological sample analysis. If high- throughput proteomics techniques are to become routinely used in biomedical applications on the population scale, it is critical to address analytical and computational factors that contribute to the inadequate identification coverage and sensitivity. Over the past several years, we and others have spent a significant amount of research activity to understand and model analytical platforms and subsequently improve computational methods for the analyses of complex biological mixtures. While our original grant application has resulted in methods and programs already accepted by the community, there is a need and significant room for further key contributions. We see many of these contributions being related to the analyses of dynamic changes in cells and tissues, and involving changes in protein quantities, protein post-translational modifications (PTMs) and transient protein-protein interactions. Mass spectrometry-based proteomics provides an excellent platform to address each of these challenges. Thus, we plan to continue to develop novel methods for label-free quantification and remain close to our core strengths, but also strongly focus on PTMs and protein-protein interactions as new directions of this renewal application. This application includes a considerably closer collaboration between computational (Dr. Radivojac, Dr. Tang) and experimental (Dr. Arnold, Dr. Clemmer, Dr. Reilly) scientists than did our original application. The investigators bring complementary expertise and experience in a range of disciplines involving protein bioinformatics, algorithms, machine learning, as well as analytical chemistry and instrumentation. Overall, we believe that this proposal will result in significant advances for mass spectrometry-based proteomics.

(Not修改）液相色谱法（LC）与串联质谱法（MS/MS）联用是一种广泛使用的用于检测和分析的平台。生物样品中蛋白质的高通量鉴定和定量。除了实验性在管道的步骤中，计算和统计程序在确定内容方面发挥着重要作用的混合物。然而，即使有最好的分析平台和现代软件，通常识别光谱，从而直接影响生物样品分析的质量。如果偏高- 通量蛋白质组学技术将成为生物医学应用中的常规应用规模，这是至关重要的，以解决分析和计算因素，有助于不充分的识别覆盖面和灵敏度。在过去的几年里，我们和其他人花了大量的研究活动，以了解和模型分析平台，并随后改进复杂分析的计算方法，生物混合物。虽然我们最初的拨款申请已经导致了方法和程序，社会各界的接受程度，我们有需要和很大的空间作出进一步的重要贡献。我们看到许多这些贡献与细胞和组织中的动态变化的分析有关，蛋白质量、蛋白质翻译后修饰（PTM）和瞬时蛋白质-蛋白质交互.基于质谱的蛋白质组学为解决其中的每一个问题提供了一个绝佳的平台挑战因此，我们计划继续开发新的无标记定量方法，并保持密切联系。我们的核心优势，但也强烈关注PTM和蛋白质-蛋白质相互作用作为新的方向，续期申请。该应用程序包括计算（Radivojac博士，Tang博士）和实验（阿诺德博士，克莱默博士，赖利博士）的科学家比我们原来的申请。的研究人员带来了涉及蛋白质的一系列学科的补充专业知识和经验，生物信息学、算法、机器学习以及分析化学和仪器。总的来说，我们相信这一建议将导致基于质谱的蛋白质组学的重大进展。