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

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“散弹枪”蛋白质组学分析在单个样品中识别数千种蛋白质，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，并通过肽解离使用串联质谱（MS/MS）进行分析。这种方法的目标是为复杂样品中蛋白质的精确检测创造新技术。目前，通过序列数据库搜索从MS/MS谱推断肽序列的主要问题是：将谱与数据库序列生成的“模型谱”进行比较，限制了这一目标的实现。由于使用了简单的模型来预测光谱，而忽略了典型MS/MS中峰的相对强度所包含的丰富信息，因此目前的算法的准确性和识别性较差。因此，迫切需要更准确的模型来预测肽序列的MS/MS谱强度。在本课题中，我们将建立一个新的、创新的肽类碎片化MS/MS光谱预测动力学模型，并利用该模型开发具有高判别能力的MS/MS识别算法。然后，动力学模型模拟的光谱将用于设计选择的反应监测（SRM）分析，这已成为人类生物标志物研究中测量目标蛋白质集的重要技术。这将解决SRM方法广泛应用于生物标志物发现的瓶颈，目前SRM方法被鉴定和优化检测SRM转换的缓慢过程所阻碍。具体目标如下：(1)建立优化的气相肽断裂动力学模型，预测任意肽序列的质谱/质谱。模型参数将使用Levenberg- Marquardt算法进行拟合，这是一种鲁棒的非线性最小二乘法。(2)将模型扩展到预测磷脂肽的MS/MS断裂。在这个目标中开发的方法可以扩展到其他疾病相关的翻译后修饰，这些修饰会深刻地改变肽片段并干扰MS/MS鉴定。(3)开发成功实现谱对谱匹配算法的途径，这是一种全新的大规模蛋白质鉴定方法，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。我们使用预测光谱来绕过对序列数据库和光谱到序列策略的需要。(4)开发一种算法，用于对复杂混合物中的蛋白质进行高度多路定量测量的选择反应监测（SRM）分析的从头预测。

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.