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

描述(申请人提供)：基于质谱学(MS)的蛋白质组学已经成为寻找疾病相关生物标记物的关键技术。最先进的仪器可以通过‘鸟枪式’蛋白质组学分析识别单个样本中的数千种蛋白质，即蛋白质混合物被蛋白质分解成多肽，通过一个或多个色层步骤分离，并使用串联质谱仪(MS/MS)进行多肽解离分析。这种方法的目标是创造新的技术，准确检测复杂样本中的蛋白质。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限制了这一目标的实现：将谱与从数据库序列生成的“模型谱”进行比较。目前的算法由于使用简单的模型来预测光谱而存在精度和区分性差的问题，这忽略了典型的MS/MS中峰的相对强度所包含的丰富信息。因此，迫切需要更准确的模型来从多肽序列中预测MS/MS的光谱强度。在这个方案中，我们将开发一个新的、创新的预测多肽裂解MS/MS图谱的动力学模型，并利用该模型来开发具有高分辨能力的MS/MS鉴定算法。然后，由动力学模型模拟的光谱将被用于设计选择性反应监测(SRM)分析，该分析已成为在人类生物标记物研究中测量靶向蛋白质集的关键技术。这将解决广泛采用SRM方法发现生物标记物的瓶颈，目前这一瓶颈受到识别和优化分析的SRM过渡的缓慢进程的阻碍。具体目标如下：(1)建立优化的气相多肽裂解动力学模型，该模型可预测任意多肽序列的MS/MS谱。模型参数将使用Levenberg-MarQuardt算法进行拟合，这是一种稳健的非线性最小二乘方法。(2)将该模型推广到预测磷酸肽的MS/MS裂解。在这一目标中开发的方法可以扩展到其他与疾病相关的翻译后修饰，这些修饰深刻地改变了肽的碎片并干扰了MS/MS的鉴定。(3)开发了一条成功实现光谱-光谱匹配算法的途径，这是一种全新的大规模蛋白质鉴定方法，利用我们的原型动力学模型模拟预测的谱库，直接搜索MS/MS。我们使用预测的光谱来绕过对序列数据库的需要，以及完全从光谱到序列的策略。(4)发展了一种用于复杂混合物中蛋白质高重复性定量检测的选择性反应监测(SRM)方法的从头预测算法。

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.