A New Model of Peptide Fragmentation for Improved Protein Identification and Targ

用于改进蛋白质鉴定和目标的肽断裂的新模型

• 批准号: 8895275
• 负责人: William Marland Old
• 金额: $ 31.44万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8895275
• 关键词: Address; Adoption; Algorithms; Biological Assay; Biological Markers; Bypass; Chemicals; Complex; Complex Mixtures; Databases; Detection; Discrimination; Disease; Dissociation; Drug resistance; Early Diagnosis; Gases; Goals; Health; Human; Ions; Kinetics; Least-Squares Analysis; Libraries; Machine Learning; Mass Spectrum Analysis; Measurement; Measures; Methods; Modeling; Molecular Target; Monitor; Peptides; Phase; Phosphopeptides; Plasma; Post-Translational Protein Processing; Process; Protein Analysis; Protein Sequence Analysis; Proteins; Proteomics; Reaction; Relative (related person); Route; Sampling; Scanning; Screening for cancer; Sensitivity and Specificity; Set protein; Shotguns; Statistical Models; Techniques; Technology; Work; base; chemotherapy; design; drug efficacy; improved; innovation; instrument; instrumentation; interest; new technology; novel strategies; prevent; protein aminoacid sequence; protein profiling; prototype; tandem mass spectrometry; tool

DESCRIPTION (provided by applicant): Mass spectrometry (MS) based proteomics has emerged as a key technology in the search for disease- associated biomarkers. State-of-the-art instruments can identify thousands of proteins in a single sample by 'shotgun' proteomic analysis, where protein mixtures are proteolyzed into peptides, separated by one or more chromatographic steps, and analyzed by peptide dissociation using tandem mass spectrometry (MS/MS). The goal of this approach is to create new technologies for the accurate detection of proteins within complex samples. Achieving this target is currently limited by the major problem of inferring the peptide sequence from MS/MS spectra by sequence database searching: spectra are compared to "model spectra" generated from database sequences. Current algorithms suffer from poor accuracy and discrimination due to the use of simple models for predicting spectra, which ignores the rich information contained in the relative intensities of peaks in a typical MS/MS. Consequently, there is a vital need for more accurate models to predict MS/MS spectrum intensities from peptide sequences. In this proposal, we will develop a new and innovative kinetic model for predicting peptide fragmentation MS/MS spectra, and use the model to develop MS/MS identification algorithms with high discrimatory power. Spectra simulated by the kinetic model will then be used to design selected reaction monitoring (SRM) assays, which have become a critically important technique for measuring targeted sets of proteins in human biomarker studies. This will solve a bottleneck for widespread adoption of SRM methods for biomarker discovery, which is currently hindered by the slow process of identifying and optimizing SRM transitions for the assays. The following specific aims are (1) Develop an optimized kinetic model of gas-phase peptide fragmentation which predicts MS/MS spectra for any peptide sequence. Model parameters will be fit using the Levenberg- Marquardt algorithm, a robust method for non-linear least squares. (2) Extend the model to predict MS/MS fragmentation of phosphopeptides. The approaches developed in this aim can be extended to other disease- relevant post-translational modifications which profoundly alter peptide fragmentation and interfere with MS/MS identification. (3) Develop a route to successful implementation of spectrum-to-spectrum matching algorithms, an entirely new approach for large scale identification of proteins, in which MS/MS are searched directly against libraries of predicted spectra, simulated using our prototype kinetic model. We use predicted spectra to bypass the need for sequence databases, and spectrum-to-sequence strategies altogether. (4) Develop an algorithm for de novo prediction of selected reaction monitoring (SRM) assays for highly multiplexed quantitative measurement of proteins in complex mixtures.

描述（由申请人提供）：基于质谱（MS）的蛋白质组学已成为寻找疾病相关生物标志物的关键技术。最先进的仪器可以通过“鸟枪法”蛋白质组学分析鉴定单个样品中的数千种蛋白质，其中蛋白质混合物被蛋白水解成肽，通过一个或多个色谱步骤分离，并使用串联质谱法（MS/MS）通过肽解离进行分析。这种方法的目标是创造新技术，用于精确检测复杂样品中的蛋白质。实现这一目标目前受到通过序列数据库搜索从MS/MS光谱推断肽序列的主要问题的限制：光谱与从数据库序列生成的“模型光谱”进行比较。目前的算法遭受差的准确性和歧视，由于使用简单的模型来预测光谱，这忽略了丰富的信息包含在一个典型的MS/MS中的峰的相对强度。因此，有一个更准确的模型来预测MS/MS光谱强度肽序列的迫切需要。在这个提议中，我们将开发一个新的和创新的动力学模型预测肽裂解MS/MS光谱，并使用该模型开发MS/MS识别算法具有高的区分能力。然后，将使用动力学模型模拟的光谱来设计选择反应监测（SRM）测定，其已成为用于测量人类生物标志物研究中的靶向蛋白质组的至关重要的技术。这将解决广泛采用SRM方法进行生物标志物发现的瓶颈，目前这受到鉴定和优化测定的SRM转换的缓慢过程的阻碍。具体目标如下：（1）建立一个优化的气相肽裂解动力学模型，预测任何肽序列的MS/MS谱。将使用Levenberg-马夸特算法（一种稳健的非线性最小二乘方法）拟合模型参数。(2)扩展该模型以预测磷酸肽的MS/MS裂解。为此目的开发的方法可以扩展到其他疾病相关的翻译后修饰，这些修饰深刻地改变肽片段化并干扰MS/MS鉴定。(3)开发一种成功实施光谱-光谱匹配算法的途径，这是一种用于大规模蛋白质鉴定的全新方法，其中MS/MS直接针对预测光谱库进行搜索，使用我们的原型动力学模型进行模拟。我们使用预测的光谱来绕过对序列数据库的需要，以及光谱到序列的策略。(4)开发一种算法，用于从头预测选择反应监测（SRM）测定，用于复杂混合物中蛋白质的高度多重定量测量。